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.
1,445
datasets available to search
ShareScore release 0.9.0
Dataset results
1,445 results for “Distances”
Forced Continuance Intention Model of Distance Online Teaching during CoVID-19 outbreak at University of Maribor, Slovenia [Project documentation]
<p>The outbreak of COVID -19 forced most universities into distance education. Three didacticians and researchers from the University of Maribor, Slovenia: Kosta Dolenc, Mateja Ploj Virtič and Andrej Šorgo formed a self-initiated initiative project group during the COVID -19 epidemic and started the project with the working title: The Side Effects of Forced Online Distance Education (FODE).</p> <p>The aim of the first study, conducted during the first wave of the epidemic in March 2020, was to investigate the response of university teachers to the new situation. The project documentation provided for the Forced Online Distance Teaching (FODT) consist of:</p> <ul> <li>abstract,</li> <li>instrument,</li> <li>copy of the descriptive statistics, and</li> <li>SPSS dataset.</li> </ul>
NTMSS Distance Matrices
<p>Distance matrices relating to New Testament textual variation. Each distance matrix gives distances between various New Testament witnesses (e.g. Greek manuscripts, versions, patristic citations, lectionaries).</p>
Even short‐distance dispersal over a barrier can affect genetic differentiation in Gyraulus, an island freshwater snail
<p>Supplementary dataset for a published paper, "Saito T., Sasaki T., Tsunamoto Y., Uchida S., Satake K., Suyama Y., <em>et al.</em> (2022). Even short‐distance dispersal over a barrier can affect genetic differentiation in <em>Gyraulus</em> , an island freshwater snail. <em>Freshwater Biology</em> <strong>67</strong>, 1971–1983. <a href="https://doi.org/10.1111/fwb.13990">https://doi.org/10.1111/fwb.13990</a>"</p>
Repeated and multivariate measures of perceived distance
<p>A dataset of repeated measures of distance perception at physical distances of 7, 8, 9, 10, and 11 meters. The data are also multivariate, with five dependent measures of distance perception. This is a 5 (physical distance) x 5 (dependent measure) within-participants design with a sample size of 46. Note data is missing for 15 trials due participant and experimenter errors.</p> <p>The csv file has 230 rows and 7 columns.</p> <p><em>Subject</em>: Unique identifier for each participant. <br> <em>Physical Distance</em>: Physical distance from the participant to the target cone, in meters.<br> <em>Blindwalk Away</em>: Participants put on the blindfold after viewing the target. Next, participants took one step to the left and turned 180 degrees to face the opposite direction. Participants were instructed to walk forward until they had walked the original distance to the target. <br> <em>Blindwalk Toward</em>: Participants put on the blindfold after viewing the target. Next, participants walked forward until they thought they had reached the target cone.<br> <em>Triangulated BW:</em> Participants put on the blindfold after viewing the target. Next, participants turned right 90 degrees and walked<br> forward 5 meters. The experimenter told participants when to stop walking. Finally, participants turned to face toward the target and walked forward two steps.<br> <em>Verbal:</em> Participants stated the distance between the target cone and themselves, in feet and inches. <br> <em>Visual Matching:</em> An experimenter stood next to the target cone and walked away from the cone in a straight line that was <br> perpendicular to the extent between the target and the participant. Participants instructed the experimenter to stop walking when they thought that the distance between the target and the experimenter was equal to the target distance.</p> <p> </p> <p> </p>
Data from "Source characterization of the declared North Korean Nuclear Tests from regional distance coda wave spectral ratios"
<p>Results of the coda spectral ratio analysis presented in Delbridge et al. (2022).</p> <p>network_average_ratios.csv - a csv file which contains each of the network average ratios calculated from all channels and stations for each event pair and component.</p>
Data from: Male long-distance migrant turned sedentary; The West European pond bat (Myotis dasycneme) alters their migration and hibernation behaviour
<p>Winter survey data, temperature data and mark recapture data of <em>Myotis dasycneme</em>. This study aimed to better understand the migration, mating and hibernation choices of the pond bat.</p> <p> </p> <p>The study area covered the whole of the Netherlands, Belgium and East Frisia (northwest Germany). We defined two study periods, data collected between 1930 and 1980 (Sluiter and van Heerdt) and data between 1980 and 2015 (Haarsma). All available mark and recovery data (ringing) of both the historical and recent migration research were digitized. Observations include location and date of capture, species, sex and ring number. The latest observations in the recent dataset (Haarsma) also include biometric measurements (forearm length, body mass) and information about age and reproductive status. These biometric measurements show that male pond bats are on average smaller and lighter than females (body mass (g)/ forearm length (mm) females: 18.9/47.1, males: 16.4/46.4). The dataset shows changes in the fat mass of both sexes during a year.