7.3. Metadata-Management with MetaLad

For many years, metadata related functionality was included in the DataLad core package. A modernized approach, however, is now developed in the datalad-metalad extension.

../_images/metadata.svg

MetaLad is a DataLad extension that allows you to

  • associate metadata in any format with a dataset, a subdataset, or a file,

  • extract metadata automatically from primary data or handle manually supplied metadata,

  • transport metadata separately from primary data,

  • dump metadata and, for example, store it in a file, or search through it with a tool of your choice.

The following section illustrates relevant concepts, commands, and workflows.

7.3.1. Primary Data versus Metadata

You might ask upfront: “What is ‘metadata’?” Very simply put: Metadata is data about data. In principle, any kind of data could be metadata. What makes it metadata is the fact that it is associated with some “primary” data, and usually describes the primary data in some way. Consider two simple examples from the physical and the digital world: A library catalog contains metadata about the library’s books, such as their location; and a file system stores the creation time of a file as well as the user ID of its creator. The location, creation time, or creator ID is metadata, while the book in the library or the file on the file system are the primary data the metadata is associated with.

And what does metadata do for you? Generally, metadata provides additional information about primary data. This allows to identify primary data with certain properties. These properties could either be contained within the primary data and (automatically) extracted from it, such as digital photographs captured in a specific time frame at a specific GPS location, or assigned to primary data based on an external policy, such as the directory “Hiking in the alps 2019” on your phone.

Importantly, primary data can have virtually unlimited different metadata associated with it, depending on what is relevant in a given context. Consider a publication in a medical field, and a few examples for metadata about it from the virtually unlimited metadata space:

  1. The full text for the scanned PDF (manually created, or automatically extracted by optical character recognition)

  2. Citation information, such as the geographic origin of citing papers or type of media outlet reporting about it

  3. Context information, e.g. publications based on similar data

  4. Structural data about the underlying medical acquisitions (such as dataset containment, modification date, or hash), which can provide basic structural information even without access to the primary data

  5. Special search indices, e.g. graph-based search indices, medical abbreviations

  6. Anonymized information extracted from medical documents.

  7. Information about the used software, e.g. security assessments, citation.cff

7.3.2. MetaLad’s extractor concept

In the context of MetaLad, each one of those metadata examples above would be called a schema, and a process or tool deriving or generating a given schema would be called an extractor.

Different metadata schemas are useful in different contexts: In the example above, citation metadata might come in handy when evaluating the impact of the scientific finding, whereas the publications full text and special search indices could be used for automated meta-analyses. To allow a variety of metadata use cases, MetaLad can use various metadata schemas simultaneously - if you want to, all schemas from the example above and many more could be created and managed in the same dataset in parallel.

To handle different schemas in parallel, MetaLad represents them based on unique identifiers of the extraction process that generated them. For example, the automatically scanned full text might be identified with an extractor name OCR, and that of the citation data could be called “altmetric”. But while the term “extractor” has a technical feel to it, an “extractor” can also be the manual process of annotating arbitrary information about a file - nothing prevents metadata from medical annotations to be called Sam-tracing-brain-regions-by-hand.

In addition to identifying schemas via extractor names, MetaLad and other DataLad extensions ship with specialized extractor tools to extract metadata of a certain schema. Likewise, anyone can build their own extractor to generate schemas of their choice. But before we take a closer look into that, let’s illustrate the metadata concepts and commands of MetaLad with a toy example.

7.3.3. Adding metadata with meta-add

In the context of DataLad datasets, metadata can either be associated with entire datasets or individual files inside of it. Whether a piece of information is dataset level or file level metadata is dependent on the nature of the metadata and the envisioned use. Metadata that describe dataset level properties could be dataset owner, dataset authors, dataset licenses, or names of contained files[1], whereas metadata that describe file level properties could be file checksums or file-specific information like the time-stamp of a photograph.

Metadata is stored in Git

When MetaLad adds metadata to your datasets, it will store the metadata in Git only. Thus, even a plain Git repository is sufficient to work with datalad-metalad. However, the metadata is stored in an unusual and somewhat hidden place, inside of the Git object store. If you’re interested in the technical details, you can find a Findoutmore a bit further down in this section.

Let’s look at a concrete example. We have a DataLad dataset cozy-screensavers that contains a single PNG-file called zen.png.

$ datalad clone https://github.com/datalad-handbook/cozy-screensavers.git
$ cd cozy-screensavers
[INFO] Remote origin not usable by git-annex; setting annex-ignore
install(ok): /home/me/beyond_basics/meta/cozy-screensavers (dataset)
$ tree
.
└── zen.jpg -> .git/annex/objects/Fx/fM/✂/MD5E-s3351765--e4c0b8d7✂MD5.jpg

0 directories, 1 file
$ datalad get zen.jpg
get(ok): zen.jpg (file) [from web...]

Let’s assume there is metadata stemming from an advanced AI called Picture2Words that is able to describe the content of images - in other words, this AI would be able to extract certain metadata from the file. In this case the AI describes the image as

"A lake with waterlilies in front of snow covered mountains"

We would like to add this description as metadata to the file ./zen.png, and will identify it with a name corresponding to its extractor, "Picture2Words".

In order to include metadata in a dataset, users need to provide a metadata entry to the datalad meta-add (manual) command. This metadata entry has two major requirements: It needs to be supplied in a certain format, in particular, as a JSON object[2], and it needs to include a set of required information in defined fields. Let’s take a look at the JSON object we could generate as a metadata entry for zen.png and identify required fields:

{
  "type": "file",
  "path": "zen.png",
  "dataset_id": "2d540a9d-2ef7-4b5f-8931-7c92f483f0c7",
  "dataset_version": "19f2d98d758116d099d467260a5a71082b2c6a29",
  "extractor_name": "Picture2Words",
  "extractor_version": "0.1",
  "extraction_parameter": {},
  "extraction_time": 1675113291.1464975,
  "agent_name": "Overworked CTO",
  "agent_email": "closetoburnout@randomtechconsultancy.com",
  "extracted_metadata": {
    "description": "A lake with waterlilies in a front of snow covered mountains"
  }
}

When adding file-level metadata to a dataset that contains the file, the metadata JSON object must contain:

  • information about the level the metadata applies to (type, with file instead of dataset as a value),

  • the file the metadata belongs to with a path,

  • the dataset ID (dataset_id) and version (dataset_version),

  • an joint identifier for the metadata extractor and schema extractor_name (i.e., Picture2Words, as well as details about the metadata extractor like its version (extractor_version), its parameterization (extraction_parameter), and the date and time of extraction (extraction_time) in the form of a Unix time stamp[3],

  • information about the agent supplying the metadata (agent_name and agent_email),

  • and finally the metadata itself (extracted_metadata).

While certain extractors can generate metadata entries automatically, or one could write scripts wrapping extracting tools to generate them, we can also create such a JSON object manually, for example in an editor. A valid metadata entry can then be read into meta-add either from the command line or from standard input (stdin). For example, we can save the metadata entry above as metadata-zen.json:

$ cat << EOT > metadata-zen.json
{
  "type": "file",
  "path": "zen.png",
  "dataset_id": "2d540a9d-2ef7-4b5f-8931-7c92f483f0c7",
  "dataset_version": "19f2d98d758116d099d467260a5a71082b2c6a29",
  "extractor_name": "Picture2Words",
  "extractor_version": "0.1",
  "extraction_parameter": {},
  "extraction_time": 1675113291.1464975,
  "agent_name": "Overworked CTO",
  "agent_email": "closetoburnout@randomtechconsultancy.com",
  "extracted_metadata": {
  "description": "A lake with waterlilies in a front of snow covered mountains"
      }
}
EOT

Then, we redirect the content of the file into the datalad meta-add command in the command line. The following call would add the metadata entry to the current dataset, cozy-screensavers:

$ datalad meta-add -d . - < metadata-zen.json
meta_add(ok): /home/me/beyond_basics/meta/cozy-screensavers/zen.png (file) [added file metadata to /home/me/beyond_basics/meta/cozy-screensavers]

meta-add validity checks

When adding metadata for the first time, its not uncommon to run into errors. Its quite easy, for example, to miss a comma or quotation mark when creating a JSON object by hand. But there are also some internal checks that might be surprising. If you want to add the metadata above to your own dataset, you should make sure to adjust the dataset_id to the ID of your own dataset, found via the command datalad configuration get datalad.dataset.id - otherwise you’ll see an error[4], and likewise the dataset_version. And in case you’d supply the extraction_time as “this morning at 8AM” instead of a time stamp, the command will be unhappy as well. In case an error occurs, make sure to read the error message, and turn the commands’ --help for insights about requirements you might have missed.

After the metadata has been added, you can view it via the command datalad meta-dump (manual). The simplest form of this command is meta-dump -r, which will show all metadata that is stored in the dataset in the current directory. To get more specific metadata records, you can give a dataset-file-path-pattern to meta-dump, much like an argument to ls, that identifies dataset ID, version and a file within the dataset. The two parts are separated by :. The following line would just dump all metadata for zen.png.

