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.
409
datasets available to search
ShareScore release 0.9.0
Dataset results
409 results for “information use”
Investigating morphological complexes using informational dissonance and bayes factors: A case study in corbiculate bees
<p>It is widely recognized that different regions of a genome often have different evolutionary histories and that ignoring this variation when estimating phylogenies can be misleading. However, the extent to which this is also true for morphological data is still largely unknown. Discordance among morphological traits might plausibly arise due to either variable convergent selection pressures or else phenomena such as hemiplasy. Here we investigate patterns of discordance among 282 morphological characters, which we scored for 50 bee species particularly targeting corbiculate bees, a group that includes the well-known eusocial honeybees and bumblebees. As a starting point for selecting the most meaningful partitions in the data, we grouped characters as morphological modules, highly integrated trait complexes that as a result of developmental constraints or coordinated selection we expect to share an evolutionary history and trajectory. In order to assess conflict and coherence across and within these morphological modules, we used recently developed approaches for computing Bayesian phylogenetic information allied with model comparisons using Bayes factors. We found that despite considerable conflict among morphological complexes, accounting for among-character and among-partition rate variation with individual gamma distributions, rate multipliers, and linked branch lengths can lead to coherent phylogenetic inference using morphological data. We suggest that evaluating information content and dissonance among partitions is useful step in estimating phylogenies from morphological data, just as it is with molecular data. Furthermore, we argue that adopting emerging approaches for investigating dissonance in genomic datasets may provide new insights into the integration and evolution of anatomical complexes.</p>
Full information on the eORCA1 grid (mesh_mask) used in IPSL-CM6A-LR configuration
<p>This file contains all relevant information on the ORCA1 grid. See https://www.nemo-ocean.eu/wp-content/uploads/NEMO_book.pdf for more details on the grid.</p>
On the use of private versus social information in oviposition site choice decisions by Drosophila melanogaster females
<p>Individuals are faced with decisions throughout their lifetimes, and the choices they make often have important consequences towards their fitness. Being able to discern which available option is best to pursue often incurs sampling costs, which may be largely avoided by copying the behaviour and decisions of others. Although social learning and copying behaviours are widespread, much remains unknown about how effective and adaptive copying behaviour is, as well as the factors that underlie its expression. Recently, it has been suggested that since female fruit flies (<i>Drosophila melanogaster</i>) appear to rely heavily on public information when selecting oviposition sites, they are a promising model system for researching patch-choice copying, and more generally, the mechanisms that control decision-making. Here, we set out to determine how well female distinguish between socially-produced cues, and whether females are using 'relevant' signals when choosing an oviposition site. We found that females showed a strong preference for ovipositing on media patches that had been previously occupied by ovipositing females of the same species and diet over other female outgroups. However, in a separate assay, we observed that females favoured ovipositing on media patches that previously housed virgin males over those exhibiting alternative conspecific signals. Our results confirm that females use cues left behind by other flies when choosing between potential oviposition sites, though their prioritization of these signals raises serious questions as to whether fruit flies are employing copying behaviour, or are instead responding to signals that may not be of relevance to oviposition site suitability.</p>
Informing antenna design for Global 21-cm experiments using a simulated Bayesian data analysis pipeline (supplementary data)
<p>These are the posterior files, foreground simulation data sets and chromaticity factor values used to produce the results for <a href="https://arxiv.org/abs/2106.10193">arXiv:2106.10193</a>.</p> <p>Plots of the fitted signal and residuals for each case are included, as is a plotting function to reproduce key figures.</p> <p>Naming conventions:</p> <ul> <li>f0: Centre frequency of the 21cm signal present in the simulated data</li> <li>A: Amplitude of the simulated 21cm signal present in the simulated data</li> <li>M_sig: Model being fit to the data includes a 21cm signal</li> <li>M_nosig: Model being fit to the data is a foreground only</li> </ul> <p>Software used:</p> <ul> <li><a href="https://github.com/PolyChord/PolyChordLite/tree/839292290a7747dbee82933bb9f7f955ac45c3ca">PolyChord</a></li> </ul> <p> </p>
On the calculation of second-order magnetic properties using subsystem approaches in the relativistic framework - supplementary information