</p> <p>This study also compares migration data with winter monitoring survey data. We selected winter roosts with three or more records of three or more pond bats in one or both of the study periods. Only data from sites with long-term data series (from the hibernacula in the Dutch provinces of Zuid-Holland, Gelderland and Limburg) were used to analyse trends and annual abundance. Our selection included 59 limestone mines in the province of Limburg and 16 WOII bunkers in Gelderland and 38 in Zuid-Holland. We divided the sites into 'core' and 'satellite' sites depending on the timing of first colonization.</p> <p> </p> <p><strong>Bunker limestone mine microclimate</strong></p> <p> </p> <p>Radiation temperature: radiation temperature of the wall, measured with a non-contact infrared thermometer</p> <p>How many bats: the group size of each bat/ group of bats observed, categorized as alone and group.</p> <p>Where: the hanging location of the observed bat, categorized as hidden (in crevice) or free (free on ceiling or wall)</p> <p>Date: date of the observation</p> <p>Xy-coord: The coordinates of the entrance of the bunker or limestone mine. The RD (Rijks-Driehoek) system is the coordinate system used by the Dutch geographical service.</p> <p>Type: Bunker or limestone</p> <p>Location description: description of the name of the site</p> <p> </p> <p><strong>Bunker monitoring core and satellite</strong></p> <p> </p> <p>Date: date</p> <p>Winter: the period between September and April is defined as the winter of the year starting in January.</p> <p>Location description: description of the name of the site</p> <p>N of pond bats: total number of observed pond bats</p> <p>Province: the province</p> <p>Type: hibernacula categorized as a core or satellite site, sites occupied by pond bats since 1977 and 1997 respectively.</p> <p>XY-coord: The coordinates of the entrance of the bunker or limestone mine. The RD (Rijks-Driehoek) system is the coordinate system used by the Dutch geographical service.</p> <p> </p> <p> </p> <p><strong>Supporting information (as referenced in the published paper, hence also available with plos one)</strong></p> <p><br> <strong>S1 Fig. The range of the West European pond bat population (TIF).</strong> The shaded areas indicate the<br> areas where the bulk of the surveys were carried out.</p> <p><br> <strong>S2 Fig. The distribution of the pond bat in Europe (country boundaries are only indicative) (JPG).</strong> Within the whole range of the species distribution seven groups can be separated.<br> A The Netherlands, Belgium and Northwest Germany (~the West European population),<br> B Jutland Peninsula,<br> C Central European lakelands,<br> D The Baltic States,<br> E Ural Mountains (hibernacula),<br> F Volga Valley (summer nurseries),<br> G Hungary and Romania.<br> <br> <strong>S3 Fig. The distribution of hibernacula used by the western pond bat population (TIF). </strong>These are<br> sites with three or more records of pond bats in one or both study periods. We identified four<br> roost categories: Roosts which have been used ever since 1900 (= green squares), roosts used<br> only between 1900–1980 (= open black squares), roosts occupied after 1980 (= purple circles),<br> roosts occupied after 1997 (= blue asterisks). Detailed maps, all with the same enlargement, of<br> the clusters in the provinces of Zuid-Holland (1), Gelderland (1) and Limburg (3) are provided.<br> <br> </p> <p><strong>S1 Table. Summary of the average weight of pond bats over the study period.</strong> The weight is averaged per week. The table gives average weight of females, males both adults and juveniles.</p> <p> </p> <p>Avg weight: average weight of pond bats of each sex, in a certain week</p> <p>Sex: male of female</p> <p>Week number: number of the week</p> <p>Age: juvenile (or young of the year). Defined as the from birth until the onset of first hibernation. Subadult or sexual immature, defined as individuals with no signs of (past) reproductive activity. Adult or sexual mature, defined as all individuals with signs of (previous) reproductive activity.</p> <p>N observations: number of observations within each subset.<br> </p> <p><strong>S2 Table. Mark and recapture data from the historical dataset.</strong><br> </p> <p>Ringnumber: the label of the ring</p> <p> Sex: male or female</p> <p>capture date: date of capture</p> <p>capture location: description of capture location</p> <p>x y coordinate: The coordinates of the capture location in RD. The RD (Rijks-Driehoek) system is the coordinate system used by the Dutch geographical service.</p> <p>recapture date: date of recapture</p> <p>recapture location: description of recapture location</p> <p>x y coordinate: The coordinates of the recapture location in RD. The RD (Rijks-Driehoek) system is the coordinate system used by the Dutch geographical service.</p> <p> </p> <p><strong>S3 Table. Mark and recapture data from the recent dataset.</strong></p> <p> </p> <p>Same dataset as the historical set, but now including age (see definition used in S1)<br> <br> </p>
ESA Cryo-TEMPO - Northern hemisphere land/ocean flag and distance to coast at resolution of 250 m.