$ datalad meta-dump -d . .:zen.png
{"type": "file", "path": "zen.png", "dataset_id": "2d540a9d-2ef7-4b5f-8931-7c92f483f0c7", "dataset_version": "19f2d98d✂SHA1", "extraction_time": 1675113291.1464975, "agent_name": "Overworked CTO", "agent_email": "closetoburnout@randomtechconsultancy.com", "extractor_name": "Picture2Words", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"description": "A lake with waterlilies in a front of snow covered mountains"}}

This could also be printed a bit more readable:

$ datalad -f json_pp meta-dump -d . .:zen.png
{
  "action": "meta_dump",
  "backend": "git",
  "metadata": {
    "agent_email": "closetoburnout@randomtechconsultancy.com",
    "agent_name": "Overworked CTO",
    "dataset_id": "2d540a9d-2ef7-4b5f-8931-7c92f483f0c7",
    "dataset_version": "19f2d98d✂SHA1",
    "extracted_metadata": {
      "description": "A lake with waterlilies in a front of snow covered mountains"
    },
    "extraction_parameter": {},
    "extraction_time": 1675113291.1464975,
    "extractor_name": "Picture2Words",
    "extractor_version": "0.1",
    "path": "zen.png",
    "type": "file"
  },
  "metadata_source": ".",
  "path": "/home/me/beyond_basics/meta/cozy-screensavers/zen.png",
  "status": "ok",
  "type": "dataset"
}

More complex metadata-dumps

TODO: add complex Dataset-file-path-pattern examples, e.g., with UUIDs, versions, etc

7.3.3.1. Using existing extractors to add metadata

If writing JSON objects by hand sounds cumbersome, it indeed is. To automate metadata extraction or generation, MetaLad can use extractors to do the job. A few built-in extractors are already shipped with it, for example annex (reporting on information git-annex provides about datasets or files), or studyminimeta (a metadata schema for archived studies). Once an extractor of choice is found, the datalad meta-extract (manual) command can do its job:

$ datalad meta-extract -d . metalad_core | jq
[INFO] Start core metadata extraction from Dataset(/home/me/beyond_basics/meta/cozy-screensavers)
[INFO] Extracted core metadata from /home/me/beyond_basics/meta/cozy-screensavers
[INFO] Finished core metadata extraction from Dataset(/home/me/beyond_basics/meta/cozy-screensavers)
{
  "type": "dataset",
  "dataset_id": "2d540a9d-2ef7-4b5f-8931-7c92f483f0c7",
  "dataset_version": "66aa4e8a✂SHA1",
  "extractor_name": "metalad_core",
  "extractor_version": "1",
  "extraction_parameter": {},
  "extraction_time": 1698916640.0831163,
  "agent_name": "Elena Piscopia",
  "agent_email": "elena@example.net",
  "extracted_metadata": {
    "@context": {
      "@vocab": "http://schema.org/",
      "datalad": "http://dx.datalad.org/"
    },
    "@graph": [
      {
        "@id": "d3765bf6✂MD5",
        "@type": "agent",
        "name": "Adina Wagner",
        "email": "adina.wagner@t-online.de"
      },
      {
        "@id": "66aa4e8a✂SHA1",
        "identifier": "2d540a9d-2ef7-4b5f-8931-7c92f483f0c7",
        "@type": "Dataset",
        "version": "0-3-g66aa4e8",
        "dateCreated": "2023-02-13T07:48:15+01:00",
        "dateModified": "2023-11-01T13:08:02+01:00",
        "hasContributor": {
          "@id": "d3765bf6✂MD5"
        },
        "distribution": [
          {
            "@id": "4aef00ff-3027-4364-911a-98905cdbdf8d"
          },
          {
            "@id": "b8938507-77c0-4479-84a6-cbeb41214ca6"
          },
          {
            "name": "origin",
            "url": "https://github.com/datalad-handbook/cozy-screensavers.git"
          }
        ]
      }
    ]
  }
}

The extracted metadata can then either be saved into a file as before, or directly pipe’d into datalad meta-add.

7.3.3.2. Creating your own extractor

The MetaLad docs have a dedicated user guide that walks you through the process of creating your own extractor. Have a look at docs.datalad.org/projects/metalad/user_guide/writing-extractors.html.

7.3.4. Distributing and Getting Metadata

Once metadata has been added to a dataset, it can be distributed and retrieved. Instead of creating and adding metadata yourself, you could download fitting pre-existing metadata. Similarly, instead of repeating a meta-add process for one and the same files across hierarchies of datasets, metadata added into one dataset can be exported into other datasets. Regardless of whether it is a distribution or a retrieval process, though, an export with MetaLad will only concern the metadata, and never the primary data.

7.3.4.1. Download Metadata from a remote repository

Let’s start by creating a place where someone else’s metadata could live.

$ datalad create metadata-assimilation
$ cd metadata-assimilation
create(ok): /home/me/beyond_basics/meta/metadata-assimilation (dataset)

Because MetaLad stores metadata in Git’s object store, we use Git to directly fetch metadata from a remote repository, such as this demo on GitHub: https://github.com/christian-monch/metadata-test.git. Because metadata added by MetaLad is not transported automatically but needs to be specifically requested, the command to retrieve it looks unfamiliar to non-Git-users: It identifies the precise location of the ref that contains the metadata.

$ git fetch \
   "https://github.com/christian-monch/metadata-test.git" \
   "refs/datalad/*:refs/datalad/*"
From https://github.com/christian-monch/metadata-test
 * [new ref]         refs/datalad/dataset-tree-version-list -> refs/datalad/dataset-tree-version-list
 * [new ref]         refs/datalad/dataset-uuid-set -> refs/datalad/dataset-uuid-set
 * [new ref]         refs/datalad/object-references-2.0 -> refs/datalad/object-references-2.0

Exactly where is metadata stored, and why?

MetaLad employs an internal metadata model that makes the following properties possible:

  • Metadata has a version encoded, but isn’t itself version controlled

  • Metadata should not be transported if not explicitly requested

  • It should be possible to only retrieve parts of the overall metadata tree, e.g. certain sub-nodes

To fulfill this, metadata is stored in Git’s internal object store as a blob, and Git ref's are used to point to these blobs. To not automatically transport them, they are organized in a directory that isn’t fetched or pushed by default, but can be transported by explicitly fetching or pushing it: .git/refs/datalad[5].

After fetching these refs, they can be found in the metadata-assimilation dataset:

$ tree .git/refs
.git/refs
├── datalad
│   ├── dataset-tree-version-list
│   ├── dataset-uuid-set
│   └── object-references-2.0
├── heads
│   ├── git-annex
│   └── main
└── tags

3 directories, 5 files

Just like other Git refs, these refs are files that identify Git objects or trees. By utilizing Git’s internal plumbing commands, we can follow them:

