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24 results for “publication metadata”
Sampling metadata for the publication: "Deep-sea sponge derived environmental DNA analysis reveals demersal fish biodiversity of a remote Arctic ecosystem "
<p>Meta data of sampling location, time and depth of eDNA samples used in the study: "Deep-sea sponge derived environmental DNA analysis reveals demersal fish biodiversity of a remote Arctic ecosystem". As well as taxonomic identification of sponges, their microbial abundance and growth form.</p>
OpenAIRE Covid-19 publications, datasets, software and projects metadata.
<p>This dataset provides access to the metadata records of publications, research data, software and projects that may be relevant to the Corona Virus Disease (COVID-19) fight. The dataset contains the OpenAIRE COVID-19 Gateway records, identified via full-text mining and inference techniques applied to the <a href="https://explore.openaire.eu">OpenAIRE Graph</a>. The OpenAIRE Graph is one of the largest Open Access collections of metadata records and links between publications, datasets, software, projects, funders, and organizations, aggregating 12,000+ scientific data sources world-wide, among which the Covid-19 data sources Zenodo COVID-19 Community, WHO (World Health Organization), BIP! FInder for COVID-19, Protein Data Bank, Dimensions, scienceOpen, and RSNA.</p> <p>The dataset consists of a tar archive containing gzip files with one json per line. Each json is compliant to the schema available at <a href="https://doi.org/10.5281/zenodo.3974226">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.8238913">10.5281/zenodo.8238913</a>.</p> <p> </p>
Datasets from Approximate equality of character strings and its application to record linkage in metadata of scientific publications thesis
<p>The datasets were produced in my thesis project. The thesis (in Czech language) explores the application of approximate string matching in scientific publication record linkage process. An introduction to record matching along with five commonly used metrics for string distance (Levenshtein, Jaro, Jaro-Winkler, Cosine distances and Jaccard coefficient) are provided. These metrics are applied on publication metadata from V3S current research information system of the Czech Technical University in Prague. Based on the findings, optimal thresholds in the F1, F2 and F3-measures are determined for each metric.</p> <p>Thesis citation:<br> DOBIÁŠOVSKÝ, Jan. <em>Approximate equality of character strings and its application to record linkage in metadata of scientific publications</em> [online]. Praha, 2020 [cit. 2020-05-04]. Masters thesis. Charles University. Faculty of Arts. Institute of Information Studies and Librarianship.</p> <p> </p>
Zenodo Public Metadata Records until 17 January 2017
<p>This dataset contains metadata of all publicly available records on Zenodo (n=142,117 data records), downloaded on 17 January 2017. The data was retrieved via the Zenodo OAI-PMH interface using the R package oai (Chamberlain & Bojanowski, 2016).</p> <p>The data is provided in two formats:</p> <ol> <li>The original XML in the format OAI DataCite v3, see <em> zenodo-all-metadata-records_oai-datacite3_2017-01-17.xml</em></li> <li>Converted to a tab-separated file, see <em>zenodo-all-metadata-records_2017-01-17.tsv</em></li> </ol>
Dataset for publication: "Exploring ORCID adoption and metadata presence in Spain's research landscape"
<p>This dataset is for the publication "<em>Exploring ORCID adoption and metadata presence in Spain's research landscape</em>" and is composed of the following files:</p> <ul> <li><strong>norm_aff.tsv</strong> (2795x5) - Curated list of role titles</li> <li><strong>openalex_all_spanish.csv</strong> (811062x4) - List of Spanish authors retrieved from OpenAlex</li> <li><strong>orcid_affiliation.csv</strong> (377377x23) - Activity employment affiliation of Spanish ORCID records</li> <li><strong>orcid_full_maps.csv</strong> (15182292x6)- Topic classification of Spanish ORCID records</li> <li><strong>orcid_metrics.csv</strong> (226512x39) - Metadata availability in Spanish ORCID records</li> </ul>
Metadata and georeferencing of the publications in the academic journal 'L'Espace Géographique' (1972-2020)
<p>The zip file contains two tab-separated files (.tsv):</p> <ul> <li>"netscity_espacegeo.tsv"</li> </ul> <p>29 columns, 1645 lines</p> <p>It contains the metadata of 1451 articles and "positions de recherche" issued in the academic journal 'L'Espace Géographique' between 1972 and 2020. The affiliations of the authors have been geocoded using the web application NETSCITY. This dataset combines the result of the geocoding process (geographical coordinates) for each publication with geographical enrichments (urban areas and countries from which the publications have been authored). Additionnal information are available: titles of the publications, sections of the journal in which the publications were published, publication year, authors names, affiliations.</p> <ul> <li>"metadata.tsv"</li> </ul> <p>7 columns, 29 lines</p> <p>It contains a description for each variable of the dataset: "netscity_espacegeo.tsv"</p> <p> </p>
Statistics and Evaluation Data for Publication "Using Supervised Learning to Classify Metadata of Research Data by Field of Study"
<p>Automated classification of metadata of research data by their discipline(s) of research can be used in scientometric research, by repository service providers, and in the context of research data aggregation services. Openly available metadata of the DataCite index for research data were used to compile a large training and evaluation set comprised of 609,524 records. This publication contains aggregated data for the paper. It also contains the evaluation data of all model/hyper-parameter training and test runs.</p>
Metadata matrix: publication type (papers) by authors, from webQDA.
