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Bibliographic dataset based on Scientometrics, including provenance information compliant with the OpenCitations Data Model
<p>The dataset contains bibliographical information about scholarly works in the journal Scientometrics only if the DOI is known. The data was extracted via Crossref. It is a temporal dataset in which provenance information and change-tracking have been managed by adopting the OpenCitations Data Model. Moreover, the dataset contains information on all the cited academic works. Journals, bibliographic resources, and authors always appear unambiguously, without duplicates. Finally, heuristics have been applied to recover the DOI of the cited works in case Crossref did not provide such information.</p> <p>The dataset is distributed as two journal files, one for the data and one for the provenance, readable via the triplestore Blazegraph. There are 4,960,087 data triples and 19,348,027 provenance triples, which corresponds to 1,134,545 entities and 2,696,689 snapshots. Therefore, on average, each entity has two snapshots. Among the data, there are 231,217 agent roles, 221,602 responsible agents, 206,003 bibliographic resources, 142,472 citations, 141,555 bibliographical references, 108,112 identifiers, and 83,584 resource embodiments.</p> <p>The code to generate and modify such collections is available at <a href="https://doi.org/10.5281/zenodo.5579754">https://doi.org/10.5281/zenodo.5579754</a>. </p>
Data for: Machine learning for predicting environmental mobility based on retention behaviour
<p>This repository contains the data and supplementary information for the paper: "Machine learning for predicting environmental mobility based on retention behaviour".</p>
Simulated data from abmAnimalMovement: An R package for simulating animal movement using an agent-based model
<p>Contained here are the data simulated as part of the manuscript: "abmAnimalMovement: An R package for simulating animal movement using an agent-based model" that can be found at: https://github.com/BenMMarshall/abmAnimalMovement (and archived at: https://doi.org/10.5281/zenodo.6951937).</p> <p>- BADGER_locations.csv: A csv file that contains the realised locations of the example badger simulation, where each row is equal to a timestep. Columns include: timestep, the timestep as an integer; x, the x coordinate of the animal; y, the y coordinate of the animal; sl, the step length between locations used during the simulation; sl_rescale the rescale factor required to return step lengths back to the input scale; ta, turning angle between locations in degrees; behave, the behavioural mode the animal was in at a given timestep; chosen, the location chosen out of the number of options available; destination_x and destination_y the point the animal was attracted to at that time (note exploratory behaviour is not subject attraction).</p> <p>- BADGER_options.csv: A csv file that contains the options available to the example badger simulation over the entire simulation duration, where each row is equal to an option repeated for each timestep. Columns include: timestep, the timestep as an integer; oall_x, and oall_y show the x and y coordinates of all the options available to an animal at a timestep; oall_steplengths are the step lengths from the current location compared to all the options.</p> <p>- completelist.RDS: This RDS file contains a list object of length three, where the full simulation outputs from each three examples are stored. Each species slot contains the “locations” dataframe (see description of locations.csv), the "options" dataframe (see description of options.csv), and a nested list containing all the "inputs" used to generate the simulated results (split into subsections: inputs_basic that contains inputs linked to simulation duration and intensity, inputs_destination that contains inputs linked to destination and attraction aspects, inputs_movement that contains inputs linked to movement capacity and behavioural switching, inputs_cycle that contains inputs linked to activity cycling, inputs_layerSeed that contains the environmental matrices and seed). A fourth object is returned called "others" that captures all other outputs, mainly used internally for debugging and checking.</p> <p>- KINGCOBRA_locations.csv: A csv file that contains the realised locations of the example king cobra simulation, where each row is equal to a timestep. The file structure follows the same as the BADGER_options.csv file.<br>KINGCOBRA_options.csv: A csv file that contains the options available to the example king cobra simulation over the entire simulation duration, where each row is equal to an option repeated for each timestep. The file structure follows the same as the BADGER_options.csv file.</p> <p>- VULTURE_locations.csv: A csv file that contains the realised locations of the example vulture simulation, where each row is equal to a timestep. The file structure follows the same as the BADGER_locations.csv file.<br>VULTURE_options.csv: A csv file that contains the options available to the example vulture simulation over the entire simulation duration, where each row is equal to an option repeated for each timestep. The file structure follows the same as the BADGER_locations.csv file.</p> <p>- eg_landscapedata_completelist.RDS: This RDS file contains the landscape matrices required for recreating the simulated outputs described in the manuscript named above. It is a list of three objects ("shelter", "forage", "movement"), each a numeric matrix of equal size, with values describing the quality of each landscape characteristic.</p> <p>- argument_table.csv: Descriptive table of the simulation inputs used in the walk-through manuscript.</p>
