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.
7,195
datasets available to search
ShareScore release 0.9.0
Dataset results
7,195 results for “SET”
Schematic and adapted figures from IPBES Sustainable Use of Wild Species Assessment - Chapter 1. Setting the Scene
<p>Schematic and adapted figures from Chapter 1 of the thematic assessment of the sustainable use of wild species of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services.</p>
Northern Nevada wildlife and topography: Camera trapping data set for 14 mammal species collected from 100 sampling sites in northwestern Nevada
<p>Camera traps are one of the most common field techniques for surverying terrestrial mammal communities and thus, much work has gone into understanding how different factors influence species detection at camera trap locations. However, the effect of fine-scale topography, such as terrain slope and position, on wildlife detection has not been explicitly quantified despite strong effects of topography on animal movement in mountainous regions. This data set contains weekly detection non-detection data for 14 mammal species from 100 camera traps sites monitored for 28 months (June 2018 - September 2020) in northwestern Nevada, U.S.A. This sampling extent was split into 3 month sampling seasons, exclusive of winter (Dec, Jan, Feb) and spring 2020, when data were sparse. In addition to species detection data, that dataset includes topographic variables at cameras sites: 1) terrain slope, calculated in R package raster from a 10m digital elevation model and 2) Topographic position index averaged across three buffer sizes around points 270m, 810m, and 2430m. The land cover variables proportion mixed conifer and proportion pinyon-juniper woodland within a 5000m buffer of sites are also included. Both are derived from the USDA/US DOI Landfire 2016 dataset. The luring variable indicates whether attractant was applied at a site during a given week, the effect of which was assumed to last for a month after the last application. </p>
single-cell RNAseq data (data set 20) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset20) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from breast cancer samples downloaded from the GEO website (GSE180286)<strong>. </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p> <p> </p>
single-cell RNAseq data (data set 18) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset18) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from Liver cancer set 1 samples downloaded from the GEO website (GSE125449)<strong>. </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 17) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset17 was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from PBMC metastatic MCC samples downloaded from the GEO website (GSE117988)<strong>. </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 12) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset12) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor10 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 16) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset16) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from CD4 T-cells in PACA samples downloaded from the GEO website (GSE156728)<strong>. </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p> <p> </p>
single-cell RNAseq data (data set 11) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset11) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor9 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 14) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset14) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor12 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 9) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset9) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor7 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 8) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset8) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor6 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 7) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset7) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor5 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 19) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset19) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from Liver cancer set 2 samples downloaded from the GEO website (GSE125449)<strong>. </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 5) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset5) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor3 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 15) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset15) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from CD8 T-cells in PACA samples downloaded from the GEO website (GSE156728)<strong>. </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 4) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset4) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor2 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 3) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset3) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from normal Pancreas donor1 downloaded from the GEO website (GSE114297)<strong>. </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p> <pre> </pre>
Research Workflows and Open Science - Data Set
<p>Data set accompanying the report "Research Workflows and Open Science", a systematic study of open science research workflows.</p> <p>The data set summarises the open science characteristics exhibited by the analysed workflows. The first two columns ‘<strong>workflow ID</strong>’ and ‘<strong>URL</strong>’ are dedicated to the ID we used to identify each workflow and to the publications related to the workflows respectively.