<p>Supplementary information to the publication "On the calculation of second-order magnetic properties using subsystem approaches in the relativistic framework"</p> <p>The attached 'supplementary_info_fde_mag.zip' unpacks to three directories:</p> <ul> <li>'optimized_structures' directory contains optimized molecular structures in xyz format</li> <li>'results' directory contains all the results obtained in this work, collected in <ul> <li>gnumeric and xlsx spreadsheets with complete results from DIRAC and from ADF</li> <li>csv files (data involving heavy atoms, X, and hydrogen-bonded H atoms, Hb)</li> <li>'supplementary_tables' latex and pdf files</li> </ul> </li> <li>'visualization' directory contains the data for plotting the NMR shielding density and prepared plots; for the explanation of files in this directory open the 'visualization/visualization.html' document in your browser or read the corresponding jupyter notebook (visualization/visualization.ipynb')</li> </ul>
On the calculation of second-order magnetic properties using subsystem approaches in the relativistic framework - supplementary information
<p>Supplementary information to the publication "On the calculation of second-order magnetic properties using subsystem approaches in the relativistic framework"</p> <p>The attached 'supplementary_info_fde_mag.tar.gz' unpacks to three directories:</p> <ul> <li>'optimized_structures' directory contains optimized molecular structures in xyz format</li> <li>'results' directory contains all the results obtained in this work, collected in <ul> <li>spreadsheets with complete results from DIRAC and from ADF (in *xlsx and *gnumeric format)</li> <li>csv files (data involving heavy atoms, X, and hydrogen-bonded H atoms, Hb obtained with DC and ZORA Hamiltonians and TZ-type basis sets)</li> <li>'supplementary_tables' latex and pdf files</li> </ul> </li> <li>'visualization' directory contains the data for plotting the NMR shielding density and prepared plots; for the explanation of files in this directory open the 'visualization/visualization.html' document in your browser or read the corresponding jupyter notebook (visualization/visualization.ipynb')</li> </ul>
Land use information and NO2 observations of environmental monitoring stations in China
<p>The land use information for environmental monitoring stations in China is stored in the "<i>site_lu.csv" </i>file. The monthly NO2 observations from 2015 to 2017 are stored in the "<i>NO2_ground.csv</i>" file.</p>
Pathformer: a biological pathway informed Transformer for disease diagnosis and prognosis using multi-omics data
<p>Integrating multi-omics data offers a more comprehensive view of gene regulation, which would be helpful in achieving accurate diagnosis of diseases like cancer. To improve the accuracy of disease diagnosis and prognosis, we developed Pathformer, a multi-omics integration method for both tissue and liquid biopsy data. We implemented Pathformer's network architecture using the “PyTorch” package in Python v3.6.9, and our codes can be found in the GitHub repository (https://github.com/lulab/Pathformer). This repository contains preprocessed TCGA dataset data, preprocessedliquid biopsy dataset data, result of Pathformer and comparison_methods, mentioned in GitHub project and article.</p>
Supporting information for: Discrimination ability of central visual field testing using stimulus size I, II, and III and relationship with macular ganglion cell thickness in chiasmal compression
<p><strong>Purpose</strong><strong>: </strong>To compare the relationship between macular ganglion cell layer (mGCL) thickness and 10-2 visual field (VF) sensitivity using different stimulus sizes in patients with temporal hemianopia from chiasmal compression.</p> <p><strong>Methods:</strong><strong> </strong>A cross-sectional study was conducted involving 30 eyes from 25 patients with temporal VF loss on 24-2 SITA standard automated perimetry due to previous chiasmal compression and 30 healthy eyes (23 controls). Optical coherence tomography (OCT) of the macular area and 10-2 VF testing using Goldmann stimulus size I (GI), II (GII), and III (GIII) were performed in the Octopus 900 perimeter. For the sake of analysis, mGCL thickness and VF data were segregated into four quadrants (two temporal and two nasal) and two halves (temporal and nasal) centered on the fovea, and the groups were compared using generalized estimated equations. The discrimination ability of GI, GII, and GIII was evaluated, as was the correlation between mGCL and 10-2 VF sensitivity using GI, GII, and GIII. </p> <p><strong>Results:</strong><strong> </strong>All mGCL parameters were significantly reduced in patients compared to controls. 10-2 VF test sensitivity using GI, GII, and GIII was significantly lower in patients than in controls (p≤0.008) for all parameters, except the three nasal divisions when using GI (p=0.41, 0.07 and 0.18) Significant correlations were found between temporal VF sectors (all stimulus sizes) and the corresponding nasal mGCL measurements, with similar discrimination ability. Significant correlations were also observed between all three nasal VF divisions and the corresponding temporal mGCL thickness when using stimulus sizes I and II, but not stimulus size III.</p> <p><strong>Conclusions</strong><strong>:</strong> On 10-2 VF testing, GII outperformed GI and GIII with regard to discrimination ability and structure-function correlation with mGCL thickness in chiasmal compression. Our findings suggest that the use of GII can enhance the diagnostic power of 10-2 VF testing, although further studies are necessary to support this conclusion.</p>