Annual land cover maps of Germany based on Sentinel-2 MSI Level 3A (WASP) data
<p>Overview:<br> This annual land cover product is available for the years 2016, 2019, 2020, 2021 for the whole of Germany. It was generated based on Sentinel-2 MSI L3A WASP Data provided by DLR (https://geoservice.dlr.de/data-assets/4hcq6dgkj648.html). For a complete description of the classification procedure please refer to<br> Riembauer, G.; Weinmann, A.; Xu, S.; Eichfuss, S.; Eberz, C.; Neteler, M.: Germany-wide Sentinel-2 based land cover classification and change detection for settlement and infrastructure monitoring. In: Proceedings of the 2021 conference on Big Data from Space (doi:10.2760/125905), 2021.</p> <p>Source data:</p> <ul> <li>Satellite data <ul> <li>German Aerospace Center (DLR): Sentinel-2 MSI - Level 3A (MAJA/WASP Tiles) - Germany, DOI: 10.15489/4hcq6dgkj648</li> </ul> </li> <li>Auxiliary data <ul> <li>European Union, Copernicus Land Monitoring Service, European Environment Agency (EEA), <strong>Copernicus High Resolution Layer: Imperviousness Status Map, 2018 </strong>(https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/status-maps/imperviousness-density-2018)</li> <li><strong>OpenStreetMap</strong> Planet dump retrieved from https://planet.osm.org, https://www.openstreetmap.org</li> <li><strong>S2GLC Map of Europe</strong> (R. Malinowski, S. Lewiński, M. Rybicki, E. Gromny, M. Jenerowicz, M. Krupiński, A. Nowakowski, C. Wojtkowski, M. Krupiński, E. Krätzschmar, and P. Schauer, "Automated Production of a Land Cover/Use Map of Europe Based on Sentinel-2 Imagery," Remote Sensing, vol. 12, no. 21, p. 3523, 2020.)</li> </ul> </li> </ul> <p>File naming:<br> classification_map_germany_[year].tif example: classification_map_germany_2020.tif</p> <p>Projection + EPSG code:<br> WGS 84 / UTM zone 32N (EPSG: 32632)</p> <p>Spatial extent:<br> north: 55:03:38.646483N<br> south: 47:08:24.738401N<br> west: 5:33:47.816647E<br> east: 15:34:24.108516E</p> <p>Spatial resolution:<br> 10 m</p> <p>Format: COG (Cloud-Optimized GeoTIFF)</p> <p>Pixel values:<br> 10: forest<br> 20: low vegetation<br> 30: water<br> 40: built-up<br> 50: bare soil<br> 60: agriculture</p> <p>Temporal coverage:<br> Years 2016, 2019, 2020, 2021</p> <p>Software used:<br> GRASS 7.8, actinia</p> <p>Original dataset license:<br> The Sentinel-2 level 3A data produced and distributed by DLR are based on Copernicus Sentinel-2 level 1C data, which are subject to the following license: https://theia.cnes.fr/atdistrib/documents/TC_Sentinel_Data_31072014.pdf One of the following citations is mandatory for using the provided MAJA/WASP L3A product: German Aerospace Center (DLR): Sentinel-2 MSI - Level 3A (MAJA/WASP Tiles) - Germany, DOI: 10.15489/4hcq6dgkj648 or Contains modified Copernicus Sentinel data, processed by DLR, licensed under CC-BY 4.0</p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p>
Data for: "Carbon dioxide reduction by lanthanide(III) complexes supported by redox-active Schiff base ligands"
<p>RAW DATA FOR ARTICLE</p> <p>DATE: NOVEMBER 2022</p> <p>TITLE: Carbon dioxide reduction by lanthanide(III) complexes supported by redox-active Schiff base ligands</p> <p>AUTHORS: Nadir Jori, Davide Toniolo, Bang C. Huynh, Rosario Scopelliti, and Marinella Mazzanti*</p> <p>JOURNAL: Inorganic Chemistry Frontiers (RSC) 2020</p> <p>DOI: <a href="https://doi.org/10.1039/D0QI00801J">10.1039/D0QI00801J</a> </p> <p> </p>
Precipitation oxygen isoscape for mainland China from 1870 to 2017 generated based on data fusion and bias correction of iGCMs simulations
<p>The dataset includes the stable oxygen isotope of precipitation for the mainland of China over the 1870-2017 period, at a spatial resolution of 50-60 km and a monthly temporal resolution. In order to make full use of observations to integrate the advantages of various iGCMs, the combination of data fusion and bias correction methods are used. Some physical-based ancillary data are introduced in the fusion methods, including elevation and meteorological data, to enrich the climate and terrain information in the process of data fusion. Specifically,</p><p>(1) for the 1979-2001 period, nine simulations from six iGCMs (CAM2, GISS E, HadAM3, IsoGSM2, LMDZ4, and MIROC32) and ancillary data are fused with observations by using the CNN fusion method;</p><p>(2) for the 2002-2007 period, seven simulations from four iGCMs (GISS E, IsoGSM2, LMDZ4, and MIROC32) and ancillary data are fused by using the CNN fusion method;</p><p>(3) for the 1969-1978 period, four simulations from three iGCMs (CAM2, GISS E, and HadAM3) and ancillary data are fused by using the CNN fusion method;</p><p>(4) for the 1958-1968 and 2008-2017 periods, two iGCM simulations (CAM2 and HadAM3 for 1958-1968 and IsoGSM2 and LMDZ4 zoomed for 2008-2017) are corrected by using two BCMs, and ensemble mean (mean of four simulations) is then calculated;</p><p>(5) for the 1870-1957 period, one iGCM simulation (HadAM3) is corrected by using two BCMs, and the ensemble mean (mean of two simulations) is then calculated.</p><p>Compared with the existing iGCMs, the isoscape has high quality and stability for a large region in China at the monthly scale. However, it should be noted that the isoscape may be more reliable for the common periods of most iGCMs (1969-2007), but mediocre for other periods. </p>
