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10,119 results for “IT Support”
Supporting data: "Uncertainty in sea level rise projections due to the dependence between contributors"
<p>These files contain the data analyzed in Le Bars 2018. The paper is available on EarthArXiv (https://eartharxiv.org/uvw3s/) and was submitted to Earth's Future.</p> <p>The NetCDF files contain the Probability Density Functions output from the Probabilistic Sea Level Projection (PSLP) model version 1.</p> <p>Simulations are:<br> IPCC1: The control IPCC AR5 simulation<br> IPCC2: The same but assuming independence between sea level contributors<br> IPCC3: The same but assuming correlation of 1 between sea level contributors<br> Prob1: The control simulation from the probabilistic model<br> Prob2: Assuming independence<br> Prob3: Assuming correlation of 1 between sea level contributors<br> Prob4: Low dependence case<br> Prob5: High dependence case<br> Prob6 to Prob9: Sensitivity experiments replacing each contributor by its expected value.</p> <p>The matrices of Spearman correlation for year 2100 for all experiments are called: <br> SpearmanCorr_namelist*_*.txt</p> <p>The Table*.txt files contain the data used to make tables of sea level percentiles in the paper.</p> <p>The pdf files contain the figures used in the paper and additional pannels not included in the paper.</p> <p>Reference:<br> Le Bars, D. (2018, March 8). Uncertainty in sea level rise projections due to the dependence between contributors. http://doi.org/10.17605/OSF.IO/UVW3S</p>
Body size convergence in Sturnira - R Code and supporting data
<p>R Code and supporting data for: Co-occurrence and character convergence in two Neotropical bats. Journal of Mammalogy</p>
Supporting dataset for: Repository optimisation & techniques to improve discoverability and web impact : an evaluation
<p>This dataset supports the working paper, "Repository optimisation & techniques to improve discoverability and web impact : an evaluation", currently under review for publication and available as a preprint at: <a href="https://doi.org/10.17868/65389/">https://doi.org/10.17868/65389/</a>. </p> <ul> <li>Macgregor, G. (2018). <em>Repository optimisation techniques to improve discoverability and web impact: an evaluation</em>. (pp. 1-13). Glasgow: University of Strathclyde [Strathprints repository]. Available: <a href="https://doi.org/10.17868/65389/">https://doi.org/10.17868/65389/</a></li> </ul> <p>The dataset comprises a single OpenDocument Spreadsheet (.ods) format file containing seven data sheets of data pertaining to COUNTER compliant usage statistics, search query traffic from Google Search Console, web traffic data for Google Analytics and Google Scholar, and usage statistics from IRStats2. All data relate to the EPrints repository, Strathprints, based at the University of Strathclyde.</p>
Supporting Information for "An empirical modification of the force field approach to describe the modulation of galactic cosmic rays close to Earth in a broad range of rigidities"
<p>This supporting information provides the Data Set S1 used to produce Fig. 6 in <strong>"An empirical modification of the force field approach to describe the modulation of galactic cosmic rays close to Earth in a broad range of rigidities"</strong> (Gieseler et al., 2017). It can be used to calculate the rigidity-dependent solar modulation potential <span class="math-tex">\(\phi(P)\)</span> for monthly intervals from 1973-2017 following Eq. 10 in Gieseler et al. (2017).</p> <p>If you use this data, please refer to and cite <strong>BOTH</strong> following publications:</p> <ul> <li>Gieseler, J., B. Heber, and K. Herbst, <em>An empirical modification of the force field approach to describe the modulation of galactic cosmic rays close to Earth in a broad range of rigidities</em>, J. Geophys. Res., 2017 (doi:10.1002/2017JA024763).</li> <li>Usoskin, I. G., G. A. Bazilevskaya, and G. A. Kovaltsov, <em>Solar modulation parameter for cosmic rays since 1936 reconstructed from ground-based neutron monitors and ionization chambers</em>, J. Geophys. Res., 2011 (doi:10.1029/2010JA016105).