Data of "Using current research information systems to investigate data acquisition and data sharing practices of computer scientists"
<p>This study describes a methodology where departmental academic publications are used to analyse the ways in which computer scientists share research data.</p> <p>Without sufficient information about researchers’ data sharing, there is a risk of mismatching FAIR data service efforts with the needs of researchers. This study describes a methodology where departmental academic publications are used to analyse the ways in which computer scientists share research data. The advancement of FAIR data would benefit from novel methodologies that reliably examine data sharing at the level of multidisciplinary research organisations. Studies that use CRIS publication data to elicit insight into researchers’ data sharing may therefore be a valuable addition to the current interview and questionnaire methodologies.</p> <p><strong>Data was collected from the following sources:</strong></p> <p>All journal articles published by researchers in the computer science department of the case study’s university during 2019 were extracted for scrutiny from the current research information system. For these 193 articles, a coding framework was developed to capture the key elements of acquiring and sharing research data. Article DOIs are included in the research data.</p> <p>The scientific journal articles and theirs DOIs are used in this study for the purpose of academic expression.</p> <p>The raw data is compiled into a single CSV file. Rows represent specific articles and columns are the values of the data points described below. Author names and affiliations were not collected and are not included in the data set. </p> <p> </p> <p>The following data points were used in the analysis:</p> <p><strong>Data points</strong></p> <ul> <li><strong>Main study types</strong></li> <li>Literature-based study (e.g. literature reviews, archive studies, studies of social media)</li> <li>yes/no</li> <li>Novel computational methods (e.g. algorithms, simulations, software)</li> <li>yes/no</li> <li>Interaction studies (e.g, interviews, surveys, tasks, ethnography)</li> <li>yes/no</li> <li>Intervention studies (e.g., EEG, MRI, clinical trials)</li> <li>yes/no</li> <li>Measurement studies (e.g. astronomy, weather, acoustics, chemistry)</li> <li>yes/no</li> <li>Life sciences (e.g. “omics”, ecology)</li> <li>yes/no</li> <li><strong>Data acquisition</strong></li> <li>Article presents a data availability statement</li> <li>yes/no</li> <li>Article does not utilise data</li> <li>yes/no</li> <li>Original data was collected</li> <li>yes/no</li> <li>Open data from prior studies were used</li> <li>yes/no</li> <li>Open data from public authorities, companies, universities and associations</li> <li>yes/no</li> <li><strong>Data sharing</strong></li> <li>Article does not use original data</li> <li>yes/no</li> <li>Data of the article is not available for reuse</li> <li>yes/no</li> <li>Article used openly available data</li> <li>yes/no</li> <li>Authors agree to share their data to interested readers</li> <li>yes/no</li> <li>Article shared data (or part of) as supplementary material</li> <li>yes/no</li> <li>Article shared data (or part of) via open deposition</li> <li>yes/no</li> <li>Article deposited code or used open code</li> <li>yes/no</li> </ul>
Data from: A genotyping-in-thousands by sequencing panel to inform invasive deer management using non-invasive fecal and hair samples
<p>Studies in ecology, evolution, and conservation often rely on non-invasive samples, making it challenging to generate large amounts of high-quality genetic data for many elusive and at-risk species. We developed and optimized a Genotyping-in-Thousands by sequencing (GT-seq) panel using non-invasive samples to inform the management of invasive Sitka black-tailed deer (<em>Odocoileus hemionus sitkensis</em>) in Haida Gwaii (Canada). We validated our panel using paired high-quality tissue and non-invasive fecal and hair samples to simultaneously distinguish individuals, identify sex and reconstruct kinship among deer sampled across the archipelago, then provided a proof-of-concept application using field-collected feces on SGang Gwaay, an island of high ecological and cultural value. Genotyping success across 244 loci was high (90.3%) and comparable to that of high-quality tissue samples genotyped using restriction-site associated DNA sequencing (92.4%), while genotyping discordance between paired high-quality tissue and non-invasive samples was low (0.50%). The panel will be used to inform future invasive species operations (culls or eradications) in Haida Gwaii by providing individual and population information to inform management. More broadly, our GT-seq workflow that includes quality control analyses for targeted SNP selection and a modified protocol may be of wider utility for other studies and systems where non-invasive genetic sampling is employed.</p>
Non-informative dataset to be used with GA-VirReport workflow
<p>Input dataset of plant chloroplast, mitochondria and rRNA</p>
Machine learning methods detect arm movement impairments in a patient with parieto-occipital lesion using only early kinematic information.