Global gross primary production (GPP) product generated by data fusion based on random forest
<p>Improving the ability of gross primary production (GPP) estimates to capture extreme climate perturbations and reduce the uncertainty of GPP response processes to extreme climate is a new challenge. Based on the random forest algorithm, we integrated the multimodel GPP simulation results published by the Multiscale Synthesis and Terrestrial Model Intercomparison Project, the FLUXNET flux-site-observed GPP, the standardized precipitation index (SPI) and the standardized temperature index (STI) to generate a set of global GPP time-series data products from 2001 to 2010. The new GPP product was named DFRF-GPP, referring to the GPP generated by data fusion based on random forest. DFRF-GPP is highly reliable and can be used as a valuable data source for various applications, especially in high-temperature and drought-related studies.</p>
Dataset: Comparative evaluation of a keyword based search and semantic search in a data portal for biodiversity research.
<p>Supplementary material for a comparative evaluation of a keyword based search and semantic search in a data portal for biodiversity research. We conducted a relevance evaluation with 6 users over 19 search questions in two search interfaces.</p> <p>The users provided up to five search questions and relevant keywords from their research background. We setup a dataset search over a corpus of ~92,000 randomly selected metadata files from GFBio (<a href="https://www.gfbio.org">https://www.gfbio.org</a>). For each of their own search queries, the users got two result sets presented. The first one displayed results obtained from a keyword search. The second panel contained dataset results from a prototypical semantic search. Instead of results with exact mentions of the query terms, the semantic search also presented related results with synonyms and more specific terms or terms obtained from concept nodes of a higher hierarchy level.</p> <p>Each user rated the relevance of his/her own search queries on a 7-point Likert scale for both search results.<br> In addition, users also assessed the expanded keywords for each question.</p> <p>More information can be found in our publication:</p> <p>Löffler, F. and Klan, F. (2016): Does Term Expansion Matter for the Retrieval of Biodiversity Data? in Joint Proceedings of the Posters and Demos Track of the 12th International Conference on Semantic Systems - SEMANTiCS2016 and the 1st International Workshop on Semantic Change & Evolving Semantics (SuCCESS'16), co-located with the 12th International Conference on Semantic Systems (SEMANTiCS 2016),2016, <a href="http://ceur-ws.org/Vol-1695/paper2.pdf">http://ceur-ws.org/Vol-1695/paper2.pdf</a></p> <p> </p>
Data for Project 'Feasibility, Usability and Acceptance of a Newly Developed Exergame-Based Training Concept for Older Adults with Mild Neurocognitive Disorder - A Pilot Randomized Controlled Trial'
<p>Data for Project 'Feasibility, Usability and Acceptance of a Newly Developed Exergame-Based Training Concept for Older Adults with Mild Neurocognitive Disorder - A Pilot Randomized Controlled Trial' (trial registered at clinicaltrials.gov (<a href="https://clinicaltrials.gov/ct2/show/NCT04996654">NCT04996654</a>; date of registration: 11 July 2021), consisting of:</p> <p>(1) the original and complete data set for all primary outcomes ('Data_Primary-Outcomes_Brain-IT-Pilot-Feasibility-RCT_for-publication.xlsx');</p> <p>(2) the original and complete data set for all secondary outcomes ('Data_Secondary-Outcomes_Brain-IT-Pilot-Feasibility-RCT_for-publication.xlsx');</p> <p>(3) the original and complete data set for all other outcomes (i.e. baseline factors (demographic data, type of usual care interventions) and training heart rate; 'Data_Other-Outcomes_Brain-IT-Pilot-Feasibility-RCT_for-publication.xlsx');</p> <p>(4) folder including the raw and processed heart rate variability (HRV) and electroencephalography (EEG) data for all participants and measurements (HRV-and-EEG_raw-and-processed-data.zip);</p> <p>(5) a corresponding README file including (a) general information, (b) data and file overview, (c) sharing and access information, (d) methodological information, and (e) data-specific information.</p>
Data base for the known manuscripts that transmit the chronicle of Jakob Twinger von Königshofen
