Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
306
datasets available to search
ShareScore release 0.7.1
Dataset results
306 results for “data archive”
Reducing the structure bias of RNA-Seq reveals a large number of non-annotated non-coding RNA Data Archive
<p>[This repository contains the source data for the workflow presented in the manuscript "<strong>Reducing the structure bias of RNA-Seq reveals a large number of non-annotated non-coding RNA</strong>". The workflow can be found here: http://gitlabscottgroup.med.usherbrooke.ca/gaspard/snakemake_blockbuster ]</p> <p>The study of RNA expression is the fastest growing area of genomic research. However, despite the dramatic increase in the number of sequenced transcriptomes, we still do not have accurate estimates of the number and expression levels of non-coding RNA genes. Non-coding transcripts are often overlooked due to incomplete genome annotation. In this study, we use annotation-independent detection of RNA reads generated using a reverse transcriptase with low structure bias to identify non-coding RNA. Transcripts between 20 and 500 nucleotides were filtered and crosschecked with non-coding RNA annotations revealing 115 non-annotated non-coding RNAs expressed in different cell lines and tissues. Inspecting the sequence and structural features of these transcripts indicated that 60% of these transcripts correspond to new tRNA and snoRNA genes. The identified genes exhibited features of their respective families in terms of structure, expression, conservation and response to depletion of interacting proteins. Together, our data reveal a new group of RNA that are difficult to detect using standard gene prediction and RNA sequencing techniques, suggesting that reliance on actual gene annotation and sequencing techniques distort the perceived architecture of the human transcriptome.</p>
Data archive for Towards understanding potential atmospheric contributions to abrupt climate changes: characterizing changes to the North Atlantic eddy-driven jet over the last deglaciation
<p>This archive contains simulated North Atlantic eddy-driven jet latitude and tilt data generated using the PlaSim model to accompany Andres and Tarasov (Climate of the Past, accepted), DOI: https://doi.org/10.5194/cp-15-1-2019.</p> <p>Paper abstract is as follows:</p> <p>"Abrupt climate shifts of large amplitudes were common features of the Earth’s climate as it transitioned into and out of the last full glacial state approximately 20 000<br> years ago, but their causes are not yet established. Midlatitude atmospheric dynamics may have played an important role in these climate variations through their effects on heat and precipitation distributions, sea ice extent, and wind-driven ocean circulation patterns. This study characterizes deglacial winter wind changes over the North Atlantic (NAtl) in a suite of transient deglacial simulations using the PlaSim Earth system model (run at T42 resolution) and the TraCE-<br> 21ka (T31) simulation. Though driven with yearly updates in surface elevation, we detect multiple instances of NAtl jet transitions in the PlaSim simulations that occur within 10<br> simulation years and a sensitivity of the jet to background climate conditions. Thus, we suggest that changes to the NAtl jet may play an important role in abrupt glacial climate changes.</p> <p>We identify two types of simulated wind changes over the last deglaciation. Firstly, the latitude of the NAtl eddy-driven jet shifts northward over the deglaciation in a sequence of distinct steps. Secondly, the variability in the NAtl jet gradually shifts from a Last Glacial Maximum (LGM) state with a strongly preferred jet latitude and a restricted latitudinal range to one with no single preferred latitude and a range that is at least 11 ◦ broader. These changes can significantly affect ocean circulation. Changes to the position of the NAtl jet alter the location of the wind forcing driving oceanic surface gyres and the limits of sea ice extent, whereas a shift to a more variable jet reduces the effectiveness of the wind forcing at driving surface ocean transports.