<p>This depository contains the data and codes of the paper: </p> <p>Bosco, A., Bertini C., Filippini M., Foglino C., Fattori P (2022). Machine learning methods detect arm movement impairments in a patient with parieto-occipital lesion using only early kinematic information. <em>Journal of Vision</em>, in press. </p> <p>Data are provided in mat-file, codes is provided in m-files (Matlab).</p>
Supporting Information: Mapping conduits in two-dimensional heterogeneous karst aquifers using hydraulic tomography
<p>This file contains the supplementary data for a article submitted to Journal of Hydrology.</p>
BdSL47: A complete dataset of sign alphabet and digits of Bangla Sign Language (BdSL) using depth information via MediaPipe
<p><strong>BdSL47</strong> is the first open-access complete dataset in Bangla Sign Language that contains hand signs from both 10 sign digits (from sign ০ to sign ৯) and 37 sign alphabet (from sign অ to sign ँ).</p> <p>Dataset summary :</p> <ul> <li>100 RGB images per sign (total 47 signs) from each of 10 users</li> <li>Total input images : 100×47×10 = 47000</li> <li>Input images are processed via MediaPipe, which provided <ul> <li>an output image with hand key-points being detected</li> <li>3D coordinate values of 21 predefined key-points</li> <li>Total 63 coordinate values for each sample</li> </ul> </li> <li>The values are stored in csv files</li> <li>1 CSV file contains values from 100 samples of 1 sign from 1 user</li> <li>Total CSV files : 47×10 = 470</li> </ul> <p>The dataset has been made public for further research purposes. It is also available upon request <a href="https://drive.google.com/drive/u/8/folders/1wmJUlgWUrWNnOvzuL8Ci82Hm3zUx4wS-" rel="noopener">here</a>.</p>
Forecasting 24-hour-averaged PM2.5concentration in the Aburrá Valley using tree-based ML models, global forecasts, and satellite information: Dataset
<p>Data necessary for the training and evaluating the 24-hourly-averaged PM2.5 forecast over 19 stations within the Aburrá Valley, Colombia, is included here.</p>
Data from: On-chip distribution of quantum information using traveling phonons
<p>Source data for Figures.</p>
Supporting Information for "Investigation of Hikurangi subduction zone slow slip events using onshore and onshore geodetic data" PhD thesis
<p>The data sets included here are those inverted using the TDEFNODE (McCaffrey et al., 2009) inversion code in the PhD thesis "Investigation of Hikurangi subduction zone slow slip events using onshore and onshore geodetic data" to obtain geodetic slip models of the 2013-2016 and February-July 2019 periods at the Hikurangi subduction zone.</p> <p> </p> <p><em>The 2013-2016 period captured the 2013 Kāpiti and 2014/2015 Manawatū slow slip events (SSEs), in addition to the 2013 Cook Strait, 2013 Lake Grassmere, and 2014 Eketāhuna earthquakes. The data related to this model are:</em></p> <p><strong>campaign_gps_2013.ts</strong><br> -campaign GPS time series<br> -columns: Year East East_sigma North North_sigma Up Up_sigma<br> -input using TDEFNODE command ts1</p> <p><strong>coseismic_displacements_2013_07_21.ds </strong><br> -coseismic displacements for Cook Strait earthquake (Hamling et al., 2014)<br> -columns: Longitude Latitude Disp_East Disp_North Sigma_East Sigma_North Site Disp_Up Sigma_Up Time1 Time2<br> -input using TDEFNODE command ds2</p> <p><strong>coseismic_displacements_2013_08_16.ds </strong><br> -coseismic displacements for Lake Grassmere earthquake (Hamling et al., 2014)<br> -columns: Longitude Latitude Disp_East Disp_North Sigma_East Sigma_North Site Disp_Up Sigma_Up Time1 Time2<br> -input using TDEFNODE command ds2</p> <p><strong>onshore_gnss_kapiti_manawatu_2013_2016.ts</strong><br> -GNSS time series<br> -columns: Year East East_sigma North North_sigma Up Up_sigma<br> -input using TDEFNODE command ts1</p> <p><strong>LOS_coseismic_2013_08_16.is<br> -</strong>coseismic Line of Sight displacements for Lake Grassmere earthquake (Hamling et al., 2014)<br> -convention: negative displacement equivalent to ground moving towards the satellite<br> -columns: Longitude Latitude LineOfSight_disp sigma Unit_x Unit_y Unit_z<br> -input using TDEFNODE command is1</p> <p> </p> <p><em>Data related to the February-July 2019 SSE model are:</em></p> <p><strong>onshore_gnss_east_coast_sse_2019.ts</strong><br> -GNSS time series<br> -columns: Year East East_sigma North North_sigma Up Up_sigma<br> -input using TDEFNODE command ts4</p> <p><strong>seafloor_displacement_gisborne.ts</strong><br> -seafloor