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7,195 results for “SET”
King Louie: DBMS Availability Evaluation Data Sets
<p>These data sets provide all availability measurements as accompanying material for the research paper <strong><em>King</em> </strong><em><strong>Louie: Reproducible Availability Benchmarking of Cloud-hosted DBMS</strong> </em>that is presented in the 35th ACM/SIGAPP Symposium on Applied Computing (SAC ’20), March 30-April 3, 2020, Brno, Czech Republic.</p> <p> </p> <p> </p>
Shear Wave Splitting and Mantle Flow beneath Alaska Data Set
<p>Entire data set for the (under review) publication "Shear Wave Splitting in Alaska."</p> <p>McPherson_S1_Station_Info is a table that contains the following columns (with header row): Station Name, Network, Latitude (Deg), Longitude (Deg). This is a table of all the seismic stations in Alaska and western Canada that we downloaded data from. Only stations that were active from Jan 1, 2010, to Aug 18, 2017 are included.</p> <p>McPherson_S2_Event_Info is a table that contains the following columns (with header row): Julian Date, Origin Time, Latitude (Deg), Longitude (Deg), Depth (km), Magnitude (Mw). This is a table of all the seismic events that occurred between Jan 1, 2010, to Aug 18, 2017 within the distance range 80 to 140 degrees from a station, over moment magnitude 5.</p> <p>McPherson_S3_Results_Info is a table that contains the following columns (with header row): Station Name, Back Azimuth (Deg), Distance (Deg), Fast Direction (Deg), Lower Bound (Deg), Upper Bound (Deg), Time Difference (sec), Lower Bound (sec), Upper Bound (sec), Julian Date, Origin Time. This table contains all of the minimum energy method (Silver & Chan, 1991) results that are displayed in Figures 4, 6-12 of the paper under review.</p> <p>McPherson_S4_Nulls_Info is a table that contains the following columns (with header row): Station Name, Back Azimuth (Deg), Distance (Deg), Julian Date, Origin Time. This tables contains all the null results displayed in Figure 5 of the paper under review.</p>
Mowgli: DBMS Performance & Scalability Evaluation Data Sets
<p>These data sets contain the performance and scalability evaluation data created by the <a href="https://omi-gitlab.e-technik.uni-ulm.de/mowgli/getting-started">Mowgli</a> framework for Apache Cassandra and Couchbase, operated on a private Openstack and the Amazon EC2 cloud.</p>
Aalto-1/RADMON data set 2017/2018
<p><strong>General information:</strong></p> <ul> <li>For a description of the Aalto-1 mission, see Kestilä et al. (2013) and Praks et al. (2018)</li> <li>For the RADMON instrument and its calibration, see Peltonen et al. (2014) and Oleynik et al. (2020)</li> <li>For this dataset description, see Gieseler et al. (2020)</li> <li>If you use this data set, please give a proper citation to Gieseler et al. (2020). e.g.: <ul> <li><em>Gieseler, J., Oleynik, P., Hietala, H., Vainio, R., Hedman, H.P., Peltonen, J., Punkkinen, A., Punkkinen, R., Säntti, T., Hæggström, E., Praks, J., Niemelä, P., Riwanto, B., Jovanovic, N., Mughal, M.R., 2020. Radiation Monitor RADMON aboard Aalto-1 CubeSat: First results. Advances in Space Research, 66 (1), 52–65</em>, <em><a href="https://doi.org/10.1016/j.asr.2019.11.023">doi:10.1016/j.asr.2019.11.023</a>, <a href="https://arxiv.org/abs/1911.07586">arXiv:1911.07586</a></em></li> </ul> </li> <li><strong>For omnidirectional averaged fluxes use combination of multiple measurements intervals (e.g., for the same coordinates) </strong><strong>with total integration times of at least 30 minutes</strong><strong> (for more details, see Gieseler et al., 2020)</strong></li> <li>Data gaps in the fluxes are indicated as empty entries, while zeros denote zero counts in the measurement interval</li> <li>Empty entries in AACGM coordinates and L parameter indicate not-defined values</li> </ul> <p> </p> <p><strong>Data file columns description:</strong></p> <ul> <li>time: end time of measurement interval in YYYY-MM-DD HH:MM:SS</li> <li>e2 - e4: integral electron fluxes in (cm<sup>2</sup> s sr)<sup>-1</sup> (see below for energies)</li> <li>i1 - i4: integral proton fluxes (cm<sup>2</sup> s sr)<sup>-1</sup> (see below for energies)</li> <li>i5: differential proton flux (cm<sup>2</sup> s sr MeV)<sup>-1</sup> (see below for energies)</li> <li>interval: integration time of measurement interval (usually 15s)</li> <li>lat: geographic latitude of Aalto-1 </li> <li>lon: geographic longitude of Aalto-1 </li> <li>altitude: altitude above sea level (R<sub>E</sub> = 6371.0 km) of Aalto-1 (in m)</li> <li>mag_lat: geomagnetic latitude of Aalto-1</li> <li>mag_lon: geomagnetic longitude of Aalto-1</li> <li>aacgmv2_lat: altitude-adjusted corrected geomagnetic latitude of Aalto-1</li> <li>aacgmv2_lon: altitude-adjusted corrected geomagnetic longitude of Aalto-1</li> <li>aacgmv2_mlt: magnetic local time based on the AACGM coordinates</li> <li>L: McIlwain L parameter</li> </ul> <p>See Gieseler et al. (2020) for descriptions of the different variables and its derivations.