</p> <p>The processes controlling these two types of changes differ on the upstream and downstream ends of the NAtl eddy-driven jet. On the upstream side over eastern North America, the elevated ice sheet margin acts as a barrier to the winds in both the PlaSim simulations and the TraCE-21ka experiment. This constrains both the position and the latitudinal variability in the jet at LGM, so the jet shifts in sync with ice sheet margin changes. In contrast, the downstream side over the eastern NAtl is more sensitive to the thermal state of the background climate. Our results suggest that the presence of an elevated ice sheet margin in the south-eastern sector of the North American ice complex strongly constrains the deglacial position of the jet over eastern North America and the western North Atlantic as well as its variability."</p> <p> </p>
Data - Mentions and names of God in the Archives Parlementaires (French Revolution)
<p>This graphs are the result of a distant reading of the Archives Parlementaires (AP), the edition of discourses (and documents related to discourses) pronounced in the French national assemblies during the French Revolution. We made a list of names of God and searched for them. We classified the results also according to time. We can get an idea of the changes in the frequency of mentions of God, and in the names used to speak of God.</p> <p>Yet it must also be mentioned that we may not have identified all possible mentions of God, for two reasons: 1) God can be referred to implicitely or through rarer expressions; 2) there are OCR errors. </p> <p>The actual figures should be thus significantly higher. In the framework of our project, we did not intend to obtain statistical precision. It was enough for us to show that mentions of God played an important role in the political culture of the French Revolution.</p> <p> </p>
E-learning Data Archive
<p>Archival bundle of Bangladeshi University students e-learning readiness and perceived stress data. </p>
Data and code archive for Andrews et al. 'Exposure to food insecurity increases energy storage and reduces somatic maintenance in European starlings'
<p>Data and code archive for Andrews et al. 'Exposure to food insecurity increases energy storage and reduces somatic maintenance in European starlings'</p> <p>The archive contains one R script and three .Rdata data files, relating to the mass data, the telomere data, and the food consumption data respectively.</p>
Data Archive for: Hurricane Laura (2020): A Comparison of Drop Size Distribution Moments Using Ground and Radar Remote Sensing Retrieval Methods
<p>This archive corresponds to the data described in Brauer et al. (2021) to be published in <em>Journal of Geophysical Research: Atmospheres.</em> Please see the included readme.txt file for details about each data file.</p>
Gene Ontology Data Archive
<p>Archival bundle of GO data release.</p>
tellingsounds/lama-data: LAMA Data - Capturing Entities (Persons, Topics, Music, etc.) in Austrian audio(-visual) archive material
<p>Data entered into LAMA (Linked Annotations for Media Analysis), a research software for capturing and visualizing the interaction of music and its contexts, developed by the Telling Sounds project.</p>
Archive of the microtremor data observed at rock/stiff-soil sites and the analysis results
<p>This archive includes the microtremor data observed at 15 rock/stiff-soil sites and the analysis results, which were fully described in a paper "Spatial autocorrelation method for a simple microtremor array survey at rock/stiff-soil sites" by Ikuo Cho (2023, Geophysical Journal International, in press).</p>
Model data archive for a model-data intercomparison of the Eocene-Oligocene transition