<p>The database derives from my research for my PhD, which I conducted between 2013 and 2017 at the University of Freiburg (Germany). The thesis was defended in summer 2018 and published in 2020 with De Gruyter: <a href="https://doi.org/10.1515/9783110636475">Ina Serif: Geschichte aus der Stadt. Überlieferung und Aneignungsformen der deutschen Chronik Jakob Twingers von Königshofen. Berlin/Boston 2020</a>. I have been using <a href="https://nodegoat.net/">nodegoat</a> for several years now as my working and data storing environment. As it is a relational database, the export for this repository consists of two files: the main file, manuscripts.csv, which contains the manuscripts and their metadata, and a content file, content.csv, which contains the relations between texts and manuscripts, using "nodegoat_ID" as identifier. As of 2022, the main file consists of 128 records, the content file of 1220 entries. You should be able to import it to the software you prefer, linking the content to the manuscripts through "nodegoat_ID". The metadata fields for the manuscripts are the following: nodegoat ID, Institution, Call no., Sigle, Folio no., Material, Language, illustrated, Exact date of origin, Date of origin, Place of origin, Place certain, Chronicle Version, Additional info. The information about the single codices derives from manuscript catalogues, research articles, and own analyses. For a complete bibliography of the used literature see my thesis, pp. 215–245. As this is a finished research project, the database gets updated only on an irregular basis. I am also maintaining an online article about the known manuscripts and existing digital versions: Ina Serif: Der zerstreute Chronist. Zur Überlieferung der deutschsprachigen Chronik Jakob Twingers von Königshofen, in: Mittelalter. Interdisziplinäre Forschung und Rezeptionsgeschichte, 2/2015, last update 10/2021, <a href="https://mittelalter.hypotheses.org/7063">https://mittelalter.hypotheses.org/7063</a>. The record for the chronicle in the <a href="https://handschriftencensus.de/werke/1906">Handschriftencensus</a> does not list all manuscripts that I consider to be textual witnesses (as of October 2021, it lists 115 manuscripts), but contains additional information regarding single codices.</p>
Italy, climate data analyst based on era5 land data
<p>These plots illustrate the results of a climatic analysis conducted in Italy using the ERA5 Land (Copernicus Climate Service), since 1950.</p>
Global Ocean Heat Content Anomalies based on Argo data
<p><strong>NOTE for users: please use the latest version of the product at https://zenodo.org/doi/10.5281/zenodo.10182972. </strong>Ocean Heat Content Anomalies (OHCA) are calculated (during 2005-2022) subtracting the mean over the period 2005-2021 from the monthly time series. Yearly OHCA time series are then calculated. OHC fields are mapped using locally stationary Gaussian processes with data-driven decorrelation scales (Kuusela and Stein, 2018). A linear time trend was included in the estimate of the mean field (along with spatial terms and harmonics for the annual cycle). In the present version, mapping is done in latitude and longitude with monthly subsets of data (a future version will add time to the mapping). Mapping is done separately for different vertical sections. Different vertical sections are combined to estimate: 1. Global OHC timeseries (e.g., for level 0-2000m: GCOS_0000_2000_OHCA_J_m2_oc, for OHC in J/m2; GCOS_0000_2000_OHCA_ZJ, for OHC in ZJ; the attribute “GCOS_area” is included for both variable types in the netcdf file and it tells the corresponding surface area); 2. Volume averaged temperature anomaly (global) timeseries (e.g., for level 0-2000m: GCOS_0000_2000_vol_ave_temp_anom, in degC; the attribute “GCOS_volume'' is included for this variable type in the netcdf file and it tells the corresponding volume). Regions of the ocean that are shallower than 300 m or are not sufficiently well sampled by the Argo array are not included.</p>
Tower-based solar-induced fluorescence and vegetation index data for Southern Old Black Spruce forest
<p>Data includes remote sensing products from PhotoSpec (a scanning spectrometer) from September 2019-December 2020 at the Southern Old Black Spruce site in Saskatchewan Canada. We provide half-hourly averaged vegetation indices (NIRv, NDVI, PRI, CCI) and solar-induced fluorescence (SIF) and for a stand-representative mix of black spruce and larch. Additionally, we provide photosynthetically active radiation (PAR), absorbed photosynthetically active radiation (APAR), and the escape fraction of SIF photons (fesc). Version 2 also provides half-hourly averaged SIF and SIFrelative for black spruce (evergreen) and larch (deciduous) separately. </p>
Open synthetic data on travel and charging demand of battery electric cars: An agent-based simulation on three charging behavior archetypes
<p><strong>Background</strong></p> <p>Battery electric vehicles (BEVs) are crucial for a sustainable transportation system. As more people adopt BEVs, it becomes increasingly important to accurately assess the demand for charging infrastructure. However, much of the current research on charging infrastructure relies on outdated assumptions, such as the assumption that all BEV owners have access to home chargers and the "Liquid-fuel" mental model. To address this issue, we simulate the travel and charging demand on three charging behavior archetypes. We use a large synthetic population of Sweden, including detailed individual characteristics, such as dwelling types (detached house vs. apartment) and activity plans (for an average weekday). This data repository aims to provide the BEV simulation's input, assumptions, and output so that other studies can use them to study sizing and location design of charging infrastructure, grid impact, etc.