<p>Land/Ocean flag nd distance to coast at high spatial resolution (250m) in the northern hemisphere. The land/ocean flag is computed from merged Open Street Map and Natural Earth land polygons. The shapefiles were rasterized and reprojected to northern hemisphere using gdal. All land mass with the exception of Greenland are based on Open Street Map. Distance to coast was computed with gdal (gdal_proximity.py). </p> <p>The file format is netCDF-4 and the datafile contains two variables (land_ocean_flag & distance_to_coast). The coordinate reference system of the variables is defined by EPSG:6931 (WGS 84 / NSIDC EASE-Grid 2.0 North) and the bounds of the data set are supplied as xc and yc variables in the data file. </p> <p>The file is used in the ESA CryoSat-2 Thematic Products (Cryo-TEMPO) Polar Ocean and Sea Ice products in the northern hemisphere. </p> <p> </p>
BLE RSSI vs distance
<p>The data set has been used in SmartCPS experiment within <a href="https://www.fed4fire.eu">Fed4Fire+</a> continuous call for studying RSSI (of BLE devices sending advertisements) depending on the distance to the scanning nodes.</p> <p>The data set contains location information transferred over Bluetooth Low Energy from a single BLE tag and registered by 5 nodes from <a href="https://doc.lab.cityofthings.eu/wiki/Nodes">CityLab testbed</a>: <a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node9">Node 9</a>, <a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node33">Node 33</a>, <a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node34">Node 34</a>, <a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node35">Node 35</a>, <a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node36">Node 36</a></p> <p>The data is stored in CSV format (with the following columns):<br> <strong>node</strong> - id of the CityLab testbed node<br> (<strong>node_lat</strong>, <strong>node_lon</strong>) - GPS coordinates of the node<br> (<strong>tag_lat</strong>, <strong>tag_lon</strong>) - GPS coordinates of the tag<br> <strong>ts</strong> - timestamp (milliseconds since the UNIX epoch: 1 January 1970)<br> <strong>rssi -</strong> received signal strength indication</p>
CLDF dataset derived from Galucio et al.'s "Lexical Distances within the Tupian Linguistic family" from 2015
<p>Cite the source of the dataset as:</p> <blockquote> <p>Galucio, Ana Vilacy and Meira, Sérgio and Birchall, Joshua and Moore, Denny and Gabas Júnior, Nilson and Drude, Sebastian and Storto, Luciana and Picanço, Gessiane and Rodrigues, Carmen Reis. (2015). Genealogical relations and lexical distances within the Tupian linguistic family. Boletim do Museu Paraense Emílio Goeldi. Ciências Humanas, 10(2), 229-274. https://dx.doi.org/10.1590/1981-81222015000200004</p> </blockquote>
Dataset for the article EDM-GNSS distance comparison at the EURO5000 calibration baseline: preliminary results
<p>GNSS RINEX observation files for the observation campaign used in the article "EDM-GNSS distance comparison at the EURO5000 calibration baseline: preliminary results" by Kinga Wezka, Luis García-Asenjo, Dominik Próchniewicz, Sergio Baselga, Ryszard Szpunar, Pascual Garrigues, Janusz Walo and Raquel Luján, Journal of Applied Geodesy https://doi.org/10.1515/jag-2022-0049. The work leading to this paper was performed within the 18SIB01 GeoMetre project of the European Metrology Programme for Innovation and Research (EMPIR). This project has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme, funder ID: 10.13039/100014132. Raquel Luján acknowledges the funding from the Programa de Ayudas de Investigación y Desarrollo (PAID-01-20) de la Universitat Politècnica de València.</p>
Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks
<p>Residue-residue distance information is useful for predicting tertiary structures of protein monomers or quaternary structures of protein complexes. Many deep learning methods have been developed to predict intra-chain residue-residue distances of monomers accurately, but few methods can accurately predict inter-chain residue-residue distances of complexes. We develop a deep learning method CDPred (i.e., Complex Distance Prediction) based on the 2D attention-powered residual network to address the gap. Tested on two homodimer datasets, CDPred achieves the precision of 60.94% and 42.93% for top L/5 inter-chain contact predictions (L: length of the monomer in homodimer), respectively, substantially higher than DeepHomo’s 37.40% and 23.08% and GLINTER’s 48.09% and 36.74%. Tested on the two heterodimer datasets, the top Ls/5 inter-chain contact prediction precision (Ls: length of the shorter monomer in heterodimer) of CDPred is 47.59% and 22.87% respectively, surpassing GLINTER’s 23.24% and 13.49%. Moreover, the prediction of CDPred is complementary with that of AlphaFold2-multimer.</p>