<p>This data package contains data used for an model-data intercomparison originally<br> published in:</p> <p>D. K. Hutchinson, H. K. Coxall, D. J. Lunt, M. Steinthorsdottir, A. M. de Boer, M. Baatsen, A. von der Heydt, M. Huber, A. T. Kennedy-Asser, L. Kunzmann, J.-B. Ladant, C. H. Lear, K. Moraweck, P. N. Pearson, E. Piga, M. J. Pound, U. Salzmann, H. D. Scher, W. P. Sijp, K. K. Śliwińska, P. A. Wilson, and Z. Zhang, 2021: <strong>The Eocene-Oligocene transition: a review of marine and terrestrial proxy data, models and model-data comparisons</strong>, Climate of the Past, 17, 269-315.<br> <a href="https://doi.org/10.5194/cp-17-269-2021">https://doi.org/10.5194/cp-17-269-2021</a></p> <p>These data are also used in a further model-data intercomparison of Antarctic temperatures:</p> <p>Emily Tibbett, Natalie J Burls, David K. Hutchinson, Sarah J Feakins, (2023), <strong>Proxy-Model Comparison for the Eocene-Oligocene Transition in Southern High Latitudes, Paleoceanography and Paleocliamtology</strong>, In Review. Pre-print avaiable from:<br> <a href="https://www.authorea.com/doi/full/10.1002/essoar.10511735.2">https://www.authorea.com/doi/full/10.1002/essoar.10511735.2</a></p> <p>The package contains surface air temperature and sea surface temperature from an ensemble of model simulations of the Eocene-Oligocene transition. These data are provided at annual and monthly frequency. They are also provided on the original model grid, and an interpolated common grid used for the intercomparison. (The common grid is based on the HadCM3BL model grid.) All data are provided in NETCDF format with self-describing variable names.</p> <p>The name and explanation of the interpolated data files are contained in:<br> <strong>table_of_experiments.xlsx</strong></p> <p>Please read that spreadsheet to interpret the filenames, and see <strong>Table 2 (p291)</strong> of Hutchinson et al (2021) for experiment descriptions.</p> <p>Please also be mindful to cite the original authors of the simulations when using these data, whose work made this dataset possible. The appropriate citations are listed below:</p> <p>Reference DOI link <br> <strong>Baatsen et al (2020)</strong> <a href="https://doi.org/10.5194/cp-16-2573-2020">https://doi.org/10.5194/cp-16-2573-2020 </a> <br> <strong>Goldner et al (2014)</strong> <a href="https://doi.org/10.1038/nature13597">https://doi.org/10.1038/nature13597</a> <br> <strong>Ladant et al (2014a,b)</strong> <a href="https://doi.org/10.5194/cp-10-1957-2014">https://doi.org/10.5194/cp-10-1957-2014</a> <a href="https://doi.org/10.1002/2013PA002593 ">https://doi.org/10.1002/2013PA002593 </a><br> <strong>Hutchinson et al (2018, 2019) </strong><a href="https://doi.org/10.5194/cp-14-789-2018">https://doi.org/10.5194/cp-14-789-2018</a> <a href="https://doi.org/10.1038/s41467-019-11828-z">https://doi.org/10.1038/s41467-019-11828-z</a> <br> <strong>Kennedy et al (2015)</strong> <a href="https://doi.org/10.1098/rsta.2014.0419">https://doi.org/10.1098/rsta.2014.0419</a> <br> <strong>Zhang et al (2012, 2014)</strong> <a href="https://doi.org/10.5194/gmd-5-523-2012">https://doi.org/10.5194/gmd-5-523-2012</a> <a href="https://doi.org/10.1038/nature13705">https://doi.org/10.1038/nature13705</a> <br> <strong>Sijp et al (2009)</strong> <a href="https://doi.org/10.1175/2009JCLI3003.1">https://doi.org/10.1175/2009JCLI3003.1</a></p>
Factors Affecting African Journals Visibility Data Archive
<p>The file contains the status of African journals indexing on Google Scholar and Scopus. The journals are hosted in a major journal repository in Africa(Sabinet journal repository) and a major journal platform for African journals (African Journal Online, AJOL). There are also factors affecting the journal indexing on Google Scholar and Scopus. </p> <p>The factors affecting journal visibility highlighted include journals' open access status, journals’ presence on the International Standard Serial Number (ISSN), journals’ publisher membership of the Committee on Publication Ethics (COPE), journals’ hosting on the International Network for Advancing Science and Policy (INASP) journal online, geographic location of the journals’ publisher. </p>