$ cat .git/refs/datalad/dataset-tree-version-list
589c3c74✂SHA1
$ git show 589c3c74✂SHA1 | jq
[
  {
    "primary_data_version": "b3f02438✂SHA1",
    "time_stamp": "1677249905.6491013",
    "path": "",
    "dataset_tree": {
      "@": {
        "type": "Reference",
        "version": "2.0"
      },
      "class_name": "MTreeNode",
      "location": "9ddcabd5✂SHA1"
    }
  },
  {
    "primary_data_version": "96883aef✂SHA1",
    "time_stamp": "1677249905.6497943",
    "path": "",
    "dataset_tree": {
      "@": {
        "type": "Reference",
        "version": "2.0"
      },
      "class_name": "MTreeNode",
      "location": "dc8f7491✂SHA1"
    }
  },
  {
    "primary_data_version": "4bcda921✂SHA1",
    "time_stamp": "1677249905.6502168",
    "path": "",
    "dataset_tree": {
      "@": {
        "type": "Reference",
        "version": "2.0"
      },
      "class_name": "MTreeNode",
      "location": "33e1f59d✂SHA1"
    }
  },
  {
    "primary_data_version": "c9f77378✂SHA1",
    "time_stamp": "1677249905.6506386",
    "path": "",
    "dataset_tree": {
      "@": {
        "type": "Reference",
        "version": "2.0"
      },
      "class_name": "MTreeNode",
      "location": "b539974a✂SHA1"
    }
  },
  {
    "primary_data_version": "1fdf34c1✂SHA1",
    "time_stamp": "1677249905.6510594",
    "path": "",
    "dataset_tree": {
      "@": {
        "type": "Reference",
        "version": "2.0"
      },
      "class_name": "MTreeNode",
      "location": "ac9d80ba✂SHA1"
    }
  },
  {
    "primary_data_version": "734d1649✂SHA1",
    "time_stamp": "1677249905.6515157",
    "path": "",
    "dataset_tree": {
      "@": {
        "type": "Reference",
        "version": "2.0"
      },
      "class_name": "MTreeNode",
      "location": "9c7b42cc✂SHA1"
    }
  },
  {
    "primary_data_version": "2a5737a7✂SHA1",
    "time_stamp": "1677249905.6519253",
    "path": "",
    "dataset_tree": {
      "@": {
        "type": "Reference",
        "version": "2.0"
      },
      "class_name": "MTreeNode",
      "location": "f8dba9ab✂SHA1"
    }
  },
  {
    "primary_data_version": "dba9e0da✂SHA1",
    "time_stamp": "1677249905.6523588",
    "path": "",
    "dataset_tree": {
      "@": {
        "type": "Reference",
        "version": "2.0"
      },
      "class_name": "MTreeNode",
      "location": "f3df9655✂SHA1"
    }
  },
  {
    "primary_data_version": "5d344fee✂SHA1",
    "time_stamp": "1677249905.6528437",
    "path": "",
    "dataset_tree": {
      "@": {
        "type": "Reference",
        "version": "2.0"
      },
      "class_name": "MTreeNode",
      "location": "94287257✂SHA1"
    }
  },
  {
    "primary_data_version": "4862b1dd✂SHA1",
    "time_stamp": "1677249905.653256",
    "path": "",
    "dataset_tree": {
      "@": {
        "type": "Reference",
        "version": "2.0"
      },
      "class_name": "MTreeNode",
      "location": "72b9f0ef✂SHA1"
    }
  },
  {
    "primary_data_version": "927fa89d✂SHA1",
    "time_stamp": "1677249905.6537495",
    "path": "",
    "dataset_tree": {
      "@": {
        "type": "Reference",
        "version": "2.0"
      },
      "class_name": "MTreeNode",
      "location": "a21b7afa✂SHA1"
    }
  },
  {
    "primary_data_version": "24cf1d64✂SHA1",
    "time_stamp": "1677249905.6543043",
    "path": "",
    "dataset_tree": {
      "@": {
        "type": "Reference",
        "version": "2.0"
      },
      "class_name": "MTreeNode",
      "location": "6df5503c✂SHA1"
    }
  },
  {
    "primary_data_version": "608716cc✂SHA1",
    "time_stamp": "1677249905.654619",
    "path": "",
    "dataset_tree": {
      "@": {
        "type": "Reference",
        "version": "2.0"
      },
      "class_name": "MTreeNode",
      "location": "9c25a07b✂SHA1"
    }
  },
  {
    "primary_data_version": "7d80d256✂SHA1",
    "time_stamp": "1677249905.6549146",
    "path": "",
    "dataset_tree": {
      "@": {
        "type": "Reference",
        "version": "2.0"
      },
      "class_name": "MTreeNode",
      "location": "6bcafa8a✂SHA1"
    }
  },
  {
    "primary_data_version": "089d0a8a✂SHA1",
    "time_stamp": "1677249905.6551852",
    "path": "",
    "dataset_tree": {
      "@": {
        "type": "Reference",
        "version": "2.0"
      },
      "class_name": "MTreeNode",
      "location": "97ef1f70✂SHA1"
    }
  },
  {
    "primary_data_version": "ae851681✂SHA1",
    "time_stamp": "1677249905.655501",
    "path": "",
    "dataset_tree": {
      "@": {
        "type": "Reference",
        "version": "2.0"
      },
      "class_name": "MTreeNode",
      "location": "e12fe917✂SHA1"
    }
  },
  {
    "primary_data_version": "66955e1d✂SHA1",
    "time_stamp": "1677249905.6558466",
    "path": "",
    "dataset_tree": {
      "@": {
        "type": "Reference",
        "version": "2.0"
      },
      "class_name": "MTreeNode",
      "location": "1fe9d3de✂SHA1"
    }
  },
  {
    "primary_data_version": "7b966eab✂SHA1",
    "time_stamp": "1677249905.656139",
    "path": "",
    "dataset_tree": {
      "@": {
        "type": "Reference",
        "version": "2.0"
      },
      "class_name": "MTreeNode",
      "location": "778aba3b✂SHA1"
    }
  },
  {
    "primary_data_version": "ba052a5a✂SHA1",
    "time_stamp": "1677249905.6564295",
    "path": "",
    "dataset_tree": {
      "@": {
        "type": "Reference",
        "version": "2.0"
      },
      "class_name": "MTreeNode",
      "location": "edfad1df✂SHA1"
    }
  },
  {
    "primary_data_version": "8219d0af✂SHA1",
    "time_stamp": "1677249905.6567576",
    "path": "",
    "dataset_tree": {
      "@": {
        "type": "Reference",
        "version": "2.0"
      },
      "class_name": "MTreeNode",
      "location": "02744bd9✂SHA1"
    }
  },
  {
    "primary_data_version": "43391d89✂SHA1",
    "time_stamp": "1677249905.6570334",
    "path": "",
    "dataset_tree": {
      "@": {
        "type": "Reference",
        "version": "2.0"
      },
      "class_name": "MTreeNode",
      "location": "f937e00e✂SHA1"
    }
  },
  {
    "primary_data_version": "afdc3973✂SHA1",
    "time_stamp": "1677249905.657296",
    "path": "",
    "dataset_tree": {
      "@": {
        "type": "Reference",
        "version": "2.0"
      },
      "class_name": "MTreeNode",
      "location": "b3468f97✂SHA1"
    }
  },
  {
    "primary_data_version": "f801161e✂SHA1",
    "time_stamp": "1677249905.6575809",
    "path": "",
    "dataset_tree": {
      "@": {
        "type": "Reference",
        "version": "2.0"
      },
      "class_name": "MTreeNode",
      "location": "43c62b84✂SHA1"
    }
  },
  {
    "primary_data_version": "15f405a8✂SHA1",
    "time_stamp": "1677249905.6578848",
    "path": "",
    "dataset_tree": {
      "@": {
        "type": "Reference",
        "version": "2.0"
      },
      "class_name": "MTreeNode",
      "location": "c3c0f6d7✂SHA1"
    }
  },
  {
    "primary_data_version": "c0b548cd✂SHA1",
    "time_stamp": "1677249905.6581686",
    "path": "",
    "dataset_tree": {
      "@": {
        "type": "Reference",
        "version": "2.0"
      },
      "class_name": "MTreeNode",
      "location": "28551fe3✂SHA1"
    }
  },
  {
    "primary_data_version": "244a8ad4✂SHA1",
    "time_stamp": "1677249905.6585097",
    "path": "",
    "dataset_tree": {
      "@": {
        "type": "Reference",
        "version": "2.0"
      },
      "class_name": "MTreeNode",
      "location": "809e3e50✂SHA1"
    }
  }
]
$ git ls-tree 9ddcabd5✂SHA1
dc8f7491✂SHA1
33e1f59d✂SHA1
b539974a✂SHA1
ac9d80ba✂SHA1
9c7b42cc✂SHA1
f8dba9ab✂SHA1
f3df9655✂SHA1
94287257✂SHA1
72b9f0ef✂SHA1
a21b7afa✂SHA1
6df5503c✂SHA1
9c25a07b✂SHA1
6bcafa8a✂SHA1
97ef1f70✂SHA1
e12fe917✂SHA1
1fe9d3de✂SHA1
778aba3b✂SHA1
edfad1df✂SHA1
02744bd9✂SHA1
f937e00e✂SHA1
b3468f97✂SHA1
43c62b84✂SHA1
c3c0f6d7✂SHA1
28551fe3✂SHA1
809e3e50✂SHA1

The identifier study-100 in a line such as 040000 tree d1ad9bfa56f5aa25a1d28caf13db719b9e710d28       study-100 is the dataset_path value of a given metadata entry. Commands such as meta-dump can use them to, e.g., only report on metadata for certain datasets, following the pattern

[DATASET_PATH] ["@" VERSION-DIGITS] [":" [LOCAL_PATH]]

e.g., ./study-100. While this is no workflow a user would have to do, this exploration might have nevertheless gotten you some insights into the inner workings of the commands and MetaLad’s internal storage model.

The metadata is now locally available in the Git repository metadata-repo. You can verify this by issuing the command datalad meta-dump -r, which will list all metadata from all dataset_paths in the repository. Can you guess what type of metadata it contains[6] ?

