These data contain aggregated survey responses assessing the quality and completeness of metadata for datasets deposited in public repositories and for the same datasets after professional curation.Responses were provided by 10 professional editors representing life, social and physical sciences. Each were randomly assigned four datasets to assess, half (20) of which had been curated according to the standards of Springer Nature's Research Data Support service and half (20) which had not.Curated datasets were shared privately with research participants. The versions that did not receive curation via Springer Nature's Research Data Support are openly accessible.Single-blind testing was employed; the researchers were not made aware which datasets had been curated and which had not, and it was ensured that no participant assessed the same dataset before and after curation. Responses were collected via an online survey. The relevant question and scoring is provided below:Rate the overall quality and completeness of the metadata for the dataset (with regards to finding and accessing and citing the data, not reusing the data)1 = not complete, 5 = very complete

The aim of the Data Rescue & Curation Best Practices Guide is to provide an accessible and hands-on approach to handling data rescue and digital curation of at-risk data for use in secondary research. We provide a set of examples and workflows for addressing common challenges with social science survey data that can be applied to other social and behavioural research data. The goal of this guide and set of workflows presented is to improve librarians’ and data curators’ skills in providing access to high-quality, well-documented, and reusable research data. The aspects of data curation that are addressed throughout this guide are adopted from long-standing data library and archiving practices, including: documenting data using standard metadata, file and data organization; using open and software-agnostic formats; and curating research data for reuse.

Data related to our FAIR-BioRS manuscript. More details are available at the associated GitHub repository: https://github.com/FAIR-BioRS/Data.

The Research Software workshop, was a co-located event with the RDA plenary. Organized by the FAIR-IMPACT European project, with the contribution of the FAIRCORE4EOSC European project. The workshop goals were: Identifying what we have - what is the current landscape of guidelines and metrics for Research Software; Discussing what we need - what are the needs that can be provided by infrastructures and research support staff; Planning how we create adapted guidelines and metrics to the current reality and how we improve the scholarly ecosystem. During the workshop we received valuable feedback which will impact the expected deliverables by the two European projects: FAIR-IMPACT: D4.4 - Guidelines for recommended metadata standard for research software within EOSC FAIR-IMPACT: D5.2 - Metrics for automated FAIR software assessment in a disciplinary context We strive that this process will be as transparent as possible and will yield a result which will be useful for the community and for the service providers that handle research software or act in the scholarly ecosystem, this is why the full planning of the workshop is now made available on Zenodo. We invited the participants to prepare for the session by reviewing the following publications: the SIRS report recommendations, the FAIR4RS principles the CodeMeta initiative vocabulary The authors of this repository are the organizing committee of the workshop and the contributors in this repository are the participants of the workshop onsite and online.

This dataset contains a standard template for representing the metadata of rock specimens (e.g., core, microanalysis, hand grab) in the CSIRO Mineral Resources Discovery program. The template includes core properties of samples such as their name, identifier, type, and location, as well as associated metadata such as project, drilling contexts, hazard declaration and physical storage. The template will be used to catalogue legacy and specimens systematically collected through mineral exploration projects. It has been developed iteratively, revised, and improved based on feedback from researchers and lab technicians. This standardized template can prevent duplicate sample metadata entry and lower metadata redundancy, thereby improving the program's physical sample curation and discovery. Lineage: The template includes a readme section summarising all the metadata fields, including their requirements and definitions. The template incorporates several established controlled terms representing, e.g., sample type, rock type, drill type, EPSG and hazard information to ensure consistency in metadata entry.

Additional file 1. Supplementary Table 1. Comparison of different omics data tools.

This dataset contains key characteristics about the data described in the Data Descriptor Data-driven curation process for describing the blood glucose management in the intensive care unit. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format

This dataset, compiled by the German Kompetenznetzwerk Bibliometrie, provides access to curated bibliometric data in OpenAlex focussing on the German research landscape.

Curated data is provided for following entities:

- Address information
- Publishers
- Funding information
- Document types
- Transformative agreements
- Authors (tba)

For an overview about the tables included, see data-overview.md

This release is based on the August 2024 snapshot of OpenAlex. The OPENBIB snapshot is offered in both CSV and JSONL format.

This is a initial release to demonstrate the current state of metadata curation. The aim is to continue these efforts and improve the curation together with the community and data providers.

Data is made available under the CC0 license.

Github repository: https://github.com/kbopenbib/kbopenbib_data/

The COVID-19 CDCS represents a metadata repository that provides a catalog of COVID-19 related research literature and data.