Bionomia: a versioned archive of associations between people and the biodiversity data records they worked on. hash://sha256/4b192ed16cfe8577c2e275ada76bfdc19fe5a5381547139c8f8f4079e704b6f2 hash://md5/a69075f7a9a19f069c6d0c6d8f312259
<p>Biodiversity data describe nature in digital form. Bionomia [1] helps to associate the people behind the creation of biodiversity data and the records they worked on. Bionomia is updated constantly, and this publication provides a versioned snapshots of Bionomia data products, such as:</p> <p>https://bionomia.net/data/bionomia-public-profiles.csv</p> <p>and </p> <p>https://bionomia.net/data/bionomia-public-claims.csv.gz</p> <p>The data downloads are versioned using Preston [2], a biodiversity data tracker, by running the following command:</p> <pre><code class="language-bash">preston track\ https://bionomia.net/data/bionomia-public-claims.csv.gz\ https://bionomia.net/data/bionomia-public-profiles.csv</code></pre> <p>The history of this publication is:</p> <pre><code><hash://sha256/4b192ed16cfe8577c2e275ada76bfdc19fe5a5381547139c8f8f4079e704b6f2> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/22afc7a3e4e1c3bc289ce39573463331d3b594a11512c1233e39436973aea974> . <urn:uuid:0659a54f-b713-4f86-a917-5be166a14110> <http://purl.org/pav/hasVersion> <hash://sha256/22afc7a3e4e1c3bc289ce39573463331d3b594a11512c1233e39436973aea974> .</code></pre> <p>as obtained via </p> <pre><code class="language-bash">preston history\ --remote https://zenodo.org/record/7810635/files,https://linker.bio\ --anchor hash://sha256/4b192ed16cfe8577c2e275ada76bfdc19fe5a5381547139c8f8f4079e704b6f2</code></pre> <p>The aliases include are </p> <pre><code><https://bionomia.net/data/bionomia-public-claims.csv.gz> <http://purl.org/pav/hasVersion> <hash://sha256/9d345ffa98f2556aa77609fa3604e61efbd8e53c067153038a65c8b4d3705ac1> <urn:uuid:05efc676-b344-45f8-a109-de4df5b85fd2> . <https://bionomia.net/data/bionomia-public-profiles.csv> <http://purl.org/pav/hasVersion> <hash://sha256/80265c7f885a261396df909163ad8df6bc32246b55350a8bc65c20a05b30a04c> <urn:uuid:b3437df4-7dd5-44b3-a154-2e06320ade2a> . <https://bionomia.net/data/bionomia-public-claims.csv.gz> <http://purl.org/pav/hasVersion> <hash://sha256/cca558f470657a3c3fb99be70907d5705e7b5c20d12412073307fc9146e94394> <urn:uuid:fa6c6542-57bc-405a-b518-01c225f474b1> . <https://bionomia.net/data/bionomia-public-profiles.csv> <http://purl.org/pav/hasVersion> <hash://sha256/80265c7f885a261396df909163ad8df6bc32246b55350a8bc65c20a05b30a04c> <urn:uuid:36c4c1ff-3baf-4250-8c1f-fbfb2f33dfaf> .</code></pre> <p>as obtained via</p> <pre><code class="language-bash">preston alias\ --remote https://zenodo.org/record/7810635/files,https://linker.bio\ --anchor hash://sha256/4b192ed16cfe8577c2e275ada76bfdc19fe5a5381547139c8f8f4079e704b6f2</code></pre> <p>The first 5 lines of the tracked content are:</p> <pre><code>Subject,Predicate,Object https://gbif.org/occurrence/1839364365,http://rs.tdwg.org/dwc/iri/identifiedBy,https://orcid.org/0000-0001-9008-0611 https://gbif.org/occurrence/657804907,http://rs.tdwg.org/dwc/iri/identifiedBy,https://orcid.org/0000-0001-9008-0611 https://gbif.org/occurrence/657804727,http://rs.tdwg.org/dwc/iri/identifiedBy,https://orcid.org/0000-0001-9008-0611 https://gbif.org/occurrence/657804529,http://rs.tdwg.org/dwc/iri/identifiedBy,https://orcid.org/0000-0001-9008-0611</code></pre> <p>as obtained via:</p> <pre><code class="language-bash">preston cat\ --remote https://zenodo.org/record/7810635/files,https://linker.bio\ --anchor hash://sha256/4b192ed16cfe8577c2e275ada76bfdc19fe5a5381547139c8f8f4079e704b6f2\ https://bionomia.net/data/bionomia-public-claims.csv.gz\ | gunzip\ | head -n5 </code></pre> <p> </p> <p>and </p> <pre><code>Family,Given,Particle,OtherNames,Country,Keywords,wikidata,ORCID,URL