$ datalad meta-dump -r
{"type": "dataset", "root_dataset_id": "84e51a9a-dc8b-11ea-ae66-7cdd908c7490", "root_dataset_version": "b3f02438✂SHA1", "dataset_path": "study-99", "dataset_id": "52b2f098-dc76-11ea-a6da-7cdd908c7490", "dataset_version": "1b0d92f6✂SHA1", "extraction_time": 1675090458.2812586, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Mueller14_EEGSZ", "keywords": ["Schizophrenia"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#52b2f098-dc76-11ea-a6da-7cdd908c7490", "@type": "Dataset", "version": "1b0d92f6✂SHA1", "name": "Dataset_omodpirgos_0", "url": "https://example.com/users/omodpirgos/dataset_0", "author": [{"@id": "e.omodpirgos@example.com"}], "description": "<this is an autogenerated description for dataset 52b2f098-dc76-11ea-a6da-7cdd908c7490, since no description was provided by the author, and because google rich-results requires the description-property in schmema.org/Dataset metadatatypes>"}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "in congruent Schizophrenia face emotional by of affective deficits", "datePublished": 2102, "sameAs": "10.100/99.abc-1", "pagination": "525-553", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "o.mal@example.com"}, {"@id": "o.gos@example.com"}, {"@id": "e.sane@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Soc Cogn Affect Neurosci", "@type": "PublicationEvent", "name": "Soc Cogn Affect Neurosci"}, "isPartOf": {"@id": "#issue(4)", "@type": "PublicationIssue", "issue_number": 4, "isPartOf": {"@id": "#volume(9)", "@type": "PublicationVolume", "volumeNumber": 9}}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#m.apalsul@example.com", "@type": "Person", "email": "m.apalsul@example.com", "name": "Dr. Malmelu Apalsul", "givenName": "Malmelu", "familyName": "Apalsul", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#a.gose@example.com", "@type": "Person", "email": "a.gose@example.com", "name": "Asulmolsana Gose", "givenName": "Asulmolsana", "familyName": "Gose"}, {"@id": "https://schema.datalad.org/person#g.agor@example.com", "@type": "Person", "email": "g.agor@example.com", "name": "Gossulsela Agor", "givenName": "Gossulsela", "familyName": "Agor"}, {"@id": "https://schema.datalad.org/person#e.ekos@example.com", "@type": "Person", "email": "e.ekos@example.com", "name": "Esulsana Ekos", "givenName": "Esulsana", "familyName": "Ekos"}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}]}]}}
{"type": "dataset", "root_dataset_id": "535edf84-dc76-11ea-979c-7cdd908c7490", "root_dataset_version": "96883aef✂SHA1", "dataset_path": "study-98", "dataset_id": "84e51a9a-dc8b-11ea-ae66-7cdd908c7490", "dataset_version": "b3f02438✂SHA1", "extraction_time": 1675090458.2790313, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Mueller15_cognflexibility", "keywords": ["Interindividual differences"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#535edf84-dc76-11ea-979c-7cdd908c7490", "@type": "Dataset", "version": "96883aef✂SHA1", "name": "Dataset_omodpirgos_3", "url": "https://example.com/users/omodpirgos/dataset_3", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "i.upirmale@example.com"}], "description": "<this is an autogenerated description for dataset 535edf84-dc76-11ea-979c-7cdd908c7490, since no description was provided by the author, and because google rich-results requires the description-property in schmema.org/Dataset metadatatypes>"}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "Interindividual of volume, differences impulsivity functional flexibility: influence matter and connectivity in", "datePublished": 2105, "sameAs": "10.100/99.abc-4", "pagination": "151-169", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "i.upirmale@example.com"}, {"@id": "e.malu@example.com"}, {"@id": "p.molludsulo@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Brain Struct Func", "@type": "PublicationEvent", "name": "Brain Struct Func"}, "isPartOf": {"@id": "#issue(4)", "@type": "PublicationIssue", "issue_number": 4, "isPartOf": {"@id": "#volume(220)", "@type": "PublicationVolume", "volumeNumber": 220}}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#m.apalsul@example.com", "@type": "Person", "email": "m.apalsul@example.com", "name": "Dr. Malmelu Apalsul", "givenName": "Malmelu", "familyName": "Apalsul", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#a.ugorsan@example.com", "@type": "Person", "email": "a.ugorsan@example.com", "name": "Dr. Apirsan Ugorsan", "givenName": "Apirsan", "familyName": "Ugorsan", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}, {"@id": "https://schema.datalad.org/person#m.pal@example.com", "@type": "Person", "email": "m.pal@example.com", "name": "Dr. phil. Madludlade Pal", "givenName": "Madludlade", "familyName": "Pal", "honorificSuffix": "Dr. phil."}]}]}}
{"type": "dataset", "root_dataset_id": "542308c8-dc76-11ea-940a-7cdd908c7490", "root_dataset_version": "4bcda921✂SHA1", "dataset_path": "study-97", "dataset_id": "84e51a9a-dc8b-11ea-ae66-7cdd908c7490", "dataset_version": "b3f02438✂SHA1", "extraction_time": 1675090458.2600777, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Mueller18_FaceALE", "keywords": ["meta-analysis"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#542308c8-dc76-11ea-940a-7cdd908c7490", "@type": "Dataset", "version": "4bcda921✂SHA1", "name": "Dataset_omodpirgos_6", "url": "https://example.com/users/omodpirgos/dataset_6", "author": [{"@id": "e.omodpirgos@example.com"}], "description": "<this is an autogenerated description for dataset 542308c8-dc76-11ea-940a-7cdd908c7490, since no description was provided by the author, and because google rich-results requires the description-property in schmema.org/Dataset metadatatypes>"}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "the An Influence task processing: of neural and meta-analysis of face network ALE", "datePublished": 2108, "sameAs": "10.100/99.abc-7", "pagination": "1634-1655", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "m.amol@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Cortex", "@type": "PublicationEvent", "name": "Cortex"}, "isPartOf": {"@id": "#volume(103)", "@type": "PublicationVolume", "volumeNumber": 103}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#m.apalsul@example.com", "@type": "Person", "email": "m.apalsul@example.com", "name": "Dr. Malmelu Apalsul", "givenName": "Malmelu", "familyName": "Apalsul", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}, {"@id": "https://schema.datalad.org/person#o.ladlud@example.com", "@type": "Person", "email": "o.ladlud@example.com", "name": "Dr. Omorkolsan Ladlud", "givenName": "Omorkolsan", "familyName": "Ladlud", "honorificSuffix": "Dr."}]}]}}
{"type": "dataset", "root_dataset_id": "54ded198-dc76-11ea-9eff-7cdd908c7490", "root_dataset_version": "c9f77378✂SHA1", "dataset_path": "study-96", "dataset_id": "84e51a9a-dc8b-11ea-ae66-7cdd908c7490", "dataset_version": "b3f02438✂SHA1", "extraction_time": 1675090458.262438, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Nostro18_Prediction", "keywords": ["study"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}, {"@id": "o.omorpallud@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#54ded198-dc76-11ea-9eff-7cdd908c7490", "@type": "Dataset", "version": "c9f77378✂SHA1", "name": "Dataset_omodpirgos_9", "url": "https://example.com/users/omodpirgos/dataset_9", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "o.omorpallud@example.com"}], "description": "<this is an autogenerated description for dataset 54ded198-dc76-11ea-9eff-7cdd908c7490, since no description was provided by the author, and because google rich-results requires the description-property in schmema.org/Dataset metadatatypes>"}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "network-based connectivity functional personality", "datePublished": 2111, "sameAs": "10.100/99.abc-10", "pagination": "1374-1394", "author": [{"@id": "a.ilud@example.com"}, {"@id": "e.omodpirgos@example.com"}, {"@id": "a.emadsanpali@example.com"}, {"@id": "p.rigmorluda@example.com"}, {"@id": "m.kolkospale@example.com"}, {"@id": "i.upirmale@example.com"}, {"@id": "m.opalsulkosu@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Brain Struct Funct", "@type": "PublicationEvent", "name": "Brain Struct Funct"}, "isPartOf": {"@id": "#issue(6)", "@type": "PublicationIssue", "issue_number": 6, "isPartOf": {"@id": "#volume(223)", "@type": "PublicationVolume", "volumeNumber": 223}}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}, {"@id": "https://schema.datalad.org/person#m.apalsul@example.com", "@type": "Person", "email": "m.apalsul@example.com", "name": "Dr. Malmelu Apalsul", "givenName": "Malmelu", "familyName": "Apalsul", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#i.mora@example.com", "@type": "Person", "email": "i.mora@example.com", "name": "Imolpir Mora", "givenName": "Imolpir", "familyName": "Mora"}, {"@id": "https://schema.datalad.org/person#g.lad@example.com", "@type": "Person", "email": "g.lad@example.com", "name": "Gosa Lad", "givenName": "Gosa", "familyName": "Lad"}, {"@id": "https://schema.datalad.org/person#a.omol@example.com", "@type": "Person", "email": "a.omol@example.com", "name": "Akosrigo Omol", "givenName": "Akosrigo", "familyName": "Omol"}, {"@id": "https://schema.datalad.org/person#a.modmod@example.com", "@type": "Person", "email": "a.modmod@example.com", "name": "Dr. Aselpal Modmod", "givenName": "Aselpal", "familyName": "Modmod", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#a.gos@example.com", "@type": "Person", "email": "a.gos@example.com", "name": "Aselu Gos", "givenName": "Aselu", "familyName": "Gos"}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#u.pulpula@example.com", "@type": "Person", "email": "u.pulpula@example.com", "name": "Upir Pulpula", "givenName": "Upir", "familyName": "Pulpula"}]}]}}