</p> <p>The remaining columns are dedicated to the characteristics exhibited by the analysed workflows and are named The remaining columns are dedicated to the characteristics exhibited by the analysed workflows and are named following the different categories identified:</p> <ul> <li> <p>'<strong>used/open science infrastructure/virtual</strong>'</p> <ul> <li> <p>If a workflow relies on a virtual open infrastructure (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open science infrastructure/physical</strong>'</p> <ul> <li> <p>If a workflow relies on a physical open infrastructure (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open scientific knowledge/open source software</strong>'</p> <ul> <li> <p>If a workflow relies on open source software (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open scientific knowledge/open hardware</strong>'</p> <ul> <li> <p>If a workflow relies on open hardware (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open scientific knowledge/open research data</strong>'</p> <ul> <li> <p>If a workflow (re)uses open research data (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open scientific knowledge/open educational resources</strong>'</p> <ul> <li> <p>If a workflow (re)uses open educational resources (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>produced/open scientific knowledge/(open access) scientific publication</strong>'</p> <ul> <li> <p>If a workflow envisages the release of a scientific publication (e.g. papers, reports, data management plans, preprints, study designs) under an open access licence (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>produced/open scientific knowledge/open source software</strong>'</p> <ul> <li> <p>If a workflow envisages the release of software (e.g. code, analysis scripts) under an open access licence (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>produced/open scientific knowledge/open research data</strong>'</p> <ul> <li> <p>If a workflow envisages the release of open research data (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>produced/open scientific knowledge/open educational resources</strong>'</p> <ul> <li> <p>If a workflow envisages the release of open educational resources (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>transparency/transparency type</strong>'</p> <ul> <li> <p>degree of transparency of a workflow, defined in terms of which research products are openly shared and when in order to document the research processes (‘built-in’ if transparent, ‘enabled’ if capable of being transparent, ‘opaque’ otherwise)</p> </li> </ul> </li> <li> <p>'<strong>transparency/sharing type</strong>'</p> <ul> <li> <p>workflow categories based on when the research products are shared (‘end’ for sharing at the end of the workflow, mixed for sharing part of the research products during the workflow and the rest at the end of it, ‘iterative’ for sharing iteratively during or at the end of the related workflow phase, and ‘user-dependent’, where it is ultimately up to the researcher to decide when to share the research products since the workflow offers different paths to follow while imposing no sharing constraint.)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/collaboration implementation</strong>'</p> <ul> <li> <p>If a workflow implements collaborative practices (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open engagement of societal actors/crowdfunding</strong>'</p> <ul> <li> <p>If a workflow envisages crowdfunding (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open engagement of societal actors/crowdsourcing</strong>'</p> <ul> <li> <p>If a workflow envisages crowdsourcing (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open engagement of societal actors/scientific volunteering</strong>'</p> <ul> <li> <p>If a workflow envisages scientific volunteering (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open engagement of societal actors/citizen and participatory science</strong>'</p> <ul> <li> <p>If a workflow envisages citizen and participatory science (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open dialogue with other knowledge systems/indigenous peoples</strong>'</p> <ul> <li> <p>If a workflow envisages the establishment of a dialogue with indigenous peoples (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open dialogue with other knowledge systems/marginalised scholars</strong>'</p> <ul> <li> <p>If a workflow envisages the establishment of a dialogue with marginalised scholars (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open dialogue with other knowledge systems/local communities</strong>'</p> <ul> <li> <p>If a workflow envisages the establishment of a dialogue with local communities (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>assessment</strong>'</p> <ul> <li> <p>If a workflow implements assessment processes for the evaluation of the research products created (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>automation</strong>'</p> <ul> <li> <p>If a workflow includes automated processes (yes/no)</p> </li> </ul> </li> </ul>
Extracting interaction potentials from trajectories: code and a set of 30 simulated contest trajectories
<p>Dataset and code provided as supporting materials for the manuscript "Modeling animal contests based on spatio-temporal dynamics". See the Supporting Information of the manuscript for detailed instructions of use.</p>
A novel probe set for the phylogenomics and evolution of RTA spiders
<p><span>Spiders are important models for evolutionary studies of web building, sexual selection and adaptive radiation. The recent development of probes for UCE-based phylogenomic studies has shed light on the phylogeny and evolution of spiders. However, the two available UCE probe sets for spider phylogenomics (Spider and Arachnida probe sets) have relatively low capture efficiency within spiders and are not optimized for the RTA clade, a hyperdiverse lineage that is key to understanding the evolution and diversification of spiders. In this study, we sequenced 15 genomes of species in the RTA clade, and using eight reference genomes, we developed a new UCE probe set (41,845 probes targeting 3802 loci, labelled as the RTA probe set).</span> <span>The performance of the RTA probes in resolving the phylogeny of the RTA clade was compared with the Spider and Arachnida probes through an in-silico test on 19 genomes. We also tested the new probe set empirically on 28 spider species of major spider lineages. The results showed that the RTA probes recovered twice and four times as many loci as the other two probe sets, and the phylogeny from the RTA UCEs provided higher support for certain relationships. This newly developed UCE probe set shows higher capture efficiency empirically and is particularly advantageous for phylogenomic and evolutionary studies of RTA clade and jumping spiders.</span></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.