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.
1,249
datasets available to search
ShareScore release 0.9.0
Dataset results
1,249 results for “R data”
Transformed raw data and R scripts for the paper Problem reports and team maturity in agile automotive software development published at CHASE2022
<p>Transformed raw data and R scripts for the paper Problem reports and team maturity in agile automotive software development published at CHASE2022</p> <p>15th International Conference on Cooperative and Human Aspects of Software Engineering, May 21–22, 2022, Pittsburgh, PA, USA}´</p>
Accuracy of bird identifications in citizen science data: a quantification of errors using photographic records [R code]
<p><strong>Appendix S1</strong>. The full dataset used in this study with a reproducible R code to perform data quality and network analyses. R code archived to Zenodo for publication. </p>
Data and R-code belonging to Manuscript: "Prior experience of captivity affects behavioural responses to 'novel' environments"
<p>Data and R-code belonging to Manuscript:"Prior experience of captivity affects behavioural responses to 'novel' environments" </p> <p>1) the data can be found in an excel file named: datafile_explorationGT2022.xlsx</p> <p>2) the R-code used to do the analysis in the MS can be found in an R-markdown file: Rmd_file_MS_GTexploration_R2.Rmd</p>
Coexpression data in COXPRESdb ver 7.1 (Xxx-r)
<p>RNAseq Coexpression data in COXPRESdb ver 7.1.</p> <p>The coexpression index is MR (smaller value indicates stronger coexpression).<br> Details of the data can be found on the download page in COXPRESdb. https://coxpresdb.jp/download/</p> <ul> <li>Cel-r.c1-0 Cel-r.v18-09.G13690-S1546.combat_pca_subagging.mrgeo.d.zip</li> <li>Cfa-r.c1-0 Cfa-r.v18-09.G15303-S253.combat_pca_subagging.mrgeo.d.zip</li> <li>Dme-r.c2-0 Dme-r.v18-09.G11937-S4596.combat_pca_subagging.mrgeo.d.zip</li> <li>Dre-r.c1-0 Dre-r.v18-09.G18446-S3049.combat_pca_subagging.mrgeo.d.zip</li> <li>Gga-r.c1-0 Gga-r.v18-09.G15554-S698.combat_pca_subagging.mrgeo.d.zip</li> <li>Hsa-r.c3-0 Hsa-r.v18-10.G16732-S36368.combat_pca_subagging.mrgeo.d.zip</li> <li>Mcc-r.c1-0 Mcc-r.v18-09.G15050-S1205.combat_pca_subagging.mrgeo.d.zip</li> <li>Mmu-r.c3-0 Mmu-r.v18-10.G16474-S42681.combat_pca_subagging.mrgeo.d.zip</li> <li>Rno-r.c1-0 Rno-r.v18-09.G15410-S2368.combat_pca_subagging.mrgeo.d.zip</li> <li>Sce-r.c1-0 Sce-r.v18-09.G5674-S1205.combat_pca_subagging.mrgeo.d.zip</li> <li>Spo-r.c1-0 Spo-r.v18-09.G5310-S143.combat_pca_subagging.mrgeo.d.zip</li> </ul> <p><strong>Reference</strong><br> Obayashi T, Kagaya Y, Aoki Y, Tadaka S, Kinoshita K. (2019) COXPRESdb v7: a gene coexpression database for 11 animal species supported by 23 coexpression platforms for technical evaluation and evolutionary inference. Nucleic Acids Res. 47: D55-D62</p> <p> </p>
› Figure 22. A, B, Tegenaria capolongoi; C-K, Tegenaria parmenidis; L-S, Tegenaria circeoensis sp. nov. Left male palp in ventral (E, N), retrolateral (F, O), and dorsal views (G, P); epigyne in ventral (A, C, Q, S) and vulva in dorsal view (B, D, R); intraspecific epigynal morphological variation (S); face of male in frontal view (J); spinnerets in ventral view (K); habitus (H) of male in dorsal and sternum in ventral view (I); carapace and abdomen of male in dorsal view (L, M). Scale bars = 0.5 mm (except 1 mm for H). in Phylogeny and taxonomy of European funnel-web spiders of the Tegenaria-Malthonica complex (Araneae: Agelenidae) based upon morphological and molecular data
› Figure 22. A, B, Tegenaria capolongoi; C-K, Tegenaria parmenidis; L-S, Tegenaria circeoensis sp. nov. Left male palp in ventral (E, N), retrolateral (F, O), and dorsal views (G, P); epigyne in ventral (A, C, Q, S) and vulva in dorsal view (B, D, R); intraspecific epigynal morphological variation (S); face of male in frontal view (J); spinnerets in ventral view (K); habitus (H) of male in dorsal and sternum in ventral view (I); carapace and abdomen of male in dorsal view (L, M). Scale bars = 0.5 mm (except 1 mm for H).