{"type": "dataset", "root_dataset_id": "5599d916-dc76-11ea-8d5f-7cdd908c7490", "root_dataset_version": "1fdf34c1✂SHA1", "dataset_path": "study-95", "dataset_id": "84e51a9a-dc8b-11ea-ae66-7cdd908c7490", "dataset_version": "b3f02438✂SHA1", "extraction_time": 1675090458.2433488, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Nostro17_VBM", "keywords": ["Correlation Personality and Brain Structure"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}, {"@id": "o.omorpallud@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#5599d916-dc76-11ea-8d5f-7cdd908c7490", "@type": "Dataset", "version": "1fdf34c1✂SHA1", "name": "Dataset_omodpirgos_12", "url": "https://example.com/users/omodpirgos/dataset_12", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "o.omorpallud@example.com"}], "description": "<this is an autogenerated description for dataset 5599d916-dc76-11ea-8d5f-7cdd908c7490, since no description was provided by the author, and because google rich-results requires the description-property in schmema.org/Dataset metadatatypes>"}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "Correlations and of Crucial A Brain Between Structure:", "datePublished": 2114, "sameAs": "10.100/99.abc-13", "pagination": "1945-1972", "author": [{"@id": "a.ilud@example.com"}, {"@id": "e.omodpirgos@example.com"}, {"@id": "l.imel@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Cereb Cortex", "@type": "PublicationEvent", "name": "Cereb Cortex"}, "isPartOf": {"@id": "#issue(7)", "@type": "PublicationIssue", "issue_number": 7, "isPartOf": {"@id": "#volume(27)", "@type": "PublicationVolume", "volumeNumber": 27}}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#i.ugos@example.com", "@type": "Person", "email": "i.ugos@example.com", "name": "Imad Ugos", "givenName": "Imad", "familyName": "Ugos"}, {"@id": "https://schema.datalad.org/person#g.lad@example.com", "@type": "Person", "email": "g.lad@example.com", "name": "Gosa Lad", "givenName": "Gosa", "familyName": "Lad"}, {"@id": "https://schema.datalad.org/person#m.apalsul@example.com", "@type": "Person", "email": "m.apalsul@example.com", "name": "Dr. Malmelu Apalsul", "givenName": "Malmelu", "familyName": "Apalsul", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}, {"@id": "https://schema.datalad.org/person#u.pulpula@example.com", "@type": "Person", "email": "u.pulpula@example.com", "name": "Upir Pulpula", "givenName": "Upir", "familyName": "Pulpula"}]}]}}
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{"type": "dataset", "root_dataset_id": "4f03a794-dc76-11ea-994b-7cdd908c7490", "root_dataset_version": "afdc3973✂SHA1", "dataset_path": "study-104", "dataset_id": "84e51a9a-dc8b-11ea-ae66-7cdd908c7490", "dataset_version": "b3f02438✂SHA1", "extraction_time": 1675090457.6748545, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Camilleri_Multistate_Collaborations", "keywords": ["study"], "accountablePerson": "g.pirpirludu@example.com", "contributor": [{"@id": "o.omorpallud@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#4f03a794-dc76-11ea-994b-7cdd908c7490", "@type": "Dataset", "version": "afdc3973✂SHA1", "name": "Dataset_omorpallud_63", "url": "https://example.com/users/omorpallud/dataset_63", "author": [{"@id": "o.omorpallud@example.com"}, {"@id": "s.omelpire@example.com"}], "description": "<this is an autogenerated description for dataset 4f03a794-dc76-11ea-994b-7cdd908c7490, since no description was provided by the author, and because google rich-results requires the description-property in schmema.org/Dataset metadatatypes>"}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#g.pirpirludu@example.com", "@type": "Person", "email": "g.pirpirludu@example.com", "name": "Goso Pirpirludu", "givenName": "Goso", "familyName": "Pirpirludu"}, {"@id": "https://schema.datalad.org/person#u.pulpula@example.com", "@type": "Person", "email": "u.pulpula@example.com", "name": "Upir Pulpula", "givenName": "Upir", "familyName": "Pulpula"}]}]}}
{"type": "dataset", "root_dataset_id": "4fb7be64-dc76-11ea-ad07-7cdd908c7490", "root_dataset_version": "f801161e✂SHA1", "dataset_path": "study-103", "dataset_id": "84e51a9a-dc8b-11ea-ae66-7cdd908c7490", "dataset_version": "b3f02438✂SHA1", "extraction_time": 1675090457.6666787, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "OHBM15", "keywords": ["OHBM 2015"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#4fb7be64-dc76-11ea-ad07-7cdd908c7490", "@type": "Dataset", "version": "f801161e✂SHA1", "name": "Dataset_omodpirgos_64", "url": "https://example.com/users/omodpirgos/dataset_64", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "i.upirmale@example.com"}], "description": "<this is an autogenerated description for dataset 4fb7be64-dc76-11ea-ad07-7cdd908c7490, since no description was provided by the author, and because google rich-results requires the description-property in schmema.org/Dataset metadatatypes>"}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#m.apalsul@example.com", "@type": "Person", "email": "m.apalsul@example.com", "name": "Dr. Malmelu Apalsul", "givenName": "Malmelu", "familyName": "Apalsul", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}]}]}}
{"type": "dataset", "root_dataset_id": "506ce096-dc76-11ea-95f0-7cdd908c7490", "root_dataset_version": "15f405a8✂SHA1", "dataset_path": "study-102", "dataset_id": "84e51a9a-dc8b-11ea-ae66-7cdd908c7490", "dataset_version": "b3f02438✂SHA1", "extraction_time": 1675090457.6932034, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "OHBM_2019", "keywords": ["OHBM 2019"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#506ce096-dc76-11ea-95f0-7cdd908c7490", "@type": "Dataset", "version": "15f405a8✂SHA1", "name": "Dataset_omodpirgos_65", "url": "https://example.com/users/omodpirgos/dataset_65", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "i.upirmale@example.com"}], "description": "<this is an autogenerated description for dataset 506ce096-dc76-11ea-95f0-7cdd908c7490, since no description was provided by the author, and because google rich-results requires the description-property in schmema.org/Dataset metadatatypes>"}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#m.apalsul@example.com", "@type": "Person", "email": "m.apalsul@example.com", "name": "Dr. Malmelu Apalsul", "givenName": "Malmelu", "familyName": "Apalsul", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}]}]}}
{"type": "dataset", "root_dataset_id": "5146e8d6-dc76-11ea-a2da-7cdd908c7490", "root_dataset_version": "c0b548cd✂SHA1", "dataset_path": "study-101", "dataset_id": "84e51a9a-dc8b-11ea-ae66-7cdd908c7490", "dataset_version": "b3f02438✂SHA1", "extraction_time": 1675090457.6865904, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Camilleri15_MultiModal", "keywords": ["study"], "accountablePerson": "g.pirpirludu@example.com"}, {"@id": "https://schema.datalad.org/datalad_dataset#5146e8d6-dc76-11ea-a2da-7cdd908c7490", "@type": "Dataset", "version": "c0b548cd✂SHA1", "name": "Dataset_omorpallud_66", "url": "https://example.com/users/omorpallud/dataset_66", "author": [{"@id": "o.omorpallud@example.com"}, {"@id": "s.omelpire@example.com"}], "description": "<this is an autogenerated description for dataset 5146e8d6-dc76-11ea-a2da-7cdd908c7490, since no description was provided by the author, and because google rich-results requires the description-property in schmema.org/Dataset metadatatypes>"}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "trail of of motor speed performance Multi-modal the correlates making imaging", "datePublished": 2168, "accountablePerson": "u.pulpula@example.com", "sameAs": "10.100/99.abc-67", "author": [{"@id": "o.omorpallud@example.com"}, {"@id": "l.imel@example.com"}, {"@id": "e.omodpirgos@example.com"}, {"@id": "i.gorsel@example.com"}, {"@id": "e.pal@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Frontiers in neurology", "@type": "PublicationEvent", "name": "Frontiers in neurology"}, "description": "e.kospulsulu@example.com", "isPartOf": {"@id": "#issue(219)", "@type": "PublicationIssue", "issue_number": 219, "isPartOf": {"@id": "#volume(6)", "@type": "PublicationVolume", "volumeNumber": 6}}, "pagination": "1446-1454"}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#g.pirpirludu@example.com", "@type": "Person", "email": "g.pirpirludu@example.com", "name": "Goso Pirpirludu", "givenName": "Goso", "familyName": "Pirpirludu"}, {"@id": "https://schema.datalad.org/person#u.pulpula@example.com", "@type": "Person", "email": "u.pulpula@example.com", "name": "Upir Pulpula", "givenName": "Upir", "familyName": "Pulpula"}, {"@id": "https://schema.datalad.org/person#i.ugos@example.com", "@type": "Person", "email": "i.ugos@example.com", "name": "Imad Ugos", "givenName": "Imad", "familyName": "Ugos"}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#e.mori@example.com", "@type": "Person", "email": "e.mori@example.com", "name": "Dr. phil. Epuli Mori", "givenName": "Epuli", "familyName": "Mori", "honorificSuffix": "Dr. phil."}, {"@id": "https://schema.datalad.org/person#a.osel@example.com", "@type": "Person", "email": "a.osel@example.com", "name": "Dr. phil. Amellade Osel", "givenName": "Amellade", "familyName": "Osel", "honorificSuffix": "Dr. phil."}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}]}]}}
{"type": "dataset", "root_dataset_id": "52142b84-dc76-11ea-98c5-7cdd908c7490", "root_dataset_version": "244a8ad4✂SHA1", "dataset_path": "study-100", "dataset_id": "84e51a9a-dc8b-11ea-ae66-7cdd908c7490", "dataset_version": "b3f02438✂SHA1", "extraction_time": 1675090457.6464868, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Camilleri18_eMDN", "keywords": ["neural correlates"], "accountablePerson": "g.pirpirludu@example.com", "contributor": [{"@id": "o.omorpallud@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#52142b84-dc76-11ea-98c5-7cdd908c7490", "@type": "Dataset", "version": "244a8ad4✂SHA1", "name": "Dataset_omorpallud_69", "url": "https://example.com/users/omorpallud/dataset_69", "author": [{"@id": "o.omorpallud@example.com"}], "description": "<this is an autogenerated description for dataset 52142b84-dc76-11ea-98c5-7cdd908c7490, since no description was provided by the author, and because google rich-results requires the description-property in schmema.org/Dataset metadatatypes>"}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "multiple-demand Definition extended network and", "datePublished": 2171, "sameAs": "10.100/99.abc-70", "pagination": "1549-1564", "author": [{"@id": "o.omorpallud@example.com"}, {"@id": "e.omodpirgos@example.com"}, {"@id": "m.meli@example.com"}, {"@id": "m.asan@example.com"}, {"@id": "m.kolkospale@example.com"}, {"@id": "m.emodgos@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Neuroimage", "@type": "PublicationEvent", "name": "Neuroimage"}, "isPartOf": {"@id": "#volume(165)", "@type": "PublicationVolume", "volumeNumber": 165}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#u.pulpula@example.com", "@type": "Person", "email": "u.pulpula@example.com", "name": "Upir Pulpula", "givenName": "Upir", "familyName": "Pulpula"}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#k.umalpalo@example.com", "@type": "Person", "email": "k.umalpalo@example.com", "name": "Kosselo Umalpalo", "givenName": "Kosselo", "familyName": "Umalpalo"}, {"@id": "https://schema.datalad.org/person#s.omelgorladi@example.com", "@type": "Person", "email": "s.omelgorladi@example.com", "name": "Dr. phil. Sansul Omelgorladi", "givenName": "Sansul", "familyName": "Omelgorladi", "honorificSuffix": "Dr. phil."}, {"@id": "https://schema.datalad.org/person#i.mora@example.com", "@type": "Person", "email": "i.mora@example.com", "name": "Imolpir Mora", "givenName": "Imolpir", "familyName": "Mora"}, {"@id": "https://schema.datalad.org/person#m.imolsel@example.com", "@type": "Person", "email": "m.imolsel@example.com", "name": "Melmodmad Imolsel", "givenName": "Melmodmad", "familyName": "Imolsel"}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}, {"@id": "https://schema.datalad.org/person#g.pirpirludu@example.com", "@type": "Person", "email": "g.pirpirludu@example.com", "name": "Goso Pirpirludu", "givenName": "Goso", "familyName": "Pirpirludu"}]}]}}