</li> </ul> <p>This data set contains the solar modulation potential values in MV for monthly intervals from 1973-2017 derived from the proton proxies IMP-8 He and ACE/CRIS C (Phi_pp), and from Usoskin et al. (2011) as provided by http://cosmicrays.oulu.fi/phi/phi.html (Phi_Uso11). The uncertainties of Phi_pp are given in column 4, those of Phi_Uso11 are 26 MV for the observed period. The LIS used to calculate the modulation potentials is that from Burger et al. (2000) as given by Usoskin et al. (2005).</p> <p>Column 1: Fractional year (start of interval)<br> Column 2: Month<br> Column 3: Phi_pp /MV<br> Column 4: Uncertainty of Phi_pp /MV<br> Column 5: Phi_Uso11 /MV</p> <p>Data also available at http://www.ieap.uni-kiel.de/et/ag-heber/cosmicrays</p>
Supporting Jupyter Python notebook for "A new class of efficient randomized benchmarking protocols"
<p>Python notebook containing the code used to generate the data for figure 2 in the appendix of "A new class of efficient randomized benchmarking protocols" (arXiv:1806.02048).</p>
Supplementary material for "Palaeo-demographic modelling supports a population bottleneck during the Pleistocene-Holocene transition in Iberia"
<p>This Supplementary material encompasses the archaeological radiocarbon dates for the Iberian Peninsula analyzed in the article “<em>Palaeo-demographic modelling supports a population bottleneck during the Pleistocene-Holocene transition in Iberia</em>". Specifically:</p> <ul> <li>‘<strong>PALEODEM_dataset.csv</strong>’ provides the dataset in comma-separated format. Contains the compiled raw data of each radiocarbon date used by every analytical procedure.</li> <li>‘<strong>PALEODEM_sites.xlsx</strong>’ is a spreadsheet containing the basic information of each archaeological site analyzed in this work. The columns include ID_site, name of Archaeological site, n<sup>o</sup> of <sup>14</sup>C dates, n<sup>o</sup> of assemblages, Regional Unit of correspondence, and bibliographical Reference.</li> <li>‘<strong>REFERENCES for PALEODEM_sites.docx</strong>’ is a text archive that collects the bibliographical references listed in the above mentioned ‘Reference’ column, that have been the main sources of archaeological information.</li> </ul>
EOSCpilot D3.7 Updates to Policy Supporting Services: supporting material for the Monitor
<p>This is to support use cases of the Monitor section of EOSCpilot D3.7 Updates to Policy Supporting Services.</p>
Supporting data and codes for: A new biological species in the Mercurialis annua polyploid complex: functional divergence in inflorescence morphology, hybrid sterility and possible introgression
<p>This GitHub repository includes R codes and datasets for the paper: A new biological species in the Mercurialis annua polyploid complex: functional divergence in inflorescence morphology, hybrid sterility and possible introgression</p>
Datasets and Supporting Materials for the IPIN 2017 Competition Track 3 (Smartphone-based, off-site)
<p>This package contains the datasets and supplementary materials used in the IPIN 2017 Competition (Sapporo, Japan).</p> <p><strong>Contents:</strong></p> <ol> <li>Track3_LogfileDescription_and_SupplementaryMaterial.pdf: Description of the logfiles and supplemental materials.</li> <li>Track3_TechnicalAnnex.pdf: Technical annex describing the competition </li> <li>01-Logfiles: This folder contains a subfolder with the 25 training logfiles, a subfolder with the 9 validation logfiles, and a subfolder with the 7 blind evaluation logfiles as provided to competitors.</li> <li>02-Supplementary_Materials: This folder contains the Matlab/Octave parser, the raster maps, the visualization of the training routes and the location of the BLE beacon (CAR) and some Wi-Fi APs (UJIUB).</li> <li>03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 505 evaluation points. The ground truth is also provided in MatLab format and as a CSV file. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT includes the closest timestamp matching the timing provided by competitors.