</p> <p> </p> <p><strong>Channel details:</strong></p> <p>Electrons:</p> <ul> <li>e2<br> cutoff energy: 1.51 ± 0.1 MeV<br> geometric factor: 0.0108 ± 0.0005 cm<sup>2</sup> s sr</li> <li>e3<br> cutoff energy: 3.1 ± 0.2 MeV<br> geometric factor: 0.0160 ± 0.0005 cm<sup>2</sup> s sr</li> <li>e4<br> cutoff energy: 6.0 ± 0.7 MeV<br> geometric factor: 0.0119 ± 0.0008 cm<sup>2</sup> s sr</li> </ul> <p>Protons:</p> <ul> <li>i1<br> cutoff energy: 10.4 ± 0.3 MeV<br> geometric factor: 0.0228 ± 0.0004 cm<sup>2</sup> s sr</li> <li>i2<br> cutoff energy: 18.5 ± 0.7 MeV<br> geometric factor: 0.0256 ± 0.0009 cm<sup>2</sup> s sr</li> <li>i3<br> cutoff energy: 23.7 ± 1.8 MeV<br> geometric factor: 0.0219 ± 0.0011 cm<sup>2</sup> s sr</li> <li>i4<br> cutoff energy: 29 ± 4 MeV<br> geometric factor: 0.0187 ± 0.0014 cm<sup>2</sup> s sr</li> <li>i5<br> effective energy: 42 ± 5 MeV<br> differential geometric factor: 0.783 ± 0.09 cm² cm<sup>2</sup> s sr MeV</li> </ul> <p>Note: The channel i5 is the only one yielding differential flux (instead of integral flux), with an energy range of 40-80 MeV, and an effective energy of 42 MeV. Its differential geometric factor is given in units of (cm<sup>2</sup> sr MeV)<sup>-1</sup>.</p> <p>General:</p> <ul> <li>See OIeynik et al. (2020) for more details, e.g., on the derivation of bowtie cutoff energies and possible proton contamination in electron channels</li> <li>Energies are bowtie cutoff energies for integral channels except for proton channel i5 for which the effective energy is given</li> <li>Uncertainties for energies and geometric factors are 95% (two sigmas)</li> <li>All channels yield integral fluxes in units of (cm<sup>2</sup> s sr)<sup>-1</sup> except for proton channel i5 which gives differential flux in (cm<sup>2</sup> s sr MeV)<sup>-1</sup></li> </ul>
Test data sets: File A
I am getting an "Entity too large" error when trying to use this file as File A, with File A taxonID: EOL-000002128790. The Encyclopedia of Life (EOL, eol.org) aggregates biodiversity information from more than 400 sources and provides access to the data through taxon pages, visual query and application programming interfaces. Scientific names are essential elements of the data integration infrastructure, but their shortcomings as key identifiers are well documented (Patterson et al., 2016). Complex automated workflows and continuous manual curation are required to address idiosyncrasies of source taxonomies, variation in data quality, and conflicting taxonomic opinions. To achieve a harmonized taxonomic view of EOL content, names from data sources are mapped to a dynamic reference hierarchy ([see current version here](<p></p>https://opendata.eol.org/dataset/tram-807-808-809-810-dh-v1-1/resource/00adb47b-57ed-4f6b-8f66-83bfdb5120e8)) using an algorithm that leverages canonical name strings, hierarchical information (ancestry, descendants), taxonomic ranks, synonym data, and author strings. Names that cannot be associated with a reference taxon are still accessible, but their unmapped status excludes them and any associated content from certain core EOL functions. For more information about the EOL taxonomy, see [EOL Dynamic Hierarchy](<p></p>https://eol.org/docs/eol-dynamic-hierarchy)
Test data sets: File B
I am getting an "Entity too large" error when trying to use this file as File B, with File B taxonID: BJ5N3. The Encyclopedia of Life (EOL, eol.org) aggregates biodiversity information from more than 400 sources and provides access to the data through taxon pages, visual query and application programming interfaces. Scientific names are essential elements of the data integration infrastructure, but their shortcomings as key identifiers are well documented (Patterson et al., 2016). Complex automated workflows and continuous manual curation are required to address idiosyncrasies of source taxonomies, variation in data quality, and conflicting taxonomic opinions. To achieve a harmonized taxonomic view of EOL content, names from data sources are mapped to a dynamic reference hierarchy ([see current version here](<p></p>https://opendata.eol.org/dataset/tram-807-808-809-810-dh-v1-1/resource/00adb47b-57ed-4f6b-8f66-83bfdb5120e8)) using an algorithm that leverages canonical name strings, hierarchical information (ancestry, descendants), taxonomic ranks, synonym data, and author strings. Names that cannot be associated with a reference taxon are still accessible, but their unmapped status excludes them and any associated content from certain core EOL functions. For more information about the EOL taxonomy, see [EOL Dynamic Hierarchy](<p></p>https://eol.org/docs/eol-dynamic-hierarchy)