Page,Roderic,,R. D. M. Page|Roderic D. M. Page|Rod Page,United Kingdom,"",,0000-0002-7101-9767,https://bionomia.net/0000-0002-7101-9767 Chatzimanolis,Stylianos,,S. Chatzimanolis|Stelios Chatzimanolis|Stelianos Chatzimanolis,United States,Taxonomist,,0000-0001-9008-0611,https://bionomia.net/0000-0001-9008-0611 Bachman,Steven,,Steven P Bachman|Steven Philip Bachman|Steve Bachman,United Kingdom,Red List|Conservation|Plants|Plantae|GIS|English,,0000-0003-1085-6075,https://bionomia.net/0000-0003-1085-6075 Robbins,Tod,,Todd Robbins,"",libraries|archives|Mormon studies|linked data|digital humanities|digital asset management,,0000-0002-6752-9721,https://bionomia.net/0000-0002-6752-9721</code></pre> <p>obtained via </p> <pre><code class="language-bash">preston cat\ --remote https://zenodo.org/record/7810635/files,https://linker.bio\ --anchor hash://sha256/4b192ed16cfe8577c2e275ada76bfdc19fe5a5381547139c8f8f4079e704b6f2\ https://bionomia.net/data/bionomia-public-profiles.csv\ | head -n5</code></pre> <p> </p> <p>This data publication was created in part in context of "Bee-hind the Scenes: Documenting Digital Traces of Prominent Natural History Bee Specimens" (see https://beehind.org), and is used together with an exhaustive list of record identifiers and associated institution, collection and catalog information [3].</p> <p><strong>References</strong></p> <p>[1] Shorthouse DP (2020) Slinging With Four Giants on a Quest to Credit Natural Historians for our Museums and Collections. Biodiversity Information Science and Standards 4: e59167. <a href="https://doi.org/10.3897/biss.4.59167">https://doi.org/10.3897/biss.4.59167</a></p> <p>[2] MJ Elliott, JH Poelen, JAB Fortes (2020). Toward Reliable Biodiversity Dataset References. Ecological Informatics. <a href="https://doi.org/10.1016/j.ecoinf.2020.101132">https://doi.org/10.1016/j.ecoinf.2020.101132</a></p> <p>[3] Poelen, Jorrit. (2023). Global Biodiversity Informatics Facility (GBIF): an exhaustive list of gbif record ids, dataset keys, and their associated Occurrence IDs, Institution Code, Collection Codes and Catalog Numbers. hash://sha256/ea88f03a7bfd1ba853fdbea3203d54ab81ac3cdc8e8da7c96bbbba9c4b05d933 hash://md5/c49fe34785354847b37ea4509261e130 (0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7789866</p> <p> </p>
Beetle Team Tribolium Data Archive
<p>The Beetle Team Tribolium Data Archive is a collection of 10 data sets created and/or analyzed over a 20-year period by a multidisciplinary group of collaborators, the "Beetle Team" (Jim Cushing, R. F. Costantino, Brian Dennis, Robert A. Desharnais, Shandelle M. Henson, Aaron A. King, and Jeffrey Edmunds). The data are from population laboratory experiments using the flour beetle <em>Tribolium castaneum</em>. Their research program was focused on providing biological evidence of complex nonlinear population dynamics.</p>
Moving Image Archive - Data Foundry - National Library of Scotland
<p>This dataset was created in October-December 2022 for the National Library of Scotland's Data Foundry by <a href="https://data.nls.uk/projects/the-national-librarians-research-fellowship-in-digital-scholarship-2022-23/">Gustavo Candela, National Librarian’s Research Fellowship in Digital Scholarship 2022-23</a>.</p> <p>This output is based on the <a href="http://data.nls.uk/data/metadata-collections/moving-image-archive/">Moving Image Archive</a> dataset and is the result of the transformation to RDF described in a research article published in the <a href="https://doi.org/10.1177/01655515231174386">Journal of Information Science</a>.</p> <p>For more information about the project, visit the <a href="https://data.nls.uk/projects/the-national-librarians-research-fellowship-in-digital-scholarship-2022-23/">Data Foundry Fellowship page</a>.</p> <p><strong>References</strong></p> <p>Candela, G. (2023). Towards a semantic approach in GLAM Labs: The case of the Data Foundry at the National Library of Scotland. <em>Journal of Information Science</em>. <a href="https://doi.org/10.1177/01655515231174386">https://doi.org/10.1177/01655515231174386</a></p>