Supplementary material 1 from: Jochum A, Slapnik R, Klussmann-Kolb A, Páll-Gergely B, Kampschulte M, Martels G, Vrabec M, Nesselhauf C, Weigand AM (2015) Groping through the black box of variability: An integrative taxonomic and nomenclatural re-evaluation of Zospeum isselianum Pollonera, 1887 and allied species using new imaging technology (Nano-CT, SEM), conchological, histological and molecular data (Ellobioidea, Carychiidae). Subterranean Biology 16: 123-165. https://doi.org/10.3897/subtbiol.16.5758
List of localities of Zospeum isselianum: Explanation note: List of localities of Zospeum isselianum. A − Austria, BiH − Bosnia and Herzegovina, Cro − Croatia, Slo − Slovenia, Ita − Italy, CBSS − Collection of Croatian Biospeleological Society, CSR SASA – Malacological collection of the Biological Institute of the Centre for Scientific Research of the Slovenian Academy of Sciences and Arts in Ljubljana, SMNH – Malacological collection of the Slovenian Museum of Natural History, LIT – data from literature.
Supplementary material 2 from: Wayland MT, Vainio JK, Gibson DI, Herniou EA, Littlewood TDJ, Väinölä R (2015) The systematics of Echinorhynchus Zoega in Müller, 1776 (Acanthocephala, Echinorhynchidae) elucidated by nuclear and mitochondrial sequence data from eight European taxa. ZooKeys 484: 25-52. https://doi.org/10.3897/zookeys.484.9132
Maximum likelihood model parameters: Explanation note: Model parameters used in the maximum likelihood approach to phylogenetic reconstruction.
Supplementary material 4 from: Wayland MT, Vainio JK, Gibson DI, Herniou EA, Littlewood TDJ, Väinölä R (2015) The systematics of Echinorhynchus Zoega in Müller, 1776 (Acanthocephala, Echinorhynchidae) elucidated by nuclear and mitochondrial sequence data from eight European taxa. ZooKeys 484: 25-52. https://doi.org/10.3897/zookeys.484.9132
Patterns of COI sequence variation: Explanation note: Patterns of COI sequence variation. Graphs and discussion of patterns of nucleotide substitions in the COI data-set.
Supplementary material 1 from: Wayland MT, Vainio JK, Gibson DI, Herniou EA, Littlewood TDJ, Väinölä R (2015) The systematics of Echinorhynchus Zoega in Müller, 1776 (Acanthocephala, Echinorhynchidae) elucidated by nuclear and mitochondrial sequence data from eight European taxa. ZooKeys 484: 25-52. https://doi.org/10.3897/zookeys.484.9132
Aligned and concatenated partial sequences of COI and 28S rDNA: Explanation note: Aligned and concatenated partial sequences of COI and 28S rDNA in nexus format. Aligned partial sequences of COI and 28S rDNA from each acanthocephalan population have been concatenated. Gaps are indicated by '-'. The first 585 characters in each block correspond to COI and the remainder to 28S rDNA. The file contains data for all nine Echinorhynchus samples and the outgroup taxon, Acanthocephalus lucii. This nexus file was used in all phylogenetic analyses.
Supplementary material 3 from: Wayland MT, Vainio JK, Gibson DI, Herniou EA, Littlewood TDJ, Väinölä R (2015) The systematics of Echinorhynchus Zoega in Müller, 1776 (Acanthocephala, Echinorhynchidae) elucidated by nuclear and mitochondrial sequence data from eight European taxa. ZooKeys 484: 25-52. https://doi.org/10.3897/zookeys.484.9132
Nucleotide substitutions: Explanation note: Substitutions of nucleotides (transitions/transversions) for 28S rDNA (below the diagonal) and COI sequence data (above the diagonal).
International data (WB, UNDP, JMP) Nov. 2017. Preprocessed with R (code included).