Test data sets: Test data set for "Request Entity Too Large" exception
The Encyclopedia of Life (EOL, eol.org) aggregates biodiversity information from more than 400 sources and provides access to the data through taxon pages, visual query and application programming interfaces. Scientific names are essential elements of the data integration infrastructure, but their shortcomings as key identifiers are well documented (Patterson et al., 2016). Complex automated workflows and continuous manual curation are required to address idiosyncrasies of source taxonomies, variation in data quality, and conflicting taxonomic opinions. To achieve a harmonized taxonomic view of EOL content, names from data sources are mapped to a dynamic reference hierarchy ([see current version here](<p></p>https://opendata.eol.org/dataset/tram-807-808-809-810-dh-v1-1/resource/00adb47b-57ed-4f6b-8f66-83bfdb5120e8)) using an algorithm that leverages canonical name strings, hierarchical information (ancestry, descendants), taxonomic ranks, synonym data, and author strings. Names that cannot be associated with a reference taxon are still accessible, but their unmapped status excludes them and any associated content from certain core EOL functions. For more information about the EOL taxonomy, see [EOL Dynamic Hierarchy](<p></p>https://eol.org/docs/eol-dynamic-hierarchy)
Test data sets: COLTest
The Encyclopedia of Life (EOL, eol.org) aggregates biodiversity information from more than 400 sources and provides access to the data through taxon pages, visual query and application programming interfaces. Scientific names are essential elements of the data integration infrastructure, but their shortcomings as key identifiers are well documented (Patterson et al., 2016). Complex automated workflows and continuous manual curation are required to address idiosyncrasies of source taxonomies, variation in data quality, and conflicting taxonomic opinions. To achieve a harmonized taxonomic view of EOL content, names from data sources are mapped to a dynamic reference hierarchy ([see current version here](<p></p>https://opendata.eol.org/dataset/tram-807-808-809-810-dh-v1-1/resource/00adb47b-57ed-4f6b-8f66-83bfdb5120e8)) using an algorithm that leverages canonical name strings, hierarchical information (ancestry, descendants), taxonomic ranks, synonym data, and author strings. Names that cannot be associated with a reference taxon are still accessible, but their unmapped status excludes them and any associated content from certain core EOL functions. For more information about the EOL taxonomy, see [EOL Dynamic Hierarchy](<p></p>https://eol.org/docs/eol-dynamic-hierarchy)
Test data sets: Amoebozoa Test
The Encyclopedia of Life (EOL, eol.org) aggregates biodiversity information from more than 400 sources and provides access to the data through taxon pages, visual query and application programming interfaces. Scientific names are essential elements of the data integration infrastructure, but their shortcomings as key identifiers are well documented (Patterson et al., 2016). Complex automated workflows and continuous manual curation are required to address idiosyncrasies of source taxonomies, variation in data quality, and conflicting taxonomic opinions. To achieve a harmonized taxonomic view of EOL content, names from data sources are mapped to a dynamic reference hierarchy ([see current version here](<p></p>https://opendata.eol.org/dataset/tram-807-808-809-810-dh-v1-1/resource/00adb47b-57ed-4f6b-8f66-83bfdb5120e8)) using an algorithm that leverages canonical name strings, hierarchical information (ancestry, descendants), taxonomic ranks, synonym data, and author strings. Names that cannot be associated with a reference taxon are still accessible, but their unmapped status excludes them and any associated content from certain core EOL functions. For more information about the EOL taxonomy, see [EOL Dynamic Hierarchy](<p></p>https://eol.org/docs/eol-dynamic-hierarchy)
Options for AdapterRemoval v2.2.1 for different sets of specimens