TRANSIT long-distance multimodal trips model results - a Spanish case study
<p>The files downloaded present the results per scenario considered obtained with the agent-based model developed for the assessment of the Intermodal Timetable Synchronisation solution proposed in the scope of the TRANSIT project (<a href="https://www.transit-h2020.eu/">https://www.transit-h2020.eu/</a>).</p> <p>An agent-based modelling framework called <a href="https://github.com/StefanoPenazzi/jtap/tree/main">J-TAP</a> has been developed and put at work to implement a Spanish long-distance multimodal trips model. The enhanced version of J-TAP used in this work can be found on a <a href="https://github.com/NommonSolutionsAndTechnologies/jtap">github repository</a>.</p> <p>The case study is focused on modelling the long-distance travel patterns of the residents in the Valencia (Spain) area. The destinations considered include the whole of Spain. The period under study is a full year from March 2019 to February 2020 (both inclusive).The files are structured in three different scenarios:</p> <ol> <li><strong>CS01 - Baseline</strong>. The current state of the network is considered and the actual long-distance travel patterns are obtained.</li> <li><strong>CS02 - HSR connection with Madrid-Barajas airport</strong>. The long-distance travel patterns are modelled with hard measures, the high-speed rail is connected to Madrid-Barajas airport.</li> <li><strong>CS03 - HSR connection with Madrid-Barajas airport and timetable synchronisation</strong>. The effects of the timetable synchronisation are modelled.</li> </ol> <p>Each scenario includes the following files:</p> <ul> <li>ctapModelParameters. A folder containing all the information extracted from the <a href="https://neo4j.com/product/graph-data-science/?utm_program=emea-prospecting&utm_source=google&utm_medium=cpc&utm_campaign=emea-search-offers&utm_adgroup=dynamic&utm_content=dynamic&utm_placement=&utm_network=g&gclid=Cj0KCQiAwJWdBhCYARIsAJc4idAo4CEi9lU8TXwmBym8MNHpEIZHPBs3x_4phxbu76y1XKbYlFoZCjIaAiGhEALw_wcB">neo4j</a> graph database created to model the multimodal network and the agents. J-TAP contains packages that simplify network creation in the graph database. This information is stored in .json files (e.g., "Os2DsTravelCostParameter.json" contains the generalised cost for each OD pair and transport mode, "AttractivenessParameter.json" contains the attractiveness by destination, activity, time of the year and agent, etc.). The solver included in the J-TAP framework uses this information to calculate the agents plans.</li> <li>population.json. The result of the J-TAP optimisation. It includes the fitness value for each agent plan evaluated during the J-TAP execution. The best plan for each agent is selected as the plan performed by the agent. An agent plan includes: <ul> <li>activities - Sequence of activities.</li> <li>locations - Sequence of locations</li> <li>ts - Initial time of the activity</li> <li>te - Final time of the activity</li> </ul> </li> <li>LinkTimeFlow.csv. It is obtained after processing the previous file. It contains the number of agents using each link in the network (i.e., road, rail, air and cross links) in each time interval. The first column represents the link id and the rest of columns indicates the number of agents in each interval.</li> </ul> <p>The J-TAP simulation framework is explained in detail in TRANSIT's deliverable <a href="http://www.nommon-files.es/transit/TRANSIT-D5.1_Modelling_Framework_v02.00.00.pdf">D5.1. TRANSIT Modelling and Simulation Framework</a> and the complete description of the case studies and scenarios tested is included in TRANSIT's deliverable <a href="http://www.nommon-files.es/transit/TRANSIT-D6.1_Assessment_of_Intermodal_Concepts_00.02.00.pdf">D6.1. Impact Assessment of New Intermodal Concepts and Passenger Information Services: Conclusions and Recommendations</a>.</p> <p>Thank you for downloading the dataset! It would be very helpful if you share your view on the data show with us. </p>
Dataset: "Traffic Noise at Moderate Levels Affects Cognitive Performance: Do Distance-Induced Temporal Changes Matter?"