</li> </ol> <p><strong>Please, cite the following works when using the datasets included in this package:</strong></p> <ul> <li>Torres-Sospedra, J.; Jiménez, A. R.; Moreira, A.; Lungenstrass, T.; Lu, W.-C.; Knauth, S.; Mendoza-Silva, G.M.; Seco, F.; Perez-Navarro, A.; Nicolau, M.J.; Costa, A.; Meneses, F.; Farina, J.; Morales, J.P.; Lu, W.-C.; Cheng, H.-T.; Yang, S.-S.; Fang, S.-H.; Chien, Y.-R. and Tsao, Y. Off-line evaluation of mobile-centric Indoor Positioning Systems: the experiences from the 2017 IPIN competition Sensors Vol. 18(2), 2018. <a href="http://dx.doi.org/10.3390/s18020487">http://dx.doi.org/10.3390/s18020487</a></li> <li>Jimenez, A.R.; Mendoza-Silva, G.M.; Seco, F.; Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2017 Competition Track 3 (Smartphone-based, off-site). <a href="http://dx.doi.org/10.5281/zenodo.2823924">http://dx.doi.org/10.5281/zenodo.2823924</a> </li> </ul> <p><strong>Additional information can be found at:</strong></p> <ul> <li><a href="http://evaal.aaloa.org/2017/2017-competition-home">http://evaal.aaloa.org/2017/2017-competition-home</a></li> <li><a href="http://indoorloc.uji.es/ipin2017track3/">http://indoorloc.uji.es/ipin2017track3/</a></li> </ul> <p><strong>For any further questions about the database and this competition track, please contact: </strong></p> <ul> <li>Joaquín Torres (<a href="mailto:jtorres@uji.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">jtorres@uji.es</a>) Institute of New Imaging Technologies, Universitat Jaume I, Spain. </li> <li>Antonio R. Jiménez (<a href="mailto:antonio.jimenez@csic.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">antonio.jimenez@csic.es</a>) Center of Automation and Robotics (CAR)-CSIC/UPM, Spain. </li> </ul> <p><br> </p>
Datasets and Supporting Materials for the IPIN 2018 Competition Track 3 (Smartphone-based, off-site)
<p>This package contains the datasets and supplementary materials used in the IPIN 2018 Competition (Nantes, France).</p> <p><strong>Contents:</strong></p> <ol> <li>IPIN2018_CallForCompetition_v2.1: Call for competition including the technical annex describing the competition </li> <li>01-Logfiles: This folder contains a subfolder with the 22 training logfiles, a subfolder with the 15 (13 + 2) validation logfiles, and a subfolder with the 1 blind evaluation logfile as provided to competitors.</li> <li>02-Supplementary_Materials: This folder contains the Matlab/octave parser, the raster maps, the vector maps and the visualization of the training routes.</li> <li>03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 99 evaluation points. The ground truth is also provided in MatLab format and as a CSV file. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT includes the closest timestamp matching the timing provided by competitors.</li> <li>03-Evaluation_alternative: This folder contains the alternative scripts used to calculate the competition metric, the 75th percentile on the 99 evaluation points. This version is compatible with MatLab and Octave and does not require any toolbox. In some cases, the differences in the reported errors might be around 10 cm with respect to the script used in the competition. The ground truth is also provided in MatLab format and as a CSV file. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT includes the closest timestamp matching the timing provided by competitors.</li> </ol> <p><strong>Please, cite the following works when using the datasets included in this package:</strong></p> <ul> <li>Jimenez, A.R.; Mendoza-Silva, G.M.; Ortiz, M.; Perez-Navarro, A.; Perul, J.; Seco, F.; Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2018 Competition Track 3 (Smartphone-based, off-site). <a href="http://dx.doi.org/10.5281/zenodo.2823964">http://dx.doi.org/10.5281/zenodo.2823964</a></li> <li>Renaudin, V.; Ortiz, M.; Perul, J.; Torres-Sospedra, J.; Ramón Jimenez, A.; Pérez-Navarro, A.; Martín Mendoza-Silva, G.; Seco, F.; Landau, Y.; Marbel, R.; Ben-Moshe, B.; Zheng, X.; Ye, F.; Kuang, J.; Li, Y.; Niu, X.; Landa, V.; Hacohen, S.; Shvalb, N.; Lu, C.; Uchiyama, H.; Thomas, D.; Shimada, A.; Taniguchi, R.; Ding, Z.; Xu, F.; Kronenwett, N.; Vladimirov, B.; Lee, S.; Cho, E.; Jun, S.; Lee, C.; Park, S.; Lee, Y.; Rew, J.; Park, C.; Jeong, H.; Han, J.; Lee, K.; Zhang, W.; Li, X.; Wei, D.; Zhang, Y.; Park, S. Y.; Park, C. G.; Knauth, S.; Pipelidis, G.; Tsiamitros, N.; Lungenstrass, T.; Pablo Morales, J.; Trogh, J.; Plets, D.; Opiela, M.; Shih-Hau Fang Tsao, Y.; Chien, Y.