pressure time series<br> -convention: positive change equivalent to seafloor uplift<br> -columns: Year East East_sigma North North_sigma Up Up_sigma<br> -input using TDEFNODE command ts4</p> <p><strong>seafloor_displacement_hawkebay.ts</strong><br> -seafloor pressure time series<br> -convention: positive change equivalent to seafloor uplift<br> -columns: Year East East_sigma North North_sigma Up Up_sigma<br> -input using TDEFNODE command ts4</p> <p><strong>LOS_SSE_2019.is</strong><br> -SSE-related Line of Sight displacement<br> -convention: positive displacement equivalent to ground moving away from satellite<br> -columns: Longitude Latitude LineOfSight_disp sigma Unit_x Unit_y Unit_z<br> -input using TDEFNODE command is1</p> <p> </p> <p>The TDEFNODE manual can be found here:<br> https://robmccaffrey.github.io/TDEFNODE/manual/tdefnode_manual.html</p> <p>The header lines in the time series files (.ts) take the site inter-SSE rates from the model of Wallace et al. (2012).</p> <p> </p> <p><em>References</em></p> <p>Hamling, I. J., D’Anastasio, E., Wallace, L. M., Ellis, S., Motagh, M., Samsonov, S., Palmer, N., and Hreinsdóttir, S. (2014). Crustal deformation and stress transfer during a propagating earthquake sequence: The 2013 Cook Strait sequence, central New Zealand. <em>Journal of Geophysical Research: Solid Earth</em>, <strong>119</strong>(7):6080–6092.</p> <p>McCaffrey, R. (2009). Time-dependent inversion of three-component continuous GPS for steady and transient sources in northern Cascadia. <em>Geophysical Research Letters</em>, <strong>36</strong>(L07304).</p> <p>Wallace, L. M., Barnes, P., Beavan, J., Van Dissen, R., Litchfield, N., Mountjoy, J., Langridge, R., Lamarche, G., and Pondard, N. (2012). The kinematics of a transition from subduction to strike-slip: An example from the central New Zealand plate boundary. <em>Journal of Geophysical Research: Solid Earth</em>, <strong>117</strong>(B2).</p>
Comparison of allosteric signaling in DnaK and BiP using mutual information between simulated residue conformations
<p>Molecular Dynamics trajectories of the Hsp70 chaperones DnaK and BiP. System configurations include DnaK or BiP bound to ATP, ATP exchanged to ADP, or the NRLLLTG peptide.</p>
Data from: Response to food restriction, but not social information use, varies seasonally in captive cardueline finches
<p>Temperate winters can impose severe conditions on songbirds that threaten survival, including shorter days and often lower temperature and food availability. One well-studied mechanism by which songbirds cope with such conditions is seasonal acclimatization of thermal metabolic traits, with strong evidence for both preparative and responsive changes in thermogenic capacity (i.e., the ability to generate heat) to low winter temperature. However, a bird's ability to cope with seasonal extremes or unpredictable events is likely dependent on a combination of behavioral and physiological traits that function to maintain allostatic balance. The ability to cope with reduced food availability may be an important component of organismal response to temperate winters in songbirds. Here we compare responses to experimentally reduced food availability at different times of year in captive red crossbills (<em>Loxia curvirostra</em>) and pine siskins (<em>Spinus pinus</em>) – two species that cope with variable food resources and live in cold places – to investigate seasonal changes in the organismal response to food availability. Further, red crossbills are known to use social information to improve response to reduced food availability, so we also examine whether use of social information in this context varies seasonally in this species. We find that pine siskins and red crossbills lose less body mass during time-restricted feedings in late winter compared to summer, and that red crossbills further benefit from social information gathered from observing other food-restricted red crossbills in both seasons. Observed changes in body mass were only partially explained by seasonal differences in food intake. Our results demonstrate seasonal acclimation to food stress and social information use across seasons in a controlled captive environment and highlight the importance of considering diverse physiological systems (e.g., thermogenic, metabolic, digestive, etc) to understand organismal responses to environmental challenges.</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.