<p>Part of the thesis of Sonia Verdugo Castro (USAL, Spain). <br> Title of the article: "The gender gap in STEM higher education studies: visualisation of literature".<br> Authors of the publication: Sonia Verdugo-Castro, Mª Cruz Sánchez-Gómez, Alicia García-Holgado, Francisco J. García-Peñalvo</p>
Metadata matrix: publication type by authors, from webQDA.
<p>Part of the thesis of Sonia Verdugo Castro (USAL, Spain). <br> Title of the article: "The gender gap in STEM higher education studies: visualisation of literature".<br> Authors of the publication: Sonia Verdugo-Castro, Mª Cruz Sánchez-Gómez, Alicia García-Holgado, Francisco J. García-Peñalvo</p>
Dataset: The availability and completeness of open funder metadata - Case study for publications funded by the Dutch Research Council
<p>Data and code belonging to the manuscript: <strong>The availability and completeness of open funder metadata - Case study for publications funded by the Dutch Research Council </strong></p> <p><strong>Abstract:</strong><br> Research funders spend considerable efforts collecting information on outcomes of the research they fund. To help funders track publication output associated with their funding, Crossref initiated FundRef in 2013, enabling publishers to register funding information using persistent identifiers. However, it is hard to assess the coverage of funder metadata because it is unknown how many articles are the result of funded research and therefore should include funder metadata. </p> <p>In this paper we looked at 5,004 publications reported by researchers to be the result of funding by a specific funding agency: the Dutch Research Council NWO. Only 67% of these articles contain funding information in Crossref, with a subset acknowledging NWO as funder name and/or Funder IDs linked to NWO (53% and 45%, respectively). </p> <p>Web of Science (WoS), Scopus and Dimensions are all able to infer additional funding information from funding statements in the full text of the articles. Funding information in Lens largely corresponds to that in Crossref, with some additional funding information likely taken from PubMed. </p> <p>We observe interesting differences between publishers in the coverage and completeness of funding metadata in Crossref compared to proprietary databases, highlighting potential to increase the quality of open metadata on funding. </p> <p><strong>This dataset contains the following files:</strong></p> <ul> <li><strong>DOIs_unique_CR_Lens_Wos_Scopus_Dim.csv</strong><em> - </em>Dataset of unique DOIs (n= 5,004) with collected information from Crossref and presence/absence of funder information in Lens, Web of Science, Scopus and Dimensions </li> <li><strong>NWO_funder_names_Crossref.txt</strong><em> </em>- List of funder name variants for NWO found in Crossref</li> <li><strong>Google_Apps_Script.js</strong> - Google Apps Script for retrieving information from Crossref and processing Dimensions results </li> <li><strong>DOI_cleaning.R</strong><em> - </em>R script for cleaning DOIs</li> </ul>
ArXiv OAI-PMH arXivRaw publication metadata
<p>This dataset contains <a href="https://www.openarchives.org/pmh/">OAI-PMH</a> metadata for all <a href="https://arxiv.org/">ArXiv</a> publications up until 2024-04-23 in the <a href="http://arxiv.org/OAI/arXivRaw.xsd"><em>arXivRaw</em> XML format</a>.</p> <p>The metadata has been harvested using the <a href="https://github.com/miku/metha/releases/tag/v0.3.3"><em>metha</em> Go package v0.3.3</a> [1] on <a href="https://go.dev/"><em>go1.18</em></a>. Specifically, harvesting was run on a small HPC cluster using the following <a href="https://slurm.schedmd.com">SLURM</a> script. The script had to be scheduled twice due to the connection being reset by the peer (see <em>combined-slurm.out</em>). <em>metha</em> caters for these situations and is able to pick up where it left off with cumulative harvesting.</p> <pre><code>#!/bin/bash #SBATCH --job-name=metha #SBATCH --nodes=1 #SBATCH --cpus-per-task=1 #SBATCH --ntasks=1 #SBATCH --time=10-20:00:00 module purge echo "Installing Go module." module add go/go-1.18/go-1.18-gcc-9.4.0-okbjyoy echo "Installed Go module: $(go version)." echo "Installing metha." go install -v github.com/miku/metha/cmd/...