</p> <p>A journal paper published in Transportation Research Part D: Transport and Environment details the method to create the data (particularly Section 2.2 BEV simulation).</p> <p><a href="https://doi.org/10.1016/j.trd.2023.103645">https://doi.org/10.1016/j.trd.2023.103645</a></p> <p><strong>Methodology</strong></p> <p>This data product is centered on the 1.7 million inhabitants of the Västra Götaland (VG) region, which includes the second largest city in Sweden, Gothenburg. We specifically simulated 284,000 car agents who live in VG, representing 35% of all car users and 18% of the total population in the region. They spend their simulation day (representing an average weekday) in a variety of locations throughout Sweden.</p> <p>This open data repository contains the core model inputs and outputs. The numbers in parentheses correspond to the data sets. We use individual agents' activity plans (1) and travel trajectories from MATSim simulation for the BEV simulation (2), in which we consider overnight charger access (3), car fleet composition referencing the current private car fleet in Sweden (4), and Swedish road network with slope information (5) with realistic BEV charging & discharging dynamics. For the BEV simulation, we tested ten scenarios of charging behavior archetypes and fast charging powers (6). The output includes the time history of travel trajectories and charging of the simulated BEVs across the different scenarios (7).</p> <p><strong>Data description</strong></p> <p>The current data product covers seven data files.</p> <p><strong>(1) Agents' experienced activity plans</strong></p> <p>File name: 1_activity_plans.csv</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>act_id</p> </td> <td> <p>Activity index of each agent</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>deso</p> </td> <td> <p>Zone code of Demographic statistical areas (DeSO)<sup>1</sup></p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>POINT_X</p> </td> <td> <p>Coordinate X of activity location (SWEREF99TM)</p> </td> <td> <p>Float</p> </td> <td> <p>meter</p> </td> </tr> <tr> <td> <p>POINT_Y</p> </td> <td> <p>Coordinate Y of activity location (SWEREF99TM)</p> </td> <td> <p>Float</p> </td> <td> <p>meter</p> </td> </tr> <tr> <td> <p>act_purpose</p> </td> <td> <p>Activity purpose (work, home, other)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>mode</p> </td> <td> <p>Transport mode to reach the activity location (car)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>dep_time</p> </td> <td> <p>Departure time in decimal hour (0-23.99)</p> </td> <td> <p>Float</p> </td> <td> <p>hour</p> </td> </tr> <tr> <td> <p>trav_time</p> </td> <td> <p>Travel time to reach the activity location</p> </td> <td> <p>String</p> </td> <td> <p>hour:minute:second</p> </td> </tr> <tr> <td> <p>trav_time_min</p> </td> <td> <p>Travel time in decimal minute</p> </td> <td> <p>Float</p> </td> <td> <p>minute</p> </td> </tr> <tr> <td> <p>speed</p> </td> <td> <p>Travel speed to reach the activity location</p> </td> <td> <p>Float</p> </td> <td> <p>km/h</p> </td> </tr> <tr> <td> <p>distance</p> </td> <td> <p>Travel distance between the origin and the destination</p> </td> <td> <p>Float</p> </td> <td> <p>km</p> </td> </tr> <tr> <td> <p>act_start</p> </td> <td> <p>Start time of activity in minute (0-1439)</p> </td> <td> <p>Integer</p> </td> <td> <p>minute</p> </td> </tr> <tr> <td> <p>act_time</p> </td> <td> <p>Activity duration in decimal minute</p> </td> <td> <p>Float</p> </td> <td> <p>minute</p> </td> </tr> <tr> <td> <p>act_end</p> </td> <td> <p>End time of activity in decimal hour (0-23.99)</p> </td> <td> <p>Float</p> </td> <td> <p>hour</p> </td> </tr> <tr> <td> <p>score</p> </td> <td> <p>Utility score of the simulation day given by MATSim</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p>1 <a href="https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/">https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/</a></p> <p> </p> <p><strong>(2) Travel trajectories</strong></p> <p>File name: 2_input_zip</p> <p>Produced by MATSim simulation, the zip folder contains ten files (events_batch_X.csv.gz, X=1, 2, …, 10) of input events for the BEV simulation. They are the moving trajectories of the car agents in their simulation days.</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>time</p> </td> <td> <p>Time in second in a simulation day (0-86399)</p> </td> <td> <p>Integer</p> </td> <td> <p>Second</p> </td> </tr> <tr> <td> <p>type</p> </td> <td> <p>Event type defined by MATSim simulation<sup>2</sup></p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>link</p> </td> <td> <p>Nearest road link consistent with (5)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>vehicle</p> </td> <td> <p>Vehicle ID identical to person</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p><sup>2 </sup>One typical episode of MATSim simulation events: Activity ends (actend) -> Agent’s vehicle enters traffic (vehicle enters traffic) -> Agent’s vehicle moves from previous road segment to its next connected one (left link) -> Agent’s vehicle leaves traffic for activity (vehicle leaves traffic) -> Activity starts (actstart)</p> <p> </p> <p><strong>(3) Overnight charger access</strong></p> <p>File name: 