<p>Settings for AdapterRemoval program options --minquality and --minadapteroverlap varied between sets of specimens run on different computing environments as shown in the table.</p>
OME-Zarr hiPSC 3D immunofluorescence images, tiny test set
<p><em>This dataset is intended to be used for automated testing of OME-Zarr processing.</em></p> <p> </p> <p>Example dataset of human induced pluripotent stem cells, imaged at 40x magnification with a Yokogawa CV7000. This is a tiny subset of a larger experiment intended as a test dataset for the <a href="https://fractal-analytics-platform.github.io/">Fractal platform</a> and others experimenting with OME-Zarrs.</p> <p>1 Channel is included:</p> <ul> <li>C01: DAPI, nuclear stain</li> </ul> <p>It is generated from this raw data: <a href="../records/8287221">https://zenodo.org/records/8287221</a></p> <p>This dataset contains 2 Z levels for 2 field of views for this 1 channel, as well as (manually adjusted) metadata files from the Yokogawa CV7000. The data was acquired in the Pelkmans lab in August 2020.</p> <p>The images have been processed using Fractal, the workflow is attached as a json file. It ran with fractal-server==2.3.6, fractal-client==2.0.1, fractal-web==1.4.0 and fractal-tasks-core==1.2.1.</p> <p>Two versions of the OME-Zarr are added here: A 3D version with both Z planes. And a 2D version (MIP of the Z-planes) which also contains label images from cellpose segmentation, measurements and output ROI tables.</p>
Integration of data sets from different sources for modeling gender violence and perception of insecurity
<p>The dataset is composed of three distinct files which aggregate processed data derived from open datasets of three cities: Dublin, San Francisco, and Valencia. The data has been mapped to a grid of 25m² for Valencia and 50m² for Dublin and San Francisco. The respective files are named DATA_ES_VLC.csv, DATA_IE_DUB.csv, and DATA_US_SFO.csv. Additionally, there is a dataset for tweets named DATA_TWT.csv, which contains tweets collected through web scraping and analysed using natural language processing (NLP) algorithms and neural networks. The aim is to identify and classify tweets that discuss gender-based violence in the city of Valencia. Another file, MAP_ES_VLC.csv, includes points collected during various mapathons conducted by the Polytechnic University of Valencia campus for a science project aimed at identifying potentially insecure locations.</p>
Ultra-high Resolution Land Use Data Set of Typical Villages in Northeastern Tibetan Plateau
<p>This dataset was collected by a research team during a field investigation in the Hehuang Valley of Qinghai Province from July to August 2022. Using the DJI Mavic2pro equipped with a Hasselblad L1D-20c camera, 55 typical villages were selected in the Hehuang Valley and over 4600 aerial photographs were obtained using drone photogrammetry technology as raw data. Using AgisfphotoScan 1.25 software to synthesize orthophoto images with a spatial resolution of 0.05m. The vector data of human settlement boundaries in villages was extracted through visual interpretation. Based on the object-oriented human-machine interaction interpretation method, 55 typical village land use datasets in 2022 were obtained (including forests, grasslands, forest land, cultivated land, water bodies, roads, unused land, and building land, totaling 8 categories). By establishing 1050 sample points and using confusion matrix analysis, it was found that the overall accuracy of the dataset was 96.86%, with a Kappa coefficient of 0.95. It can accurately reflect the spatial form, land use composition, and surrounding environment of typical villages. Aerial photographs all have longitude, latitude, and altitude information, providing ultra-high resolution data sources for village spatial structure analysis, land use mapping, and analysis work, effectively assisting in the improvement of human housing and rural revitalization strategies.</p>
Dataset on consumers' perception of different types of sustainability levies, Swiss agriculture and farmers and willingness to choose suboptimal potatoes in different settings