<p>Preprocess of the following data for use with R:</p> <ul> <li>JMP, http://washdata.org/data [Nov. 2017]</li> <li>UNDP, http://hdr.undp.org/en/data [Nov. 2017]</li> <li>WB, http://data.worldbank.org/data-catalog/world-development-indicators [Nov. 2017]</li> </ul> <p> </p> <p>R files:</p> <p>- DATA2017_Part0.R : It reads the .csv files and writes three .Rdata with the tables with country data and description of variables, one for each data source. The WB pairs indicator-year are dropped if they do not have a minimum of records available. The selection is time consuming. Because of this, an auxiliary table with the number of records for all indicator-years has been saved separately from main results. Also, specific outputs for two thresholds have been already computed (200 and 250). A correspondence between country names used by UNDP and JMP-WB is provided in a .csv file.</p> <p>- DATA2017_Part0B.R : Here, the three tables of data and the three ones with description of the indicators are merged. The countries are kept in the final list only if they are present in the three sources. </p> <p>- DATA2017_Part0C.R : First, data available for year 2015 are collected.. The WB records are kept only if they have less than 40 empty cases. Second, the indicators with time evolution data from 2000-2010 are merged in a single table (WB and JMP data). </p> <p> </p> <p>Directories:</p> <ul> <li>csv/</li> <li>data_org/</li> <li>data_org/JMP/</li> <li>data_org/UNDP/</li> <li>data_org/WB/</li> <li>Rdata/ </li> </ul>
Goldenberg, J., Bisschop, K., Bruni, G., Di Nicola, M. R., Banfi, F., Faraone, F. P. "Replication Data for: Melanin-based color variation in response to changing climates in snakes"
<p>This repository contains the data used to produce the manuscrpit "Melanin-based color variation in response to changing climates in snakes" by Goldenberg, J., Bisschop, K., Bruni, G., Di Nicola, M. R., Banfi, F., Faraone, F. P.</p> <p>Article DOI: 10.1002/ece3.11627</p> <p>Journal: Ecology and Evolution</p>
Gavin and Rían: Final FORRT R&R data
<p>Non-random sample of psychology papers + relevant replications/conceptual replications/meta analysis collected as part of FORRT project. </p>
Data for "SPH modelling of AGB wind morphology in hierarchical triple systems & comparison to observation of R Aql"
<div> <p>Additional material to Malfait et al. 2024, subm. "SPH modelling of AGB wind morphology in hierarchical triple systems & comparison to observation of R Aql"</p> <p>This contains input files and final output dumps of the Phantom simulations of this paper.</p> <p>The code used to perform the simulations is available at: <a href="https://github.com/danieljprice/phantom">https://github.com/danieljprice/phantom.</a></p> <p>Splash (<a href="https://github.com/danieljprice/splash">https://github.com/danieljprice/splash</a> ) and Plons (<a href="https://github.com/Ensor-code/plons">https://github.com/Ensor-code/plons</a> ) were used to create figures and plots from this data.</p> <p> </p> </div>
Data file for paper: Javier Rubio-Garcia; Anthony R J Kucernak, and Alexandra Charleson, "Direct visualization of reactant transport in forced convection electrochemical cells and its application to Redox Flow Batteries, Electrochemistry Communications, 2018
<p>Excel Data file containing the data presented in the figures of the paper:</p> <p>Javier Rubio-Garcia; Anthony R J Kucernak, and Alexandra Charleson, "Direct visualization of reactant transport in forced convection electrochemical cells and its application to Redox Flow Batteries</p> <p>Electrochemistry Communications, 2018,</p> <p>DOI:10.1016/j.elecom.2018.07.002</p> <p>Please cite the above reference if you wish to use this data</p>
Supplementary data to the manuscript "Morphology and metabarcoding! A test with stream diatoms from Mexico highlights the complementarity of methods", submitted to Freshwater Science by D. Mora, N. Abarca, S. Proft, J. Grau, N. Enke, J. Carmona, O. Skibbe, R. Jahn and J. Zimmermann.
<p>Demultiplexed fastq files for 18 samples, accompanied by a table including sample numbers in the manuscript and sample numbers in the fastq files.</p>
Supplementary material 7 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
This document contains an annotated set of data quality checks that participants report they use when evaluating and cleaning datasets. These items outline how participants are judging if the data suits their purpose.
Supplementary material 3 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
The informed consent request and workshop survey questions given to participants after the workshop each day for 4 consecutive days.
Supplementary material 2 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
This document shows just the questions we asked the applicants who applied to participate in this Georeferencing for Research Use workshop. We used a Google Form to deliver these questions and collect responses. It is both an application and serves as our pre-workshop survey.
Supplementary material 8 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
Summary of desired future workshop topics that were listed by participants on the last day of the workshop.
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.