Find research datasets worth reusing

opencc-by-4.0Jul 2022View details →

Concentrations of trace metals (Cu, Cd, Zn) in the ocean at given open and coastal locations

This data compilation contains previously published concentrations of copper, cadmium and zinc in open and coastal oceans of the world. The data was recalculated to nmol/L for consistency, assuming the following molar masses of copper, cadmium and zinc as 63.546, 112.411 and 65.380 g/mol, respectively, and salinity as 1.025 kg/L. The temperature and salinity provided by the authors were also included, in case there is a desire for future users to utilize different conversion methods to recalculate original data. Only data with information on whether the authors measured the trace metal concentrations in open or coastal marine environments were included. The oceans were divided into different geographical regions, namely the Atlantic Ocean, Pacific Ocean, Indian Ocean and Southern Ocean, and subsequently subdivided according to the information authors have given in their publications. In this context, several chemically diverse seas were included in geographical regions in order to limit the number of broad ocean regimes. Chemically diverse sub-regimens were, however, considered within each geographical grouping.

opencc-by-4.0Nov 2022View details →

Advanced open source data formats for geometrically and physically coupled systems - examples

Some model files to describe a geometrically and physically coupled PDE and a ODE system, arising from discretization in space. The files correspond to: <ul> <li>a simplified two component problem with coupling and an FMU in withFMU.json</li> <li>the corresponding ODE in io_withFMU_FECoupled.json</li> <li>a PDE model of a complete machine in ictimt_coupledModel.zip. This model belongs to https://doi.org/10.17973/MMSJ.2021_7_2021072</li> <li>the corresponding discrete model in ictimt_feCoupled.zip This model belongs to https://doi.org/10.17973/MMSJ.2021_7_2021072</li> </ul>

opencc-by-4.0Nov 2022View details →

Open database on global coal and metal mine production

See also the associated Data Descriptor published in Nature Scientific Data: <a href="https://www.nature.com/articles/s41597-023-01965-y">www.nature.com/articles/s41597-023-01965-y</a> This data set covers global extraction of coal and metal ores on an individual mine level. It covers 1171 individual mines in 80 different countries, reporting mine-level production for 80 different materials in the period 2000-2021. Furthermore, also data on mining coordinates, ownership, mineral reserves, mining waste, transportation of mining products, as well as mineral processing capacities (smelters and mineral refineries) and production is included. The data was gathered manually from more than 1900 openly available sources, such as annual or sustainability reports of mining companies. All datapoints are linked to their respective source documents. After manual screening and entry of the data, automatic cleaning, harmonization and data checking was conducted. Geoinformation was obtained either from coordinates available in company reports, or by retrieving the coordinates via Google Maps API and subsequent manual checking. For mines where no coordinates could be found, other geospatial attributes such as province, region, district or municipality were recorded, and linked to the GADM data set, available at <a href="https://www.gadm.org">www.gadm.org</a>. The data set, found in the "data" sub-folder, consists of 12 tables. The table “facilities” contains descriptive and spatial information of mines and processing facilities, and is available as a GeoPackage (GPKG) file. All other tables are available in comma-separated values (CSV) format. If you are working in Excel or have problems handling the GeoPackage file, it can be converted to Excel with an online tool, such as <a href="https://mygeodata.cloud/converter/gpkg-to-xlsx">https://mygeodata.cloud/converter/gpkg-to-xlsx</a>. A schematic depiction of the database is provided in the file database_model.pdf. A description of all variables of all tables is provided in the Excel file variables_descriptions.xlsx, and all materials for which production is reported in the database are listed in the file materials_covered.xlsx. For convenience, global and national coverage shares for every material and country with recorded production in the database is provided in the file coverage_table.pdf. These coverage shares were calculated by comparing the production values of this database to official production statistics reported in the UNEP IRP Global Material Flows Database, to be found under <a href="https://www.resourcepanel.org/global-material-flows-database">https://www.resourcepanel.org/global-material-flows-database</a>. For significant raw material producing countries, these coverage shares are also visualised in the file coverage_national_area_charts.pdf.

opencc-by-4.0Dec 2022View details →

PLOS Open Science Indicators & Zotero Romania Metadata

Matched metadata from PLOS Open Science Indicators and Zotero export

opencc-zeroDec 2022View details →

The Unofficial Guide on applying NCN Open Access rules to GitHub repositories.

The Unofficial Guide on applying NCN Open Access rules to GitHub repositories. Some HTML code can be used here.

Thermodynamic and hydrological drivers of the subsurface thermal regime in Central Spain: open data and code

Quality controlled temperature data at daily resolution at CTS, HRR, HYS, NVC, RSI and SGV and the most relevant codes for data processing used in: García-Pereira, F., González-Rouco, J. F., Schmid, T., Melo-Aguilar, C, Vegas-Cañas, C., Steinert, N. J.,  Roldán-Gómez, P. J., Cuesta-Valero, F. J., García-García, A., Beltrami, H., and de Vrese, H.: "Thermodynamic and  hydrological drivers of the subsurface thermal regime in Central Spain". Earth Surf. Dynam., submitted, 2023. All data can be also freely obtained for research from the original data sources, GuMNet (https://www.ucm.es/gumnet/) and AEMET (https://www.aemet.es/en/datos_abiertos). Further details of the code are available upon request to the corresponding author (Félix García-Pereira, felgar03@ucm.es).