Sequencing data for: Tracking climate-change induced biological invasions over 4 decades by metabarcoding archived natural eDNA samplers
<p><span>In a time of unprecedented global environmental change, understanding the response of biodiversity is paramount. However, our knowledge of anthropogenic impacts on ecosystems is limited by a lack of standardized retrospective biomonitoring data. Here, we use four-decade time series of archived blue mussels to trace spatiotemporal biodiversity change in coastal ecosystems. The filter-feeding mussels can serve as natural eDNA samplers, carrying an imprint of the surrounding aquatic community at the time of sampling. By sequencing the preserved DNA, we characterize highly diverse mussel-associated communities and reconstruct the invasion trajectory of an invasive species to the detriment of native taxa uncovering repeated population collapses and reinvasions after cold winters. Time series of natural eDNA samplers provide highly resolved temporal data on community assembly and global warming-driven invasion processes and overcome critical shortfalls in our understanding of biodiversity change in the Anthropocene.</span></p>
Data Archive for the NASA DAILI CubeSatMission:NASA Award Number: 80NSSC18K0973
<p>These are the operational data collected by the NASA DAILI CubeSat Mission from March 2 2022 to June 4 2022.</p> <p>The readme file describes the data in detail</p>
Zip archive containing datasets described in the manuscript entitled "Distribution-agnostic Deep Learning Enables Accurate Single‐Cell Data Recovery and Transcriptional Regulation Interpretation"
<p>The datasets used in the manuscript entitled "Distribution-agnostic Deep Learning Enables Accurate Single‐Cell Data Recovery and Transcriptional Regulation Interpretation". These datasets encompass all the experiments conducted in the manuscript, including simulation experiments, downsampling experiments, clustering, differential expression analysis, enrichment analysis, trajectory inference, batch correction, and clinical case discovery.</p> <p>The open-source software is available at https://github.com/XuYuanchi/Bis.</p>
Computational Data Archive in Support of the Ping-Pong Mechanism for Peptide Bond Formation and Hydrolysis
<p>In search for the goldilocks zone in the chemical space for amino acid oligomerization that is relevant to the emergence of protein-based metabolism, we identified a significant gap in our understanding about how peptide bond is formed and hydrolyzed under ambient aqueous conditions. We identified a six-step mechanism that reproduces the experimentally known experimental barriers. By careful evaluation of a comprehensive set of levels of theory, modelling of solvation effect, and calculation of thermochemical parameters, we established a robust computational model to expand the bulk water focus to interfacial phenomena of atmospheric/hydrospheric and litospheric/hydrospheric boundaries. </p> <p>While we cannot travel back in time billions of years to witness the "birth" of the first macromolecule with pre-biotic relevance, we present the dataset and the corresponding publication in RSC Organic and Biomolecular Chemistry (July 26, 2023 issue) our attempt to establish an unbiased computational platform where we can evaluate experiments and generate experimentally testable ideas.</p>
FEW-meter project data archive_31_12_2020
<p>Data collected in the FEW-meter project for the growing season 2020 from all case studies. The state as of December 31, 2020.</p>
FEW-meter project data archive_01_07_2020
<p>Data collected in the FEW-meter project for the growing season 2020 from all case studies. The state as of July 07, 2020.</p>
ScienceDex guides
Understand access before you commit
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.