<p><em><span>This dataset includes survey data from 481 Swiss consumers. Data were collected in the German-speaking parts of Switzerland in February and March 2024. The survey includes three independent main parts. </span></em></p> <p><em><span>In a first part, we collected qualitative and quantitative data on participants’ perception of Swiss agriculture and farmers. Specifically, participants’ trust in crop and livestock production farmers and their perceived knowledge about production methods and their affect towards farmers was assessed. </span></em></p> <p><em><span>In a second part, we collected quantitative data on participants’ preference for different sustainability levies. For this, six different products were used (i.e., fresh/processed vegetables, dairy, and meat). For each of these six products, participants were shown four levy options from which they had to choose the one that they found most appealing. For vegetables, the options were: (A) reduction of risks related to plant protection products, (B) more support for local farmers, (C) support for environmental sustainability, and (D) sustainability projects in general. For the animal products, option (A) was an increase in animal welfare, whilst options (B), (C) and (D) were the same as for the vegetable products.</span></em></p> <p><em><span>In a third part, we collected qualitative and quantitative data on participants preferences for suboptimal or optimal potatoes. Here, a 2 × 2 experimental design (setting × information) was used. This means that participants were presented with either a supermarket or farm shop setting and with or without food waste information. Participants then chose between two potatoes: optimal potato A, suboptimal potato B, or neither. Both potatoes were equally expensive.</span></em></p>
cif files and VASP files for Li6PS5X crystals and a set of isovalent analogs
<p>This dataset is meant as supplementary material to the research paper titled "The Devil in the Details: Lessons from Li6PS5X for Robust High-Throughput Workflows" authored by Asif Iqbal Bhatti, Sandeep Kumar, Catharina Jaeken, Michael Sluydts, Danny E.P. Vanpoucke and Stefaan Cottenier. At the time of writing this description, the manuscript is under peer review (publication info will be added here once published). </p> <p>The data set contains cif files as well as VASP input and output for all crystals that are being discussed in this paper.</p>
Data set for risk management in the allocation of vehicles to tasks in transport companies using a heuristic algorithm
<p>The purpose of this dataset is to enable the replication of the research results presented in the article: Izdebski, M. (2023). Risk management in the allocation of vehicles to tasks in transport companies using a heuristic algorithm. Archives of Transport, 67(3), 139-153. https://doi.org/10.5604/01.3001.0053.7463 - published online: 2023-09-30, which discusses the allocation problem of vehicles to tasks, taking into account risk issues.</p> <p>Dataset contains:</p> <ul> <li>Readme.txt: description of the dataset</li> <li>InputData.xlsx: Contains the input data used in the model</li> <li>DistributionFit.xlsx: Compliance testing and distribution parameters for road accidents of any type and collision-type</li> <li>OutputAssignment.xlsx: Results of assignment and alghoritm tests</li> </ul> <p>The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).<br>Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022.<br> E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).<br>- The Chinese part of the project is funded by National Natural Science Foundation of China.<br>- The Swedish part of the project is funded by Swedish Energy Agency.<br>- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).</p>
Data set of detected atmospheric rivers, cyclones, and fronts within the region of 75°N – 82.5°N, 0°E – 30°E and at Ny-Ålesund (Svalbard) for 2017 – 2021
<p>This data set contains times when atmospheric rivers, cyclones, or fronts have been detected within the broader region of 75°N – 82.5°N, 0°E – 30°E and specifically at Ny-Ålesund, Svalbard (78.92308 °N, 11.92108 °E) for the years 2017 to 2021. To this end, the detection methods, as described in Lauer et al. (2023), have been applied to the hourly-resolved ERA5 reanalysis (Hersbach et al., 2020) data. </p> <p>Data set overview</p> <p>Each file contains the times (year, month, day, hour in UTC) when the corresponding weather system, i.e. atmospheric river, cyclone and front, has been detected within the region of 75°N – 82.5°N, 0°E – 30°E. The last column indicates if the weather system was located also over Ny-Ålesund Svalbard (78.92308 °N, 11.92108 °E). </p>
INDICATORS TO EVALUATE THE LABOUR INSERTION OF PEOPLE WITH DISABILITIES IN CONVENTIONAL COMPANIES IN SPAIN: QUANTITIATIVE DATA SET OF DELPHI STUDY (PHASE 2 AND PHASE 3)