3_home_charger_access.csv</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>home_charger</p> </td> <td> <p>Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>(4) Car fleet composition</strong></p> <p>File name: 4_car_fleet.csv</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>income_class</p> </td> <td> <p>Income group (0=None, 1=below 180K, 2=180K-300K, 3=300K-420K, 4=above 420K)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>car</p> </td> <td> <p>Car model class (B=40 kWh, C=60 kWh, D=100 kWh)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p> </p> <p>(<strong>5) Road network with slope information</strong></p> <p>File name: 5_road_network_with_slope.shp (5 files in total)</p> <table> <tbody> <tr> <td> <p>Column</p> </td> <td> <p>Description</p> </td> <td> <p>Data type</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>length</p> </td> <td> <p>The length of road link</p> </td> <td> <p>Float</p> </td> <td> <p>meter</p> </td> </tr> <tr> <td> <p>freespeed</p> </td> <td> <p>Free speed</p> </td> <td> <p>Float</p> </td> <td> <p>km/h</p> </td> </tr> <tr> <td> <p>capacity</p> </td> <td> <p>Number of vehicles</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>permlanes</p> </td> <td> <p>Number of lanes</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>oneway</p> </td> <td> <p>Whether the segment is one-way (0=no, 1=yes)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>modes</p> </td> <td> <p>Transport mode (car)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>link_id</p> </td> <td> <p>Link ID</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>from_node</p> </td> <td> <p>Start node of the link</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>to_node</p> </td> <td> <p>End node of the link</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>count</p> </td> <td> <p>Aggregated traffic (number of cars travelled per day)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>slope</p> </td> <td> <p>Slope in percent from -6% to 6%</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>geometry</p> </td> <td> <p>LINESTRING (SWEREF99TM)</p> </td> <td> <p>geometry</p> </td> <td> <p>meter</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>(6) Simulation scenarios specifying the parameter sets</strong></p> <p>File name: 6_scenarios.txt</p> <table> <tbody> <tr> <td> <p><strong>Parameter set</strong></p> <p><strong>(paraset)</strong></p> </td> <td> <p><strong>Strategy 1</strong></p> </td> <td> <p><strong>Strategy 2</strong></p> </td> <td> <p><strong>Strategy 3</strong></p> </td> <td> <p><strong>Fast charging power (kW)</strong></p> </td> <td> <p><strong>Minimum parking time for charging (min)</strong></p> </td> <td> <p><strong>Intermediate charging power (kW)</strong></p> </td> </tr> <tr> <td> <p>0</p> </td> <td> <p>0.2</p> </td> <td> <p>0.2</p> </td> <td> <p>0.9</p> </td> <td> <p>150</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>0.2</p> </td> <td> <p>0.2</p> </td> <td> <p>0.9</p> </td> <td> <p>50</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>0.3</p> </td> <td> <p>0.3</p> </td> <td> <p>0.9</p> </td> <td> <p>150</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>0.3</p> </td> <td> <p>0.3</p> </td> <td> <p>0.9</p> </td> <td> <p>50</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>(7) Time history of travel trajectories and charging of the simulated BEVs</strong></p> <p>File name: 7_output.zip</p> <p>Produced by the BEV simulation, the zip folder contains four files (parasetX.csv.gz, X=1, 2, 3, 4) corresponding to the four parameter sets specified in (6). They are the moving trajectories of the car agents with simulated energy and charging time history in their simulation days.</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>home_charger</p> </td> <td> <p>Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>car</p> </td> <td> <p>Car model class (B=40 kWh, C=60 kWh, D=100 kWh)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>seq</p> </td> <td> <p>Sequence ID of time history by agent</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>time</p> </td> <td> <p>Time (0-86399)</p> </td> <td> <p>Integer</p> </td> <td> <p>Second</p> </td> </tr> <tr> <td> <p>purpose</p> </td> <td> <p>Valid for activities (home, work, school, other)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>type</p> </td> <td> <p>Event type defined by MATSim simulation</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>link</p> </td> <td> <p>Link ID (link_id in File 5)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>distance_driven</p> </td> <td> <p>Cumulative driven distance in the simulation day</p> </td> <td> <p>Float</p> </td> <td> <p>km</p> </td> </tr> <tr> <td> <p>energy_1</p> </td> <td> <p>Energy consumed while driving (-) or charging (+) (Strategy 1)</p> </td> <td> <p>Float</p> </td> <td> <p>kWh</p> </td> </tr> <tr> <td> <p>energy_2</p> </td> <td> <p>Energy consumed while driving (-) or charging (+) (Strategy 2)</p> </td> <td> <p>Float</p> </td> <td> <p>kWh</p> </td> </tr> <tr> <td> <p>energy_3</p> </td> <td> <p>Energy consumed while driving (-) or charging (+) (Strategy 3)</p> </td> <td> <p>Float</p> </td> <td> <p>kWh</p> </td> </tr> <tr> <td> <p>charger_1</p> </td> <td> <p>Power rating of the charger (Strategy 1)</p> </td> <td> <p>Float</p> </td> <td> <p>kW</p> </td> </tr> <tr> <td> <p>charger_2</p> </td> <td> <p>Power rating of the charger (Strategy 2)</p> </td> <td> <p>Float</p> </td> <td> <p>kW</p> </td> </tr> <tr> <td> <p>charger_3</p> </td> <td> <p>Power rating of the charger (Strategy 3)</p> </td> <td> <p>Float</p> </td> <td> <p>kW</p> </td> </tr> <tr> <td> <p>soc_1</p> </td> <td> <p>State of charge (0-1, Strategy 1)</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>soc_2</p> </td> <td> <p>State of charge (0-1, Strategy 2)</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>soc_3</p> </td> <td> <p>State of charge (0-1, Strategy 3)</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p> </p>