<p>This repository contains the dataset presented in "Traffic Noise at Moderate Levels Affects Cognitive Performance: Do Distance-Induced Temporal Changes Matter?" (https://doi.org/10.3390/ijerph20053798) as well as the SPSS syntax used for the statistical evaluation. Additionally, calibrated binaural recordings of the evaluated stimuli are provided as 32 bit .wav files, the values stored in those files correspond to pascals.</p>
Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks
<p>Residue-residue distance information is useful for predicting tertiary structures of protein monomers or quaternary structures of protein complexes. Many deep learning methods have been developed to predict intra-chain residue-residue distances of monomers accurately, but few methods can accurately predict inter-chain residue-residue distances of complexes. We develop a deep learning method CDPred (i.e., Complex Distance Prediction) based on the 2D attention-powered residual network to address the gap. Tested on two homodimer datasets, CDPred achieves the precision of 60.94% and 42.93% for top L/5 inter-chain contact predictions (L: length of the monomer in homodimer), respectively, substantially higher than DeepHomo’s 37.40% and 23.08% and GLINTER’s 48.09% and 36.74%. Tested on the two heterodimer datasets, the top Ls/5 inter-chain contact prediction precision (Ls: length of the shorter monomer in heterodimer) of CDPred is 47.59% and 22.87% respectively, surpassing GLINTER’s 23.24% and 13.49%. Moreover, the prediction of CDPred is complementary with that of AlphaFold2-multimer.</p>
Dataset for Charge collection efficiency, underlying recombination mechanisms, and the role of electrode distance of vented ionization chambers under ultra-high dose-per-pulse conditions
<p>Dataset for paper: Kranzer et al., <a href="https://www.sciencedirect.com/journal/physica-medica">Physica Medica</a> <a href="https://www.sciencedirect.com/journal/physica-medica/vol/104/suppl/C">Volume 104</a>, December 2022, Pages 10-17</p> <p><a href="https://doi.org/10.1016/j.ejmp.2022.10.021">https://doi.org/10.1016/j.ejmp.2022.10.021</a></p>
Morphological cladogenesis and terminal dwarfing in extinct Late Miocene through Pliocene menardiform globorotalids: New complementary data to «Evolutionary prospection in the Neogene planktic foraminifer Globorotalia menardii and related forms from ODP Hole 925B (Céara Rise, western tropical Atlantic): evidence for gradual evolution superimposed by long distance dispersal ?, Swiss J. Palaeontology, 135:205-248»
<p>A complementary morphometric data set is provided to the study of Knappertsbusch (2016) about the shell evolution of menardiform globorotalids (Neogene planktic foraminifera) at ODP Hole 925B from Céara Rise in the the western tropical Atlantic. The new measurements confirm splitting of extinct <em>Globorotalia multicamerata</em> from the <em>G. menardii</em> stock via the intermediate form <em>G. limbata</em> between about 6 Ma to 5 Ma ago. After splitting both <em>G. limbata</em> and <em>G. multicamerata</em> show gradual divergence from <em>G. menardii</em> in several shell parameters illustrating morphological cladogenesis. Between 2.88 Ma and 2.59 Ma the same parameters show a concerted trend towards reduced values indicating pre-extinction dwarfing. A comparison with published literature data of Delta<sup>18</sup>O trends between species, that populated the mixed layer (<em>Globigerinoides sacculifer</em>) and the thermocline layer (<em>Neogloboquadrina dutertrei</em>) at this location during those times suggests, that both divergence and subsequent dwarfing trends were probably the results of changes in upper watermass stratification.</p> <p>The complementary data set is provided in six zipped archives APPENDIX A, B, C, D, E and F (zipped with free software 7-Zip 22.00 (x64), 2022-06-15 from 1999-2022 Igor Pawlow), together with a description of the data in file Report_925B_suppl_1.pdf.</p>
Driven distance by commuters to the center of major and minor cities in Europe