-R.; Yang, S.-S.; Ye, S.-J.; Ali, M. U.; Hur, S.; and Park, Y. Evaluating Indoor Positioning Systems in a Shopping Mall: The Lessons Learned from the IPIN 2018 Competition IEEE Access Vol. 7, pp. 148594-148628, 2019. http://dx.doi.org/10.1109/ACCESS.2019.2944389</li> </ul> <p><strong>Additional information can be found at:</strong></p> <ul> <li><a href="http://evaal.aaloa.org/2018/call-for-competitions">http://evaal.aaloa.org/2018/call-for-competitions</a></li> <li><a href="http://ipin-conference.org/2018/ipincompetition/">http://ipin-conference.org/2018/ipincompetition/</a></li> </ul> <p><strong>For any further questions about the database and this competition track, please contact: </strong></p> <ul> <li>Joaquín Torres (<a href="mailto:jtorres@uji.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">jtorres@uji.es</a>) Institute of New Imaging Technologies, Universitat Jaume I, Spain. </li> <li>Antonio R. Jiménez (<a href="mailto:antonio.jimenez@csic.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">antonio.jimenez@csic.es</a>) Center of Automation and Robotics (CAR)-CSIC/UPM, Spain. </li> </ul>
Phenome-wide association studies across large population cohorts support drug target validation
<p>Summary-level data generated by Genomics plc as presented in:<br> Diogo, D. et al. Phenome-wide association studies across large population cohorts support drug target validation. Nat. Commun. 9, 4285 (2018). https://doi.org/10.1038/s41467-018-06540-3</p> <p>If you have any questions or comments regarding these files, please contact Genomics plc at <a href="mailto:research@genomicsplc.com">research@genomicsplc.com</a></p> <p>NOTES<br> -----------------------------<br> These analyses were carried out using the interim UK Biobank imputation data release. Analyses were restricted to a subset of "white-British" unrelated samples with a maximum sample size of 112,337 individuals. </p> <p>Case control phenotypes were defined based on categorical datafields as listed in the accompanying file. <br> Quantitative phenotypes were either rank-normalised before analysis, or beta/se values were standardised after analysis using the variance of the phenotype. The normalisation value is indicated in the accompanying file.<br> <br> All analyses included Age at assessment, sex, genotyping chip, and 10 principal components as covariates. </p> <p>We used plink1.9 linear/logistic regression as appropriate. For chromosome X variants males were treated as having 0 or 2 alternative alleles. </p> <p>The results are not adjusted for genomic control.</p> <p>DATA FILE CONTENT DESCRIPTION<br> -----------------------------<br> CHR - Chromosome<br> SNP - Variant rsID<br> ALT - Alternative allele (effect allele)<br> REF - Reference Allele (non-effect allele)<br> BP - Position in base pairs (b37, 1-based)<br> NMISS - Number of samples with non-missing genotypes<br> BETA - Effect size (log odds ratio or standardised effect size)<br> SE - Standard error<br> P - P-value<br> F_MISS - genotype missing rate<br> P_hwe - Hardy-weinberg p-value<br> MAF - ALT allele frequency</p>
[Dataset] Software Process Line as an Approach to Support Software Process Reuse: a Systematic Literature Review
<p>Dataset of a Systematic Literature Review on Software Process Line as an Approach to Support Software Process Reuse</p> <p>Please, read README.txt file before go through dataset.</p>
Supporting data for "Quantifying the Strength of a Salt Bridge by Neutron Scattering and Molecular Dynamics"
<p>Supporting data for the following published paper: Mason, Jungwirth, Duboué-Dijon, 2019, JPhysChemLett, 10, 3254-3259</p> <p>Contains both data from neutron scattering measurements and input simulation files necessary for reproduction of the work.</p>
Support Data Assets for Figures and Tables in gmd-12-3449-2019 (Geosci. Model Dev.)