Atmopheric Angular Momentum based on JRA-55 data
<p>Atmospheric Angular Momentum based on JRA-55 data.</p> <p>ChiP_IB.txt is the matter term, considering the inverted barometer (IB). ChiW2.txt is the motion term, taking into account the topography.</p>
Data from: CoAct Citizen Science chatbot explores social support networks in mental health based on lived experiences
<p>A data set on lived experiences in the context of social support in mental health, created within a Citizen Social Science project. </p> <p><br> Societies around the world increasingly encounter wicked and complex problems, such as those related to mental health, environmental justice, and youth employment. <strong>CoAct as a EU-funded global effort</strong> addresses these problems by deploying Citizen Social Science. </p> <p> </p> <p><strong>Citizen Social Science</strong> is understood here as participatory research co-designed and directly driven by citizen groups sharing a social concern. This methodology wants to give citizen groups an equal ‘seat at the table’ through <strong>active participation in research</strong>, from the design to the interpretation of the results and their transformation into concrete actions. Citizens thus act as <strong>co-researchers</strong> and are recognised as in-the-field competent experts. </p> <p> </p> <p>In Barcelona, a group of <strong>32 co-researchers</strong> work together with the OpenSystems group, Universitat de Barcelona, the Catalan Federation of Mental Health (Federació Salut Mental Catalunya), and with the help of many others on a better understanding of informal <strong>social support networks in mental health</strong> in the project <em>CoActuem per la Salut Mental</em> (lit. “We act together for mental health”). The co-researchers, who are either persons with a personal history of mental health problems or are family members of the latter, contributed their <strong>personal experiences related to social support</strong> in the form of <strong>222 micro-stories</strong>, each shorter than 400 characters, and most accompanied by an illustration by Pau Badia.</p> <p> </p> <p>Those micro-stories form the heart of the first co-created Citizen Science chatbot, the code of which is open on <a href="https://github.com/Chaotique/CoActuem_per_la_Salut_Mental_Chatbot.git">https://github.com/Chaotique/CoActuem_per_la_Salut_Mental_Chatbot.git</a> . The <strong>Telegram chatbot</strong> sends them to participants <strong>on a daily basis over the course of a year</strong> and asks them either, whether they and/ or their close surrounding lived this experience, too (stories of type C), or, how they would or would have reacted in the presented situation (stories of type T). The answers of each participant can be contrasted with the individual participants’ answer to a 32-questions <strong>socio-demographic survey</strong>. Further, the timing of the messages is included to allow for a broader analysis. </p> <p> </p> <p>The chatbot is still running, hence this data set will still be updated. For further information on the project <strong>CoAct</strong>, see <a href="https://coactproject.eu/">https://coactproject.eu/</a>. For further details on the co-creation process and purpose of the chatbot <strong>CoActuem per la Salut Mental</strong>, take a look on <a href="https://coactuem.ub.edu/">https://coactuem.ub.edu/</a>. Please direct your questions regarding the data set to <strong>coactuem[at]ub.edu</strong>.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The CoAct project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement number 873048. We especially thank the co-researchers for the passion and time invested.</p>
ArrayCGH microarray images for 'Autoencoder and NCA based neural network model to estimate survival prognosis in multiple myeloma using arrayCGH data'
<p>ArrayCGH microarray images for 'Autoencoder and NCA based neural network model to estimate survival prognosis in multiple myeloma using arrayCGH data'</p>
Supporting data for "CoVEffect: Interactive System for Mining the Effects of SARS-CoV-2 Mutations and Variants Based on Deep Learning"