@latest echo "Installed metha: $(<retracted>/go/bin/metha-sync -v)" echo "Harvesting ArXiv OAI-PMH metadata in format 'arXivRaw' from http://export.arxiv.org/oai2." <retracted>/go/bin/metha-sync -T 5m -base-dir /scratch/<retracted>/arxiv -format "arXivRaw" http://export.arxiv.org/oai2 # For the second run, '-from' was specified to pick up the harvest where it was left off. # <retracted>/go/bin/metha-sync -from 2020-09-29 -T 5m -base-dir /scratch/<retracted>/arxiv -format "arXivRaw" http://export.arxiv.org/oai2 echo "Done." exit 0</code></pre> <h2>Dataset contents</h2> <p>This deposit of the dataset contains the following files:</p> <ul> <li><strong>metha-output-OAI-PMH-arXivRaw-until-2024-03-24.tar.gz</strong>: an archive file containing the archive files (gzipped, <em>*.xml.gz</em>) produced by <em>metha</em>, which in turn contain the XML metadata files. The gzipped files contained in the archive are named following the pattern <em>YYYY-MM-DD-<8-digit zero-padded 0-index file count>.xml.gz</em>, e.g., <em>2024-03-24-00000001.xml.gz</em>.</li> <li><strong>README.md</strong>: This file, containing basic information about the dataset and deposit.</li> <li><strong>combined-slurm.out</strong>: The combined SLURM log for the two consecutive SLURM runs that have produced the dataset. Run-specific information has been retracted.</li> </ul> <h2>Reproducibility</h2> <p>As the OAI-PMH metadata is not static but may change at any time, this dataset isn't fully reproducible. However, running the same <em>metha</em> version on the same <em>go</em> version with the same commands should yield very similar results, but will contain newer metadata.</p> <h2>Licenses</h2> <ul> <li>All ArXiv OAI-PMH metadata is <a href="https://info.arxiv.org/help/license/index.html#metadata-license">licensed</a> under <a href="https://creativecommons.org/publicdomain/zero/1.0/deed">CC0-1.0</a>.</li> <li>combined-slurm.out is licensed under <a href="https://creativecommons.org/publicdomain/zero/1.0/deed">CC0-1.0</a>.</li> <li>README.md is licensed under <a href="https://creativecommons.org/publicdomain/zero/1.0/deed">CC0-1.0</a>.</li> </ul> <p>Licenses are documented in a machine-readble manner following the <a href="https://reuse.software/spec/">REUSE 3.0 Specification</a>. License deeds are included in this deposit as <em>.txt</em> files named using the respective SPDX license identifiers.</p> <p>[1] Martin Czygan, Thomas Gersch, ACz-UniBi, Justin Kelly, Gunnar Þór Magnússon, dvglc, & Natanael Arndt. (2024). miku/metha: v0.3.3 (v0.3.3). Zenodo. doi:<a href="https://doi.org/10.5281/zenodo.10940212">10.5281/zenodo.10940212</a>.</p>
Harmonized metadata of Hexa-X-II enablers of Hexa-X-II D2.3 public deliverable
<p>Harmonized metadata of Hexa-X-II enablers of Hexa-X-II D2.3 public deliverable, used in the enabler selection methodology used in the chapter 7 of the deliverable - based on enebaler data collected from the contributors of Hexa-X-II project.</p>
IMPULSE: Integrate Public Metadata Underneath professional Library SErvices
<p>This repository contains the results of our experimental evaluation conducted with IMPULSE (<a href="https://github.com/t-blume/impulse">https://github.com/t-blume/impulse</a>). The datasets are subsets of the Billion Triple Challenge Dataset 2014 (<a href="http://km.aifb.kit.edu/projects/btc-2014/">http://km.aifb.kit.edu/projects/btc-2014/</a>) containing only bibliographic metadata.</p>
l-sized Training and Evaluation Data for Publication "Using Supervised Learning to Classify Metadata of Research Data by Field of Study"
<p>Automated classification of metadata of research data by their discipline(s) of research can be used in scientometric research, by repository service providers, and in the context of research data aggregation services. Openly available metadata of the DataCite index for research data were used to compile a large training and evaluation set comprised of 609,524 records. This is the cleaned and vectorized version with a feature selection of large size.</p>