<p>Support datasets for "VISIR-1.b: ocean surface gravity waves and currents for energy-efficient navigation", by G. Mannarini and L. Carelli (Geosci. Model Dev. paper, https://doi.org/10.5194/gmd-12-3449-2019):</p><ul><li><strong> graphs_gmd-2018-292.tar.gz</strong></li></ul><p>Graphs for running VISIR computations </p><ul><li><strong>figTab_DATA_v3.tar.gz:</strong></li></ul><p>Source data for Figures and Tables</p>
FADN data on the support under the CAP delimited for LAU2 (NUTS2) regions in the EU Member States for the 2007-2013 programming period
<p> </p> <p>Ready to use FADN dataset on the support under the CAP in 2007-2013 delimited for LAU2 (NUTS2) regions in the EU Member States.</p> <p>Investigation of the interaction between Cohesion and Rural Policies requires analysing comparable data. However, the CAP data are usually collected at the national level. The FADN database is the only data source for analysing the impact of agricultural policy instruments on the economic situation of farms. However, the regional breakdown of FADN data in some countries does not correspond to the NUTS2 breakdown for which cohesion policy is defined.</p> <p>The provided FADN data delimitation uses a methodology that takes into account the range of impact and features specific to a given region. Because the research shows a very strong relationship between the amount of support under the CAP and the number and size of farms on a given area, this criterion was used to delimit FADN data for particular LAU2 (NUTS2) regions, while maintaining the allocation to individual measures.</p> <p>FADN data aggregated (averaged) to the level of FADN regions and economic size classes were used. Each FADN region has been assigned a corresponding NUTS2 region (or regions) according to the classification in 2010 in which the full census of the farm structure survey was carried out. The delimitation of FADN data to NUTS2 regions was based on weights constructed on the basis of Eurostat data on utilised agricultural area and number of holdings in 2010. In each economic size class, each FADN region consisted of the sum of the NUTS2 regions weighted by the utilised agricultural area. The result of each FADN variable was the sum of its values in each economic size class, weighted by the total number of holdings in each class.</p> <p>This database has served as a basis for two articles, one validating the assumptions of the NUTS2 (LAU) delimitation of the FADN regions and the other using the database to compare synergies and trade-offs between cohesion policy and the common agricultural policy.</p>
Zambezi dataset to "WHAT-IF: an open-source decision support tool for water infrastructure investment planning within the Water-Energy-Food-Climate Nexus"
<p>This is the dataset used in the HESS publication "<a href="https://www.hydrol-earth-syst-sci-discuss.net/hess-2019-167/">WHAT-IF: an open-source decision support tool for water infrastructure investment planning within the Water-Energy-Food-Climate Nexus</a>"</p> <p>The dataset describes the water-energy-food nexus of the Zambezi River Basin used as input to the <a href="https://github.com/RaphaelPB/WHAT-IF">WHAT-IF model</a>.</p> <p>The file Data_Organization.pdf, summarizes the available data. For more info look at the <a href="https://www.hydrol-earth-syst-sci-discuss.net/hess-2019-167/">publication</a> and/or <a href="https://github.com/RaphaelPB/WHAT-IF">Github</a>.</p>
Project files provided as supporting information to the manuscript "A deep learning approach to the structural analysis of proteins"
<p><strong>README file to the project files provided as supporting information to the manuscript “A deep learning approach to the structural analysis of proteins”</strong></p> <p>Dec. 30, 2018</p> <p>Authors: Marco Giulini and Raffaello Potestio</p> <p>==================================</p> <p>The dataset contains the following files:</p> <p> </p> <p>- datasets.zip: archive containing five .csv files, namely:</p> <p> - decoys_cm.csv : all the data for 10728 protein decoys, training set</p> <p> - evaluation_cm.csv : all data for 146 proteins in the evaluation set</p> <p> - random_CG.csv : 1200 Coulomb matrices. 100 CG models for each protein with 120 amino acids</p> <p> - 1e5g_centered_sphere.csv : 100 CG models in which the central atoms in 1e5g are not removed</p> <p> - 1e5g_random_sphere.csv : 10 CG models for 10 different (random) locations for the sphere that includes atoms that have to be retained. 