<p>This repository contains the datasets created and extracted for the paper:</p> <p>Giuseppe Serna García, Ruba Al Khalaf, Francesco Invernici, Stefano Ceri, and Anna Bernasconi. 2022.<br> "<strong>CoVEffect</strong>: Interactive System for Mining the <strong>Effects of SARS-CoV-2 Mutations and Variants</strong> Based on Deep Learning". (Available online at http://gmql.eu/coveffect)</p> <p>--------------------------------------------------------------------------------<br> LIST OF FILES WITH DESCRIPTION:<br> --------------------------------------------------------------------------------</p> <p>AdditionalFile1-effects-taxonomy:<br> Descriptions of legal values for the 'Effect' field, based on a categorized taxonomy.</p> <p>AdditionalFile2-levels-taxonomy:<br> Descriptions of legal values for the 'Level' field.</p> <p>AdditionalFile3-training_dataset_target:<br> List of target tuples (manually annotated) of 221 abstracts considered for training the model. For each abstract, target tuples follow the schema ID, DOI, title, entity, effect, level, type (mutation or variant), tuples_count (>1 when an effect/level is shared by multiple entities, #abstracts containing the same effect described in the tuple).</p> <p>AdditionalFile4-validation_dataset_target:<br> List of target tuples (manually annotated) of 50 abstracts considered for validating the prepared prediction model.<br> For each abstract, target tuples follow the schema defined for AdditionalFile3.</p> <p>AdditionalFile5-validation_dataset_highlighted:<br> Textual abstracts of the 50 manuscripts considered for validation; the text used to support the manual target annotations has been highlighted in yellow.</p> <p>AdditionalFile6-validation_dataset_prediction:<br> List of predicted annotations of 50 abstracts considered for validating the prepared prediction model. The file is split in 4 TSV, respectively for entity (a), effect (b), level (c), and whole tuple predictions (d).</p> <p>AdditionalFile7-keywords_query_list:<br> Keyword-based search run on the CORD-19 dataset to extract a relevant subset of abstracts regarding the scope of interest of CoVEffect. The Boolean logic used to combine keywords is explained in the section 'Annotations of the biology-related CORD-19 cluster'.</p> <p>AdditionalFile8-CORD-19_batch_dataset_metadata:<br> Metadata of the 7,230 papers extracted by the keyword-based query in AdditionalFile7.<br> These abstracts have been annotated by the prediction framework.</p> <p>AdditionalFile9-CORD-19_batch_dataset_prediction:<br> List of predicted annotations of 7,230 abstracts extracted from the biology-related cluster of CORD-19.</p> <p>AdditionalFile10-test_dataset_target:<br> List of target tuples (manually annotated) of 100 abstracts randomly selected from the 7,230 extracted as in AdditionalFile8.<br> For each abstract, target tuples follow the schema defined for AdditionalFile3.</p> <p>AdditionalFile11-test_dataset_prediction:<br> List of predicted annotations of 100 abstracts considered for testing the prediction model on a subset of the CORD-19 biology-related cluster. As AdditionalFile6, it is split in 4 TSV, respectively for entity (a), effect (b), level (c), and whole tuple predictions (d).</p>
SKY-LAUT – Carriage-based laser scanner data from Austrian forest stands
<p>This dataset contains 3D point clouds, automatically calculated single tree parameters, reference data, and application videos of carriage-based laserscanning in cable yarding operations. Point clouds from 8 cable corridors and 4 scan varaints are provided in .las format in the folder point_clouds.zip. The individual files are labeled with numeric cable corridor IDs and scan variant IDs. According to standard conventions, a .las file contains a header block, variable-length records, and the point cloud data. The .las files can be read, visualized, and processed with common software programs for point cloud processing (e.g., CloudCompare), and they can also be handled with the free statistical software (e.g., R Foundation for Statistical Computing, Vienna, Austria). The reference and algorithm dataset is provided in a comma-separated values (CSV) file (algorithm_results_reference_data.csv ) and contains the manual and automatic measurements of the single-tree attributes. "stand_id" marks the stand (can be stand_1 or stand_2). "cable_corridor" can range from 1 to 8 and "scan_variant" from 1 to 4. "tree_id_ref" is a continuous id for the trees from the reference data collection. "x_ref", "y_ref", "dbh_ref", "h_ref" and "tree_species" are the coordinates, diameter at breast heigths, tree heights and tree species from the reference data collection. "x_cbls", "y_cbls", "dbh_ref" and "h_cbls" are the coordinates, diameter at breast heigth, tree height and tree species from carriage-based laser scanning point clouds and the automatic algorithm. "dist_to_skyline" is the orthogonal distance from the tree to the skyline. "tree_detection" indicates whether a tree was detected "correct", "non" oder "false" by the automatic algorithm.</p>
FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and GEDI data with a deep learning approach.
<p>The products can be vizualized at <a href="https://martinschwartz0.users.earthengine.app/view/forms-height-biomass-volume-viewer">https://martinschwartz0.users.earthengine.app/view/forms-height-biomass-volume-viewer</a></p> <p>- FORMS-H: Canopy height map of France at 10 m resolution. The units are in centimeter (10^-2 m).</p> <p>- FORMS-B: Above-ground biomass density map of France at 30 m resolution. The units are in Mg ha-1</p> <p>- FORMS-V: Wood volume density map of France at 30 m resolution. The units are in m3 ha-1</p> <p>Please refer to the paper <a href="https://doi.org/10.5194/essd-15-4927-2023">https://doi.org/10.5194/essd-15-4927-2023</a> for further details.</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.