A final note is that datalad meta-dump can also be a source of metadata for datalad meta-add. While metadata can indeed be provided manually, or by running extractors as outlined so far, it can also be provided by any other means that create correct metadata records, and datalad meta-dump is one of them. For example, you could copy the complete metadata from dataset_0 to dataset_1, by dumping it from one dataset into another:

$ datalad meta-dump -d dataset_0 -r | \
  datalad meta-add -d dataset_1 --json-lines -

7.3.4.2. Publish metadata to a Git-Repository

You can also push your metadata to a remote sibling (if you have write permissions). This, too, uses a Git command to push only specific refs. Let’s assume you are in the directory that contains the git repository with your metadata, then you can push your metadata to a remote git repository <your repository>:

$ git push "<your repository>" "refs/datalad/*:refs/datalad/*"

You will notice that no primary data is stored in the repository metadata-destination. That allows you to publish metadata without publishing the primary data at the same time.

7.3.5. Querying metadata

As the metadata is in a highly structured form, and could correspond to agreed-upon or established schemas, queries through such metadata can use flexible tooling and don’t need to rely on DataLad. One popular choice for working with JSON data, for example, is the JSON command line processor jq. In conjunction with Unix pipes, one can assemble powerful queries in a single line. The (cropped) query below, for example, lists all unique family names of the authors in the institute’s scientific project metadata in metadata-assimilation:

$ datalad meta-dump -r | jq '.extracted_metadata["@graph"][3]["@list"][].familyName' | sort | uniq
"Agor"
"Akolkol"
"Akolmormal"
"Amodkosgoru"
"Apali"
"Apalsul"
"Apir"
"Aselmoro"
"Ekos"
"Ekoslud"
"Emalkosseli"
"Emalselkosu"
"Esanmodmadu"
"Esel"
"Eselmodi"

7.3.5.1. Querying metadata remotely

You do not have to download metadata to dump it. It is also possible to specify a git-repository, and let metalad only read the metadata that it requires to fulfill your request. For example, in order to only retrieve metadata from a metadata entry that has the dataset_path value of study-100, you can simply run:

$ datalad meta-dump \
   -d  https://github.com/christian-monch/metadata-test.git \
   ./study-100
{"type": "dataset", "root_dataset_id": "52142b84-dc76-11ea-98c5-7cdd908c7490", "root_dataset_version": "244a8ad4✂SHA1", "dataset_path": "study-100", "dataset_id": "84e51a9a-dc8b-11ea-ae66-7cdd908c7490", "dataset_version": "b3f02438✂SHA1", "extraction_time": 1675090457.6464868, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Camilleri18_eMDN", "keywords": ["neural correlates"], "accountablePerson": "g.pirpirludu@example.com", "contributor": [{"@id": "o.omorpallud@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#52142b84-dc76-11ea-98c5-7cdd908c7490", "@type": "Dataset", "version": "244a8ad4✂SHA1", "name": "Dataset_omorpallud_69", "url": "https://example.com/users/omorpallud/dataset_69", "author": [{"@id": "o.omorpallud@example.com"}], "description": "<this is an autogenerated description for dataset 52142b84-dc76-11ea-98c5-7cdd908c7490, since no description was provided by the author, and because google rich-results requires the description-property in schmema.org/Dataset metadatatypes>"}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "multiple-demand Definition extended network and", "datePublished": 2171, "sameAs": "10.100/99.abc-70", "pagination": "1549-1564", "author": [{"@id": "o.omorpallud@example.com"}, {"@id": "e.omodpirgos@example.com"}, {"@id": "m.meli@example.com"}, {"@id": "m.asan@example.com"}, {"@id": "m.kolkospale@example.com"}, {"@id": "m.emodgos@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Neuroimage", "@type": "PublicationEvent", "name": "Neuroimage"}, "isPartOf": {"@id": "#volume(165)", "@type": "PublicationVolume", "volumeNumber": 165}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#u.pulpula@example.com", "@type": "Person", "email": "u.pulpula@example.com", "name": "Upir Pulpula", "givenName": "Upir", "familyName": "Pulpula"}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#k.umalpalo@example.com", "@type": "Person", "email": "k.umalpalo@example.com", "name": "Kosselo Umalpalo", "givenName": "Kosselo", "familyName": "Umalpalo"}, {"@id": "https://schema.datalad.org/person#s.omelgorladi@example.com", "@type": "Person", "email": "s.omelgorladi@example.com", "name": "Dr. phil. Sansul Omelgorladi", "givenName": "Sansul", "familyName": "Omelgorladi", "honorificSuffix": "Dr. phil."}, {"@id": "https://schema.datalad.org/person#i.mora@example.com", "@type": "Person", "email": "i.mora@example.com", "name": "Imolpir Mora", "givenName": "Imolpir", "familyName": "Mora"}, {"@id": "https://schema.datalad.org/person#m.imolsel@example.com", "@type": "Person", "email": "m.imolsel@example.com", "name": "Melmodmad Imolsel", "givenName": "Melmodmad", "familyName": "Imolsel"}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}, {"@id": "https://schema.datalad.org/person#g.pirpirludu@example.com", "@type": "Person", "email": "g.pirpirludu@example.com", "name": "Goso Pirpirludu", "givenName": "Goso", "familyName": "Pirpirludu"}]}]}}

As the output shows, this command only downloaded enough data from the remote repository to dump all metadata in the specified dataset tree-path. If you want to query all metadata remotely from the repository you could issue the following command:

$ datalad meta-dump \
  -d https://github.com/christian-monch/metadata-test.git -r
{"type": "dataset", "root_dataset_id": "84e51a9a-dc8b-11ea-ae66-7cdd908c7490", "root_dataset_version": "b3f02438✂SHA1", "dataset_path": "study-99", "dataset_id": "52b2f098-dc76-11ea-a6da-7cdd908c7490", "dataset_version": "1b0d92f6✂SHA1", "extraction_time": 1675090458.2812586, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Mueller14_EEGSZ", "keywords": ["Schizophrenia"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#52b2f098-dc76-11ea-a6da-7cdd908c7490", "@type": "Dataset", "version": "1b0d92f6✂SHA1", "name": "Dataset_omodpirgos_0", "url": "https://example.com/users/omodpirgos/dataset_0", "author": [{"@id": "e.omodpirgos@example.com"}], "description": "<this is an autogenerated description for dataset 52b2f098-dc76-11ea-a6da-7cdd908c7490, since no description was provided by the author, and because google rich-results requires the description-property in schmema.org/Dataset metadatatypes>"}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "in congruent Schizophrenia face emotional by of affective deficits", "datePublished": 2102, "sameAs": "10.100/99.abc-1", "pagination": "525-553", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "o.mal@example.com"}, {"@id": "o.gos@example.com"}, {"@id": "e.sane@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Soc Cogn Affect Neurosci", "@type": "PublicationEvent", "name": "Soc Cogn Affect Neurosci"}, "isPartOf": {"@id": "#issue(4)", "@type": "PublicationIssue", "issue_number": 4, "isPartOf": {"@id": "#volume(9)", "@type": "PublicationVolume", "volumeNumber": 9}}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#m.apalsul@example.com", "@type": "Person", "email": "m.apalsul@example.com", "name": "Dr. Malmelu Apalsul", "givenName": "Malmelu", "familyName": "Apalsul", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#a.gose@example.com", "@type": "Person", "email": "a.gose@example.com", "name": "Asulmolsana Gose", "givenName": "Asulmolsana", "familyName": "Gose"}, {"@id": "https://schema.datalad.org/person#g.agor@example.com", "@type": "Person", "email": "g.agor@example.com", "name": "Gossulsela Agor", "givenName": "Gossulsela", "familyName": "Agor"}, {"@id": "https://schema.datalad.org/person#e.ekos@example.com", "@type": "Person", "email": "e.ekos@example.com", "name": "Esulsana Ekos", "givenName": "Esulsana", "familyName": "Ekos"}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}]}]}}
{"type": "dataset", "root_dataset_id": "535edf84-dc76-11ea-979c-7cdd908c7490", "root_dataset_version": "96883aef✂SHA1", "dataset_path": "study-98", "dataset_id": "84e51a9a-dc8b-11ea-ae66-7cdd908c7490", "dataset_version": "b3f02438✂SHA1", "extraction_time": 1675090458.2790313, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Mueller15_cognflexibility", "keywords": ["Interindividual differences"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#535edf84-dc76-11ea-979c-7cdd908c7490", "@type": "Dataset", "version": "96883aef✂SHA1", "name": "Dataset_omodpirgos_3", "url": "https://example.com/users/omodpirgos/dataset_3", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "i.upirmale@example.com"}], "description": "<this is an autogenerated description for dataset 535edf84-dc76-11ea-979c-7cdd908c7490, since no description was provided by the author, and because google rich-results requires the description-property in schmema.org/Dataset metadatatypes>"}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "Interindividual of volume, differences impulsivity functional flexibility: influence matter and connectivity in", "datePublished": 2105, "sameAs": "10.100/99.abc-4", "pagination": "151-169", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "i.upirmale@example.com"}, {"@id": "e.malu@example.com"}, {"@id": "p.molludsulo@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Brain Struct Func", "@type": "PublicationEvent", "name": "Brain Struct Func"}, "isPartOf": {"@id": "#issue(4)", "@type": "PublicationIssue", "issue_number": 4, "isPartOf": {"@id": "#volume(220)", "@type": "PublicationVolume", "volumeNumber": 220}}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#m.apalsul@example.com", "@type": "Person", "email": "m.apalsul@example.com", "name": "Dr. Malmelu Apalsul", "givenName": "Malmelu", "familyName": "Apalsul", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#a.ugorsan@example.com", "@type": "Person", "email": "a.ugorsan@example.com", "name": "Dr. Apirsan Ugorsan", "givenName": "Apirsan", "familyName": "Ugorsan", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}, {"@id": "https://schema.datalad.org/person#m.pal@example.com", "@type": "Person", "email": "m.pal@example.com", "name": "Dr. phil. Madludlade Pal", "givenName": "Madludlade", "familyName": "Pal", "honorificSuffix": "Dr. phil."}]}]}}
{"type": "dataset", "root_dataset_id": "542308c8-dc76-11ea-940a-7cdd908c7490", "root_dataset_version": "4bcda921✂SHA1", "dataset_path": "study-97", "dataset_id": "84e51a9a-dc8b-11ea-ae66-7cdd908c7490", "dataset_version": "b3f02438✂SHA1", "extraction_time": 1675090458.2600777, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Mueller18_FaceALE", "keywords": ["meta-analysis"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#542308c8-dc76-11ea-940a-7cdd908c7490", "@type": "Dataset", "version": "4bcda921✂SHA1", "name": "Dataset_omodpirgos_6", "url": "https://example.com/users/omodpirgos/dataset_6", "author": [{"@id": "e.omodpirgos@example.com"}], "description": "<this is an autogenerated description for dataset 542308c8-dc76-11ea-940a-7cdd908c7490, since no description was provided by the author, and because google rich-results requires the description-property in schmema.org/Dataset metadatatypes>"}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "the An Influence task processing: of neural and meta-analysis of face network ALE", "datePublished": 2108, "sameAs": "10.100/99.abc-7", "pagination": "1634-1655", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "m.amol@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Cortex", "@type": "PublicationEvent", "name": "Cortex"}, "isPartOf": {"@id": "#volume(103)", "@type": "PublicationVolume", "volumeNumber": 103}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#m.apalsul@example.com", "@type": "Person", "email": "m.apalsul@example.com", "name": "Dr. Malmelu Apalsul", "givenName": "Malmelu", "familyName": "Apalsul", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}, {"@id": "https://schema.datalad.org/person#o.ladlud@example.com", "@type": "Person", "email": "o.ladlud@example.com", "name": "Dr. Omorkolsan Ladlud", "givenName": "Omorkolsan", "familyName": "Ladlud", "honorificSuffix": "Dr."}]}]}}
{"type": "dataset", "root_dataset_id": "54ded198-dc76-11ea-9eff-7cdd908c7490", "root_dataset_version": "c9f77378✂SHA1", "dataset_path": "study-96", "dataset_id": "84e51a9a-dc8b-11ea-ae66-7cdd908c7490", "dataset_version": "b3f02438✂SHA1", "extraction_time": 1675090458.262438, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Nostro18_Prediction", "keywords": ["study"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}, {"@id": "o.omorpallud@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#54ded198-dc76-11ea-9eff-7cdd908c7490", "@type": "Dataset", "version": "c9f77378✂SHA1", "name": "Dataset_omodpirgos_9", "url": "https://example.com/users/omodpirgos/dataset_9", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "o.omorpallud@example.com"}], "description": "<this is an autogenerated description for dataset 54ded198-dc76-11ea-9eff-7cdd908c7490, since no description was provided by the author, and because google rich-results requires the description-property in schmema.org/Dataset metadatatypes>"}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "network-based connectivity functional personality", "datePublished": 2111, "sameAs": "10.100/99.abc-10", "pagination": "1374-1394", "author": [{"@id": "a.ilud@example.com"}, {"@id": "e.omodpirgos@example.com"}, {"@id": "a.emadsanpali@example.com"}, {"@id": "p.rigmorluda@example.com"}, {"@id": "m.kolkospale@example.com"}, {"@id": "i.upirmale@example.com"}, {"@id": "m.opalsulkosu@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Brain Struct Funct", "@type": "PublicationEvent", "name": "Brain Struct Funct"}, "isPartOf": {"@id": "#issue(6)", "@type": "PublicationIssue", "issue_number": 6, "isPartOf": {"@id": "#volume(223)", "@type": "PublicationVolume", "volumeNumber": 223}}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}, {"@id": "https://schema.datalad.org/person#m.apalsul@example.com", "@type": "Person", "email": "m.apalsul@example.com", "name": "Dr. Malmelu Apalsul", "givenName": "Malmelu", "familyName": "Apalsul", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#i.mora@example.com", "@type": "Person", "email": "i.mora@example.com", "name": "Imolpir Mora", "givenName": "Imolpir", "familyName": "Mora"}, {"@id": "https://schema.datalad.org/person#g.lad@example.com", "@type": "Person", "email": "g.lad@example.com", "name": "Gosa Lad", "givenName": "Gosa", "familyName": "Lad"}, {"@id": "https://schema.datalad.org/person#a.omol@example.com", "@type": "Person", "email": "a.omol@example.com", "name": "Akosrigo Omol", "givenName": "Akosrigo", "familyName": "Omol"}, {"@id": "https://schema.datalad.org/person#a.modmod@example.com", "@type": "Person", "email": "a.modmod@example.com", "name": "Dr. Aselpal Modmod", "givenName": "Aselpal", "familyName": "Modmod", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#a.gos@example.com", "@type": "Person", "email": "a.gos@example.com", "name": "Aselu Gos", "givenName": "Aselu", "familyName": "Gos"}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#u.pulpula@example.com", "@type": "Person", "email": "u.pulpula@example.com", "name": "Upir Pulpula", "givenName": "Upir", "familyName": "Pulpula"}]}]}}
{"type": "dataset", "root_dataset_id": "5599d916-dc76-11ea-8d5f-7cdd908c7490", "root_dataset_version": "1fdf34c1✂SHA1", "dataset_path": "study-95", "dataset_id": "84e51a9a-dc8b-11ea-ae66-7cdd908c7490", "dataset_version": "b3f02438✂SHA1", "extraction_time": 1675090458.2433488, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Nostro17_VBM", "keywords": ["Correlation Personality and Brain Structure"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}, {"@id": "o.omorpallud@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#5599d916-dc76-11ea-8d5f-7cdd908c7490", "@type": "Dataset", "version": "1fdf34c1✂SHA1", "name": "Dataset_omodpirgos_12", "url": "https://example.com/users/omodpirgos/dataset_12", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "o.omorpallud@example.com"}], "description": "<this is an autogenerated description for dataset 5599d916-dc76-11ea-8d5f-7cdd908c7490, since no description was provided by the author, and because google rich-results requires the description-property in schmema.org/Dataset metadatatypes>"}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "Correlations and of Crucial A Brain Between Structure:", "datePublished": 2114, "sameAs": "10.100/99.abc-13", "pagination": "1945-1972", "author": [{"@id": "a.ilud@example.com"}, {"@id": "e.omodpirgos@example.com"}, {"@id": "l.imel@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Cereb Cortex", "@type": "PublicationEvent", "name": "Cereb Cortex"}, "isPartOf": {"@id": "#issue(7)", "@type": "PublicationIssue", "issue_number": 7, "isPartOf": {"@id": "#volume(27)", "@type": "PublicationVolume", "volumeNumber": 27}}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#i.ugos@example.com", "@type": "Person", "email": "i.ugos@example.com", "name": "Imad Ugos", "givenName": "Imad", "familyName": "Ugos"}, {"@id": "https://schema.datalad.org/person#g.lad@example.com", "@type": "Person", "email": "g.lad@example.com", "name": "Gosa Lad", "givenName": "Gosa", "familyName": "Lad"}, {"@id": "https://schema.datalad.org/person#m.apalsul@example.com", "@type": "Person", "email": "m.apalsul@example.com", "name": "Dr. Malmelu Apalsul", "givenName": "Malmelu", "familyName": "Apalsul", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}, {"@id": "https://schema.datalad.org/person#u.pulpula@example.com", "@type": "Person", "email": "u.pulpula@example.com", "name": "Upir Pulpula", "givenName": "Upir", "familyName": "Pulpula"}]}]}}