<p>The two files contains respectively a map of driving distances around major and minor cities in Europe. The distance is given by range of 5 km from 5 to 45 km in a resolution of 100m*100m. The encoding is uint8.</p> <p>The metropolitan areas considered in this study are those from this study of <a href="https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Archive:European_cities_%E2%80%93_the_EU-OECD_functional_urban_area_definition">functional urban area definition</a>.</p> <p>The corresponding geofile is available at <a href="https://circabc.europa.eu/ui/group/8eafb630-27cf-4fae-a4c2-f1234a123fd7/library/59bfa33a-8f4b-413d-8552-ac0a93ad7e5f/details">this adress</a>.</p> <p>Most of the cities with more than 50'000 habitants are included, some cities have been removed or added depending on the local context.</p> <p>As the geofile contains only polygones of the metropolitan areas and not the city point, the points form cities with the number of habitants have been dowloaded from <a href="http://www.naturalearthdata.com/downloads/110m-cultural-vectors/110m-populated-places/">Natural Data</a> and then selected by comparison with the polygones.</p> <p>Cities with more than 20'000 have also been selected as commuting center for rural areas. Only the cities outside the metropolitan commuting zones are included.</p> <p>The calculation of driving distances around centers have been performed with <a href="https://openrouteservice.org/">OpenRouteService</a> (personal API key required, free of charge).</p> <p> </p> <p> </p>
Euclide, the crow, the wolf and the pedestrian: distance metrics for linguistic typology - Dataset
<p>This repository contains the distance matrices and code for the paper "Euclide, the crow, the wolf and the pedestrian: distance metrics for linguistic typology"</p>
Dataset of Detection Distances to Small Bodies using Spacecraft Cameras
<p>The dataset contains the detection distances to small bodies (in kilometres) considering three different spacecraft camera setups for the full list of known objects by the Minor Planet Center catalogue (https://www.minorplanetcenter.net). A separate ASCII file has been created per each considered phase angle.</p> <p>The generation of the dataset as well as the simulation settings are detailed in the following paper:</p> <p>Franzese, Hein, Modelling Detection Distances to Small Bodies Using Spacecraft Cameras, <em>Modelling</em> <strong>2023</strong>, <em>4</em>(4), 600-610; <a href="https://doi.org/10.3390/modelling4040034">https://doi.org/10.3390/modelling4040034</a></p> <p>The columns of the dataset are as follows:</p> <ol> <li>Object: The object's numerical identifier.</li> <li>MPC Designation: The object designation of the Minor Planet Center.</li> <li>Name: The object name, if available.</li> <li>HP Cam & rp: Object detection distance in km considering the high-performance camera and the object at perihelion</li> <li>HP Cam & ra: Object detection distance in km considering the high-performance camera and the object at aphelion</li> <li>MP Cam & rp: Object detection distance in km considering the medium performance camera and the object at perihelion</li> <li>MP Cam & ra: Object detection distance in km considering the medium performance camera and the object at aphelion</li> <li>LP Cam & rp: Object detection distance in km considering the low-performance camera and the object at perihelion</li> <li>LP Cam & ra: Object detection distance in km considering the low-performance camera and the object at aphelion.</li> </ol> <p>Note that the detection distances refer to the following phase angles: 0 deg, 15 deg, 30 deg, 60 deg, and 90 deg.</p>
Distance matrix to newly build motorways in Slovakia
<p>This dataset includes distances from villages and cities within (mostly) central Slovakia to various motorway exits. Focus is on the R1 motorway. It includes car distances both in kilometers and in seconds. The dataset is intended for analysis on socio-economic development of settlements as a result of motorway expansion.</p> <p>Four distances to four different stages of building the motorways in Slovakia are present:</p> <ol> <li>useky1: link to Trnava built in 2000</li> <li>useky2: segments built between 1990 and 2000</li> <li>useky3: newest built segments of motorway (built after 2003, major parts in 2011)</li> <li>useky4: D1 motorway</li> </ol> <p>interactive map at https://epsilon.sk/mapa-dostupnosti/dialnice.html</p> <p> </p> <p>The dataset was created using OpenStreetMap data with OSRM routing engine.</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.