Identifiers of many kinds are the key to creating unambiguous and persistent connections between research objects and other items in the global research infrastructure (GRI). Many repositories are implementing mechanisms to collect and integrate these identifiers into their submission and record curation processes. This bodes well for a well-connected future, but many existing resources submitted in the past are missing these identifiers, thus missing the connections required for inclusion in the connected infrastructure. Re-curation of these metadata is required to make these connections. The Dryad Data Repository has existed since 2008 and has successfully re-curated the repository metadata several times, adding identifiers for research organizations, funders, and researchers. Understanding and quantifying these successes depends on measuring repository and identifier connectivity. Metrics are described and applied to the entire repository here. Identifiers for papers (DOIs) connected to datasets in Dryad have long been a critical part of the Dryad metadata creation and curation processes. Since 2019, the % of datasets with connected papers has decreased from 100% to less than 40%. This decrease has significant ramifications for the re-curation efforts described above as connected papers are an important source of metadata. In addition, missing connections to papers make understanding and re-using datasets more difficult. Connections between datasets and papers are many times difficult to make because of time lags between submission and publication, lack of clear mechanisms for citing datasets and other research objects from papers, changing focus of researchers, and other obstacles. The Dryad community of members, i.e. users, research institutions, publishers, and funders have vested interests in identifying these connections and critical roles in the curation and re-curation efforts. Their engagement will be critical in building on the successes Dryad has already achieved and ensuring sustainable connectivity in the future. Methods These data are Dryad metadata retrieved from https://datadryad.org and translated into csv files. There are two datasets: 1. DryadJournalDataset was retrieved from Dryad using the ISSNs in the file DryadJournalDataset_ISSNs.txt, although some had no data. 2. DryadOrganizationDataset was retrieved from Dryad using the RORs in the file DryadOrganizationDataset_RORs.txt, although some had no data. Each dataset includes four types of metadata: identifiers, funders, keywords, and related works, each in a separate comma (.csv) or tab (.tsv) delimited files. There are also Microsoft Excel files (.xlsx) for the identifier metadata and connectivity summaries for each dataset (*.html). The connectivity summaries include summaries of each parameter in all four data files with definitions, counts, unique counts, most frequent values, and completeness. These data formed the basis for an analysis of the connectivity of the Dryad repository for organizations, funders, and people.

Harvester-Curator is a tool, designed to elevate metadata provision in data repositories. In the first phase, Harvester-Curator acts as a scanner, navigating through user code and/or data repositories to identify suitable parsers for different file types. It collects metadata from each of the files by applying corresponding parsers and then compiles this information into a structured JSON file, providing researchers with a seamless and automated solution for metadata collection. Moving to the second phase, Harvester-Curator transforms into a curator, leveraging the harvested metadata to populate metadata fields in a target repository. By automating this process, it not only relieves researchers of the manual burden but also ensures the accuracy and comprehensiveness of the metadata. Beyond its role in streamlining the intricate task of metadata collection, this tool contributes to the broader objective of elevating data accessibility and interoperability within repositories.

This object has been created as a part of the web harvesting project of the Eötvös Loránd University Department of Digital Humanities ELTE DH. Learn more about the workflow HERE about the software used HERE.The aim of the project is to make online news articles and their metadata suitable for research purposes. The archiving workflow is designed to prevent modification or manipulation of the downloaded content. The current version of the curated content with normalized formatting in standard TEI XML format with Schema.org encoded metadata is available HERE. The detailed description of the raw content is the following:

The portal's archived content (from 2000-09-10 to 2021-10-16) in WARC format available HERE (crawled: 2021-09-02T19:51:04.673039 - 2021-10-16T16:25:30.654675).Please fill in the following form before requesting access to this dataset: ACCESS FORM

This dataset contains a standard template for representing the metadata of mineral spectral reference specimens in the CSIRO Mineral Resources Discovery program. The template includes core properties of samples such as their name, identifier, type, and location, as well as associated metadata such as project, hazard declaration and physical storage. The template will be used to catalogue reference samples used for mineral spectral analysis (NVCL). It has been developed iteratively, revised, and improved based on feedback from researchers and lab technicians. This standardized template can prevent duplicate sample metadata entry and lower metadata redundancy, thereby improving the program's physical sample curation and discovery. Lineage: This template was built on the CMR rock metadata template (https://doi.org/10.25919/2prf-dk88). The template includes a readme section summarising all the metadata fields, including their requirements and definitions. The template incorporates several established controlled terms representing, e.g., sample type, mineral type, EPSG and hazard information to ensure consistency in metadata entry. The template also contains few metadata fields that are specific to mineral spectra samples like different analysis conducted for the samples (XRD, Whole-rock geochemical analysis, etc).