s-sized Training and Evaluation Data for Publication "Using Supervised Learning to Classify Metadata of Research Data by Field of Study"
<p>Automated classification of metadata of research data by their discipline(s) of research can be used in scientometric research, by repository service providers, and in the context of research data aggregation services. Openly available metadata of the DataCite index for research data were used to compile a large training and evaluation set comprised of 609,524 records. This is the cleaned and vectorized version with a feature selection of small size.</p>
The Metadata of "A Rapid Investigation of Artificial Intelligence Generated Content Footprints in Scholarly Publications"
<p>This resource contains all the raw data associated with the article "A Rapid Investigation of Artificial Intelligence Generated Content Footprints in Scholarly Publications." This raw data underpins the conclusions presented in the article.</p>
This dataset contains the measurement data and its metadata of the publication "Out-of-plane corrugations in graphene based van der Waals heterostructures".
<p>This dataset contains the measurement data and its metadata of the publication "Out-of-plane corrugations in graphene based van der Waals heterostructures".</p>
Data, Metadata, R-codes and R data files for publication "Comparative ungulate diversity and biomass change with human use and drought: implications for community stability and protected area prioritization in African savannas" by Bartzke et al. in Ecology and Evolution
<p>These files contain data and metadata for modeling ungulate diversity and biomass in the Maasai Mara ecosystem in Kenya in the drought year of 1999 and a year with normal rainfall, 2002. The files also contain R codes and R data files.</p> <p>Metadata.pdf: Metadata for files "mc_333m.csv" and "mc_1km.csv"</p> <p>mc_333m.csv: A data file for 333-meter-by-333-meter sub-blocks.</p> <p>prepare_data.r: R code to impute missing vegetation records in 333-meter-by-333-meter subblocks and summarize the data over 1-kilometer-by-1-kilometer blocks for analysis.</p> <p>krige_vegetation.RData: An R data file containing the imputed vegetation records.</p> <p>mc_1km.csv: A data file for 1-kilometer-by-1-kilometer blocks for analysis.</p> <p>mc_1km.r: R code for modeling ungulate diversity and biomass; mc_1km_mod.RData: An R data file for loading the ungulate diversity and biomass models.</p> <p>mc_1km.RData: An R data file containing model predictions of ungulate diversity and biomass.</p> <p>mc_1km_plots.r: R code for plotting model predictions of ungulate diversity and biomass.</p> <p>MMNR_boundary.shp: A shapefile of the Maasai Mara National Reserve boundary in Kenya and associated files. These files are used for plotting the predictions of ungulate diversity and biomass.</p> <p>MMNR_border.zip: A shapefile and associated files for the Maasai Mara National Reserve border with Tanzania. These files are also used for plotting predictions of ungulate diversity and biomass.</p>
GitHub Public Repository Metadata
<p>Metadata (i.e. no code) of all public repositories with 5+ stars on GitHub</p><p> </p><p>This dataset is obtained from the Github API and contains only public repository-level metadata.<br>It may be useful for anyone interested in studying the Github ecosystem. It contains approximately 3.1 million entries.</p><p>The Github API Terms of Service apply.</p><blockquote><p>You may not use this dataset for spamming purposes, including for the purposes of selling GitHub users' personal information, such as to recruiters, headhunters, and job boards.</p></blockquote><p><br>The data format is a JSON array of entries, an example of which is given below.</p><p><i>Redacted for anonymity.</i></p>
Metadata of sampling strategy during metabarcoding analysis (Journal publication supplement)
<p>The table presents supplementary materials and contains metadata of sampling strategy during metabarcoding analysis of four types of substrates in two ombrotrophic bog habitats, with other experimental and environmental parameters included in analyses. </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.