<p><span>The level of labor integration of people with disability (PwD) is notably lower than that of people without disabilities. In order to evaluate the success of the labor market integration of people with disabilities, it is necessary to establish a series of indicators that go beyond hiring rates. Hence, the objective of this study is to develop a list of indicators with their specified individual weight that will serve to evaluate the success of the labor market insertion of PwD in conventional companies. </span></p> <p><span>Methodology: </span></p> <p><span>The Delphi method was used. </span></p> <p><span>PHASE 1</span></p> <p><span>In Phase 1, an open-ended questionnaire was distributed to 48 human resources and disability experts. <span><br></span></span></p> <p><span>PHASE 2</span></p> <p><span>Based on the theoretical dimensions obtained, a list of 52 indicators was drawn up and the experts were asked to evaluate the importance of each item using a scale of 0 to 10 points. In addition, in this second questionnaire, they were encouraged to propose improvements in the final wording of the items, as well as in the relevance and denomination of the dimensions into which they had been grouped. No suggestions were received to modify the wording or to incorporate additional items.</span></p> <p><span>PHASE 3</span></p> <p><span>Once the scores of all the participants had been collected, a third questionnaire was sent out with the aim of achieving a statistical consensus within the group of experts. In this questionnaire, each panel member was informed of their degree of agreement or disagreement in relation to the group as a whole, without revealing the identity of the other participants. In other words, each participant was provided with information on the average rating of the group and their own initial rating (from Phase 2) of each of the 52 indicators, offering them the option to modify their response if they considered it appropriate. If they chose to change their assessment, they were asked to justify their reasons.</span></p> <p><span><span>To assess the possible convergence of opinion, the change in the responses received in the third phase with respect to the second phase was analyzed. We examined whether there had been variations in the scores given by the experts in the second phase once the group's mean ratings had been received. For this purpose, the “proportion of experts” statistic was used to verify that the average value of the responses in this third phase was within a range from [-0.5 to +0.5], compared to the average value of the scores in the second phase. In the case of non-convergence of opinion, this methodology allows for as many rounds as necessary until convergence is achieved. </span></span></p> <p><span><span>THIS DATA SET COLLECT THE ANSWERS OF THE EXPERTS OF PHASE 2 AND PHASE 3.</span></span></p>
Data set for "Cell class-specific long-range axonal projections of neurons in mouse whisker-related somatosensory cortices"
<p>Data set for: Liu Y, Bech P, Tamura K, Délez LT, Crochet S, Petersen CCH (2024) Cell class-specific long-range axonal projections of neurons in mouse whisker-related somatosensory cortices. eLife 13: RP97602. https://doi.org/10.7554/eLife.97602</p> <p>There are 3 files in this upload:</p> <p>1. The file named "2024_Liu_eLife.pdf" is the Open Access pdf of the online publication in eLife.</p> <p>2. The file named "Liu_anatomy_data_code.zip" (~35 GB) is a zipped version of a folder "Liu_anatomy_data_code" (~111 GB), which contains the anatomical data analysed in the study along with the Python codes used to generate the published figures 1-7 and their associated figure supplements. </p> <p>3. The file named "Liu_function_data_code.zip" (~10 GB) is a zipped version of a folder "Liu_function_data_code" (~35 GB), which contains the functional data analysed in the study along with the Python codes used to generate the published figure 8 and its associated figure supplement. </p> <p>After unzipping, the Python codes should run as a Jupyter notebook (anatomy .ipynb code) or Python code (function .py code) in Anaconda.</p>
WP5.1 Stochastic and Empirical Multi-Hazard Event Sets for Europe: Winterstorm Stochastic Event Set for Europe (ISO3166 code)
<p>Stochastic event set of winter storms based on PRIMAVERA European winter windstorm event set: https://zenodo.org/record/6492182<br> Footprints for each model provided as *.csv file in xyz-format ("_footprints" suffix) and resampled as annualy maximum windspeeds for a 10.000-year stochastic run ("_10kyears" suffix). Winterstorm windspeed return period maps provided as *.tif at 5, 10, 20, 50 and 100-year periods. Windspeeds given as maximum surface windspeeds per event in "km/h".</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.