This is the supplementary material accompanying the manuscript "Daily life in the Open Biologist’s second job, as a Data Curator", published in Wellcome Open Research.

It contains:

- Python_scripts.zip: Python scripts used for data cleaning and organization:

-add_headers.py: adds specified headers automatically to a list of csv files, creating new output files containing a "_with_headers" suffix.

-count_NaN_values.py: counts the total number of rows containing null values in a csv file and prints the location of null values in the (row, column) format.

-remove_rowsNaN_file.py: removes rows containing null values in a single csv file and saves the modified file with a "_dropNaN" suffix.

-remove_rowsNaN_list.py: removes rows containing null values in list of csv files and saves the modified files with a "_dropNaN" suffix.

- README_template.txt: a template for a README file to be used to describe and accompany a dataset.

- template_for_source_data_information.xlsx: a spreadsheet to help manuscript authors to keep track of data used for each figure (e.g., information about data location and links to dataset description).

- Supplementary_Figure_1.tif: Example of a dataset shared by us on Zenodo. The elements that make the dataset FAIR are indicated by the respective letters. Findability (F) is achieved by the dataset unique and persistent identifier (DOI), as well as by the related identifiers for the publication and dataset on GitHub. Additionally, the dataset is described with rich metadata, (e.g., keywords). Accessibility (A) is achieved by the ease of visualization and downloading using a standardised communications protocol (https). Also, the metadata are publicly accessible and licensed under the public domain. Interoperability (I) is achieved by the open formats used (CSV; R), and metadata are harvestable using the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH), a low-barrier mechanism for repository interoperability. Reusability (R) is achieved by the complete description of the data with metadata in README files and links to the related publication (which contains more detailed information, as well as links to protocols on protocols.io). The dataset has a clear and accessible data usage license (CC-BY 4.0).

A curated dataset of XRefs extracted from agri-food ontologies and curated using OMHT (Ontology Mapping Harvester Tool), which is a script in Java language designed to automatically extract and semi-automatically curate declared mappings from ontologies and reify them into specific objects with metadata and provenance information.

The Lithuanian Data Archive for Social Sciences and Humanities (LiDA) developed a framework for harvesting and delivering rich metadata of social science data (SSD) objects for the EOSC Portal Service Catalogue, with the aim of meeting the FAIR F2 principle of providing rich metadata in a machine-readable format. The project identified and recommended the vocabulary for standardized description of major data types for SSD objects, which allows for more detailed descriptions of data and increases possibilities for secondary analysis on generic data portals. The framework is extensible to other disciplines, which benefits developers of software, infrastructures, and data curation standards in other communities. Scientists and government bodies could benefit from the resources produced during the project, such as the recommendations and milestones, which may help them develop their own data curation software, data repositories, and controlled vocabularies. The project’s impact lies in benefiting the wider research community by enabling better data discovery, reuse, and interoperability in the EOSC Portal Service Catalogue.

This object has been created as a part of the web harvesting project of the Eötvös Loránd University Department of Digital Humanities ELTE DH. Learn more about the workflow HERE about the software used HERE.The aim of the project is to make online news articles and their metadata suitable for research purposes. The archiving workflow is designed to prevent modification or manipulation of the downloaded content. The current version of the curated content with normalized formatting in standard TEI XML format with Schema.org encoded metadata is available HERE. The detailed description of the raw content is the following:

• The portal's archived content (from 2017-01-29 to 2021-05-21) in WARC format available HERE (crawled: 2021-05-21T09:51:12.531750 - 2021-05-21T18:38:24.961226).

This object has been created as a part of the web harvesting project of the Eötvös Loránd University Department of Digital Humanities ELTE DH. Learn more about the workflow HERE about the software used HERE.The aim of the project is to make online news articles and their metadata suitable for research purposes. The archiving workflow is designed to prevent modification or manipulation of the downloaded content. The current version of the curated content with normalized formatting in standard TEI XML format with Schema.org encoded metadata is available HERE. The detailed description of the raw content is the following:

• The portal's archived content (from 2001-01-01 to 2021-05-22) in WARC format available HERE (crawled: 2021-05-21T10:01:38.592950 - 2021-05-22T20:50:22.079445).

This is a manually curated dataset of metadata downloaded off GenBank. The metadata is information pertaining to plant-associated fungal communities from marker gene sequences.

Curated list of new and notable datasets, visualizations etc.