{"type": "dataset", "root_dataset_id": "56370470-dc76-11ea-b64b-7cdd908c7490", "root_dataset_version": "734d1649✂SHA1", "dataset_path": "study-93", "dataset_id": "84e51a9a-dc8b-11ea-ae66-7cdd908c7490", "dataset_version": "b3f02438✂SHA1", "extraction_time": 1675090458.243436, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Mueller16_ALEdepression", "keywords": ["Unipolar Depression"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#56370470-dc76-11ea-b64b-7cdd908c7490", "@type": "Dataset", "version": "734d1649✂SHA1", "name": "Dataset_omodpirgos_15", "url": "https://example.com/users/omodpirgos/dataset_15", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "i.upirmale@example.com"}], "description": "<this is an autogenerated description for dataset 56370470-dc76-11ea-b64b-7cdd908c7490, since no description was provided by the author, and because google rich-results requires the description-property in schmema.org/Dataset metadatatypes>"}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "Revisited: in Unipolar Brain Activity Neuroimaging of Studies", "datePublished": 2117, "sameAs": "10.100/99.abc-16", "pagination": "1071-1083", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "e.malu@example.com"}, {"@id": "m.emod@example.com"}, {"@id": "m.asan@example.com"}, {"@id": "m.meli@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#JAMA Psychiatry", "@type": "PublicationEvent", "name": "JAMA Psychiatry"}, "isPartOf": {"@id": "#issue(1)", "@type": "PublicationIssue", "issue_number": 1, "isPartOf": {"@id": "#volume(74)", "@type": "PublicationVolume", "volumeNumber": 74}}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#k.umalpalo@example.com", "@type": "Person", "email": "k.umalpalo@example.com", "name": "Kosselo Umalpalo", "givenName": "Kosselo", "familyName": "Umalpalo"}, {"@id": "https://schema.datalad.org/person#m.apalsul@example.com", "@type": "Person", "email": "m.apalsul@example.com", "name": "Dr. Malmelu Apalsul", "givenName": "Malmelu", "familyName": "Apalsul", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#o.selsulmor@example.com", "@type": "Person", "email": "o.selsulmor@example.com", "name": "Dr. Osanmodmelo Selsulmor", "givenName": "Osanmodmelo", "familyName": "Selsulmor", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}, {"@id": "https://schema.datalad.org/person#a.ugorsan@example.com", "@type": "Person", "email": "a.ugorsan@example.com", "name": "Dr. Apirsan Ugorsan", "givenName": "Apirsan", "familyName": "Ugorsan", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#s.omelgorladi@example.com", "@type": "Person", "email": "s.omelgorladi@example.com", "name": "Dr. phil. Sansul Omelgorladi", "givenName": "Sansul", "familyName": "Omelgorladi", "honorificSuffix": "Dr. phil."}]}]}}
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{"type": "dataset", "root_dataset_id": "4fb7be64-dc76-11ea-ad07-7cdd908c7490", "root_dataset_version": "f801161e✂SHA1", "dataset_path": "study-103", "dataset_id": "84e51a9a-dc8b-11ea-ae66-7cdd908c7490", "dataset_version": "b3f02438✂SHA1", "extraction_time": 1675090457.6666787, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "OHBM15", "keywords": ["OHBM 2015"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#4fb7be64-dc76-11ea-ad07-7cdd908c7490", "@type": "Dataset", "version": "f801161e✂SHA1", "name": "Dataset_omodpirgos_64", "url": "https://example.com/users/omodpirgos/dataset_64", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "i.upirmale@example.com"}], "description": "<this is an autogenerated description for dataset 4fb7be64-dc76-11ea-ad07-7cdd908c7490, since no description was provided by the author, and because google rich-results requires the description-property in schmema.org/Dataset metadatatypes>"}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#m.apalsul@example.com", "@type": "Person", "email": "m.apalsul@example.com", "name": "Dr. Malmelu Apalsul", "givenName": "Malmelu", "familyName": "Apalsul", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}]}]}}
{"type": "dataset", "root_dataset_id": "506ce096-dc76-11ea-95f0-7cdd908c7490", "root_dataset_version": "15f405a8✂SHA1", "dataset_path": "study-102", "dataset_id": "84e51a9a-dc8b-11ea-ae66-7cdd908c7490", "dataset_version": "b3f02438✂SHA1", "extraction_time": 1675090457.6932034, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "OHBM_2019", "keywords": ["OHBM 2019"], "accountablePerson": "m.apalsul@example.com", "contributor": [{"@id": "e.omodpirgos@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#506ce096-dc76-11ea-95f0-7cdd908c7490", "@type": "Dataset", "version": "15f405a8✂SHA1", "name": "Dataset_omodpirgos_65", "url": "https://example.com/users/omodpirgos/dataset_65", "author": [{"@id": "e.omodpirgos@example.com"}, {"@id": "i.upirmale@example.com"}], "description": "<this is an autogenerated description for dataset 506ce096-dc76-11ea-95f0-7cdd908c7490, since no description was provided by the author, and because google rich-results requires the description-property in schmema.org/Dataset metadatatypes>"}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#m.apalsul@example.com", "@type": "Person", "email": "m.apalsul@example.com", "name": "Dr. Malmelu Apalsul", "givenName": "Malmelu", "familyName": "Apalsul", "honorificSuffix": "Dr."}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}]}]}}
{"type": "dataset", "root_dataset_id": "5146e8d6-dc76-11ea-a2da-7cdd908c7490", "root_dataset_version": "c0b548cd✂SHA1", "dataset_path": "study-101", "dataset_id": "84e51a9a-dc8b-11ea-ae66-7cdd908c7490", "dataset_version": "b3f02438✂SHA1", "extraction_time": 1675090457.6865904, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Camilleri15_MultiModal", "keywords": ["study"], "accountablePerson": "g.pirpirludu@example.com"}, {"@id": "https://schema.datalad.org/datalad_dataset#5146e8d6-dc76-11ea-a2da-7cdd908c7490", "@type": "Dataset", "version": "c0b548cd✂SHA1", "name": "Dataset_omorpallud_66", "url": "https://example.com/users/omorpallud/dataset_66", "author": [{"@id": "o.omorpallud@example.com"}, {"@id": "s.omelpire@example.com"}], "description": "<this is an autogenerated description for dataset 5146e8d6-dc76-11ea-a2da-7cdd908c7490, since no description was provided by the author, and because google rich-results requires the description-property in schmema.org/Dataset metadatatypes>"}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "trail of of motor speed performance Multi-modal the correlates making imaging", "datePublished": 2168, "accountablePerson": "u.pulpula@example.com", "sameAs": "10.100/99.abc-67", "author": [{"@id": "o.omorpallud@example.com"}, {"@id": "l.imel@example.com"}, {"@id": "e.omodpirgos@example.com"}, {"@id": "i.gorsel@example.com"}, {"@id": "e.pal@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Frontiers in neurology", "@type": "PublicationEvent", "name": "Frontiers in neurology"}, "description": "e.kospulsulu@example.com", "isPartOf": {"@id": "#issue(219)", "@type": "PublicationIssue", "issue_number": 219, "isPartOf": {"@id": "#volume(6)", "@type": "PublicationVolume", "volumeNumber": 6}}, "pagination": "1446-1454"}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#g.pirpirludu@example.com", "@type": "Person", "email": "g.pirpirludu@example.com", "name": "Goso Pirpirludu", "givenName": "Goso", "familyName": "Pirpirludu"}, {"@id": "https://schema.datalad.org/person#u.pulpula@example.com", "@type": "Person", "email": "u.pulpula@example.com", "name": "Upir Pulpula", "givenName": "Upir", "familyName": "Pulpula"}, {"@id": "https://schema.datalad.org/person#i.ugos@example.com", "@type": "Person", "email": "i.ugos@example.com", "name": "Imad Ugos", "givenName": "Imad", "familyName": "Ugos"}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#e.mori@example.com", "@type": "Person", "email": "e.mori@example.com", "name": "Dr. phil. Epuli Mori", "givenName": "Epuli", "familyName": "Mori", "honorificSuffix": "Dr. phil."}, {"@id": "https://schema.datalad.org/person#a.osel@example.com", "@type": "Person", "email": "a.osel@example.com", "name": "Dr. phil. Amellade Osel", "givenName": "Amellade", "familyName": "Osel", "honorificSuffix": "Dr. phil."}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}]}]}}
{"type": "dataset", "root_dataset_id": "52142b84-dc76-11ea-98c5-7cdd908c7490", "root_dataset_version": "244a8ad4✂SHA1", "dataset_path": "study-100", "dataset_id": "84e51a9a-dc8b-11ea-ae66-7cdd908c7490", "dataset_version": "b3f02438✂SHA1", "extraction_time": 1675090457.6464868, "agent_name": "Mad Isanpire", "agent_email": "m.isanpire@example.com", "extractor_name": "metalad_studyminimeta", "extractor_version": "0.1", "extraction_parameter": {}, "extracted_metadata": {"@context": {"@vocab": "http://schema.org/"}, "@graph": [{"@id": "#study", "@type": "CreativeWork", "name": "Camilleri18_eMDN", "keywords": ["neural correlates"], "accountablePerson": "g.pirpirludu@example.com", "contributor": [{"@id": "o.omorpallud@example.com"}]}, {"@id": "https://schema.datalad.org/datalad_dataset#52142b84-dc76-11ea-98c5-7cdd908c7490", "@type": "Dataset", "version": "244a8ad4✂SHA1", "name": "Dataset_omorpallud_69", "url": "https://example.com/users/omorpallud/dataset_69", "author": [{"@id": "o.omorpallud@example.com"}], "description": "<this is an autogenerated description for dataset 52142b84-dc76-11ea-98c5-7cdd908c7490, since no description was provided by the author, and because google rich-results requires the description-property in schmema.org/Dataset metadatatypes>"}, {"@id": "#publicationList", "@list": [{"@id": "#publication[0]", "@type": "ScholarlyArticle", "headline": "multiple-demand Definition extended network and", "datePublished": 2171, "sameAs": "10.100/99.abc-70", "pagination": "1549-1564", "author": [{"@id": "o.omorpallud@example.com"}, {"@id": "e.omodpirgos@example.com"}, {"@id": "m.meli@example.com"}, {"@id": "m.asan@example.com"}, {"@id": "m.kolkospale@example.com"}, {"@id": "m.emodgos@example.com"}, {"@id": "s.ladsela@example.com"}], "publication": {"@id": "https://schema.datalad.org/publication_event#Neuroimage", "@type": "PublicationEvent", "name": "Neuroimage"}, "isPartOf": {"@id": "#volume(165)", "@type": "PublicationVolume", "volumeNumber": 165}}]}, {"@id": "#personList", "@list": [{"@id": "https://schema.datalad.org/person#u.pulpula@example.com", "@type": "Person", "email": "u.pulpula@example.com", "name": "Upir Pulpula", "givenName": "Upir", "familyName": "Pulpula"}, {"@id": "https://schema.datalad.org/person#i.selkolu@example.com", "@type": "Person", "email": "i.selkolu@example.com", "name": "Ipalmad Selkolu", "givenName": "Ipalmad", "familyName": "Selkolu"}, {"@id": "https://schema.datalad.org/person#k.umalpalo@example.com", "@type": "Person", "email": "k.umalpalo@example.com", "name": "Kosselo Umalpalo", "givenName": "Kosselo", "familyName": "Umalpalo"}, {"@id": "https://schema.datalad.org/person#s.omelgorladi@example.com", "@type": "Person", "email": "s.omelgorladi@example.com", "name": "Dr. phil. Sansul Omelgorladi", "givenName": "Sansul", "familyName": "Omelgorladi", "honorificSuffix": "Dr. phil."}, {"@id": "https://schema.datalad.org/person#i.mora@example.com", "@type": "Person", "email": "i.mora@example.com", "name": "Imolpir Mora", "givenName": "Imolpir", "familyName": "Mora"}, {"@id": "https://schema.datalad.org/person#m.imolsel@example.com", "@type": "Person", "email": "m.imolsel@example.com", "name": "Melmodmad Imolsel", "givenName": "Melmodmad", "familyName": "Imolsel"}, {"@id": "https://schema.datalad.org/person#e.irigludkolo@example.com", "@type": "Person", "email": "e.irigludkolo@example.com", "name": "Epalmollud Irigludkolo", "givenName": "Epalmollud", "familyName": "Irigludkolo"}, {"@id": "https://schema.datalad.org/person#g.pirpirludu@example.com", "@type": "Person", "email": "g.pirpirludu@example.com", "name": "Goso Pirpirludu", "givenName": "Goso", "familyName": "Pirpirludu"}]}]}}

This will take a lot longer than the previous command because datalad has to fetch more item from the remote repository. If you use the remote meta-dump option properly, you can quickly examine small subsets of very large metadata repositories.

7.3.6. Using metadata

Now that we know all about metadata and how it is handled by MetaLad, here’s a final note on its utility: Metadata, especially when it originates from different sources and gets harmonized to a single schema, provides the powerful opportunity to aid data discoverability. An example of a good use case for metadata is therefore a search or browsing interface, or data bases, such as data portals and graph query databases. MetaLad-extracted metadata can be used in workflows to generate such interfaces, and a concrete example is the DataLad Catalog, which the next section will introduce. So to aid with the discoverability of data, one could add metadata to DataLad datasets, extract metadata with MetaLad and multiple extractors, translate extracted metadata to the catalog schema, submit it to datalad-catalog in order to generate catalog entries, which can all be browsed in a user friendly web-based interface. Intrigued? Read on to the next section for more information.

7.3.7. Installation

MetaLad is a stand-alone Python package, and can be installed using

pip install datalad-metalad

As with DataLad and other Python packages, you might want to do the installation in a virtual environment.

Footnotes