100 CG models in total</p> <p> </p> <p>- decoys_labels.lab containing the labels associated to the 10728 decoys present in the training set</p> <p>- evaluation_labels.lab containing the labels associated to the 146 pdb files in the evaluation set</p> <p>- random_CG_labels.lab containing the labels associated to the 6 proteins with 120 amino acids</p> <p>- network_development_training: a python script that performs cross validation and full training of the model</p> <p>- saved_networks.zip FOLDER containing 10 networks: the architecture is included in .json files while weight parameters are inside .hs files</p> <p> </p> <p>- pdb_files.zip FOLDER containing the PDB files that have been employed in the project, namely:</p> <p> - pdb_files_len100 : pdb files with 100 amino acids</p> <p> - pdb_files_len101-110 : pdb files with a number of amino acids between 101 and 110</p> <p> - decoys : decoys of length 100 extracted from the above folder: name syntax == PDBNAME_decoy_STARTRES_ENDRES.pdb</p> <p> EXAMPLE 6gsp.pdb will give rise to 6gsp_decoy_0_100.pdb , 6gsp_decoy_1_101.pdb , 6gsp_decoy_2_102.pdb , 6gsp_decoy_3_103.pdb , 6gsp_decoy_4_104.pdb</p> <p> - pdb_files_len100 : 6 pdb files with 120 amino acids</p> <p> </p>
The global renewable power support policy dataset
<p>The global renewable power support policy dataset was compiled by Sarah Hafner (Anglia Ruskin University, United Kingdom) and Johan Lilliestam (Institute for Advanced Sustainability Studies (IASS), Germany) in February-July 2017 and completed during 2017. The work was led by Johan Lilliestam but each author gathered half of the data. The data was formatted and checked for internal consistency by Tim Tröndle, IASS.</p> <p>All non-commercial users are allowed to use and manipulate our data, but are required to give appropriate attribution. Hence, <strong>please cite this data as</strong>:</p> <p>Hafner, S. & Lilliestam, J. (2019): <em>The global renewable power support dataset</em>. Institute for Advanced Sustainability Studies (IASS) & Anglia Ruskin University, Potsdam & Cambridge. Doi: https://doi.org/ 10.5281/zenodo.3371375.</p> <p><strong>If you are interested in contributing</strong> to and further developing the dataset: please contact Johan Lilliestam (IASS Potsdam).</p> <p>The search was done in publically available sources, including but not limited to the IEA renewables policy database, res-legal.eu, Worldbank data, as well as data from the responsible national ministries.</p> <p>Our data holds information on 10 specific policy instruments explicitly dedicated to the support for expansion of renewable electricity generation 1990-2016; some instruments, including taxation of non-renewables or emission trading, affect other sectors than renewable power, but are mentioned in their original policy description to also be dedicated to increasing renewable power. Our data concerns national policy measures, but ignores policies enacted on higher (e.g. EU-level in Europe) or lower (e.g. state-level policies in Canada, USA) political levels. For example, the “no support” entry for the United Arab Emirates indicates that there were no national-level policies: all policies were, in this case, emirate-specific.</p> <p>The data exists in two versions: one version readable for humans (RE_policies_fullglobal.xlsx) and for each instrument type as .csv. The information in the two versions is identical and differs only in the way it is displayed.</p> <p><strong>Please refer to the metadata file for a detailed description of the dataset and the data categories.</strong></p>
Supporting transcriptomic data and code for: "Rapid and dose-dependent Natural Killer (NK) cell modulation and cytokine correlations after human rVSV-ZEBOV Ebolavirus vaccination"
<p>The counts_NK.csv file contains gene expression data (counts) for genes in the Ion Ampliseq human Gene expression kit panel. Data were obtained from whole blood RNA. Subjects were vaccinated with a high dose of the rVSV-ZEBOV vaccine against Ebola virus disease in the Geneva clinical trial.</p> <p>The Descriptive_Table_NK_2.csv contains descriptive data of the subjects for differential expression analysis.</p> <p>The NK_analysis_code.R file contains the code used for analysis.</p>
Supporting Material for article "The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences"
<p>This data set is the Supporting Material referred to in the Supplementary Data for the article "The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences" (Drysdale, et al.) submitted for publication in April 2019.</p> <p> </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.