Accompanying data for the open-source book Modeling of Hydrological Systems in Semi-Arid Central Asia

This data set is used to reproduce examples in the open-source book <a href="https://hydrosolutions.github.io/caham_book/">"Modeling of Hydrological Systems in Semi-Arid Central Asia"</a> which is part of a free course on hydrological modeling in Central Asia. The course teaches how to use publicly available data to implement a hydrological model for climate impact studies (Marti et al., 2023).  To use the data set to reproduce the examples in the book: Download the book from https://doi.org/10.5281/zenodo.6350042 and this data set to the same hierarchical level in your file system:  |- caham_book |- caham_data    |- AmuDarya    |- central_asia_domain    |- student_case_study_basins    |- SyrDarya You will need a working installation of R (https://www.r-project.org/) and a GUI (e.g. Posit, formerly RStudio https://posit.co/) to reproduce the scripted examples in the book. Once your software is set up, you can proceed to run the examples.

Data on the actual use of open data/ open source on pre-prints at arXiv/bioRxiv

Articles submitted (1st edition) to the preprint server arXiv/bioRxiv were converted to text and analysed as follows : <ul> <li>For arXiv articles, nationality was assigned to the manuscript using the first occurrence of the email address in the manuscript.</li> <li>For bioRxiv articles, we assigned nationality using the country tag information in the metadata about the first author.</li> <li>We listed the URLs that appeared in each manuscript.</li> <li>We checked how many articles contained a particular URL (e.g. github; https://github.com ) by year, month and nationality.</li> </ul> This dataset describes the results of the above work.

opencc-by-4.0Dec 2021View details →

The LOTUS Initiative for Open Natural Products Research: frozen dataset union wikidata (with metadata)

Dataset present on Wikidata used in the frame of the LOTUS Initiative: <a href="https://doi.org/10.7554/eLife.70780">https://doi.org/10.7554/eLife.70780</a>

opencc-by-4.0Dec 2021View details →

The LOTUS Initiative for Open Natural Products Research: waste to recycle

Dataset not uploaded to Wikidata.  Generated in the frame of the LOTUS Initiative: <a href="https://doi.org/10.7554/eLife.70780">https://doi.org/10.7554/eLife.70780</a> Shared for further curation.

opencc-by-4.0Mar 2022View details →

The LOTUS Initiative for Open Natural Products Research: metadata

Metadata of each of the three objects (structures, organisms, references) used in the frame of the LOTUS Initiative: <a href="https://doi.org/10.7554/eLife.70780">https://doi.org/10.7554/eLife.70780</a>

Dataset and codebook for the article by Gaume J, Bertholet N, McCambridge J, et al. Effect of a Novel Brief Motivational Intervention for Alcohol-Intoxicated Young Adults in the Emergency Department: A Randomized Clinical Trial. JAMA Netw Open. 2022;5(10):e2237563. doi: 10.1001/jamanetworkopen.2022.37563

Dataset and codebook for the article Gaume J, Bertholet N, McCambridge J, et al. Effect of a Novel Brief Motivational Intervention for Alcohol-Intoxicated Young Adults in the Emergency Department: A Randomized Clinical Trial. JAMA Netw Open. 2022;5(10):e2237563. doi: <a href="http://jamanetwork.com/article.aspx?doi=10.1001/jamanetworkopen.2022.37563">10.1001/jamanetworkopen.2022.37563</a> The dataset contains all data needed to reproduce the results in the above cited article. Variable description and labels can be found in the codebook. Please refer to the published article and supplemental online content for further information about the data and the study procedures.

5D-NP-FABTECH_SALV - Open Dataset for "3D Micropatterned Functional Surface Inspired by Salvinia Molesta via Direct Laser Lithography for Air Retention and Drag Reduction"

This is the open dataset for the paper: "Omar Tricinci*, Francesca Pignatelli, Virgilio Mattoli*, 3D Micropatterned Functional Surface Inspired by Salvinia Molesta via Direct Laser Lithography for Air Retention and Drag Reduction, On line (2023) [DOI: 10.1002/adfm.202206946] " This include the Supplementary Information file ("SI-PaperSalvinia3_PostRevOKV2.pdf.pdf"), all the source material used for the paper preparation and more.  For each folder (sub-dataset) there is a corresponding readme file describing the content and including metadata

Umfragedaten: Studentische Perspektiven auf Open Educational Resources in der Rechtswissenschaft

Der Datensatz enthält die Rohdaten, ausgewerteten Daten und das dazugehörige R-Skript für eine 2022 durchgeführte Umfrage unter  Studierenden der Rechtswissenschaft in Deutschland zu Chancen und Grenzen von Open Educational Resources. Für weitere Fragen zu der Umfrage wenden Sie sich bitte an <a href="https://www.jura.uni-muenster.de/de/institute/lehrstuhl-fuer-oeffentliches-recht-voelker-und-europarecht-sowie-empirische-rechtsforschung/team/weitere-personen/max-milas/">Max Milas</a> oder <a href="https://www.jura.fu-berlin.de/fachbereich/einrichtungen/oeffentliches-recht/lehrende/calliessc/Mitarbeiterinnen-und-Mitarbeiter/Wissenschaftliche_Mitarbeiter_innen/Valentina-Chiofalo/index.html">Valentina Chiofalo</a>.

Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring (Open Pit Extraction, Valea Sesei and Roșia Poieni (Romania)).

Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring in the Open Pit Extraction (mine located at Valea Sesei and Roșia Poieni (Romania)) (3D view mode). Accessing the GOLDENAI GUI, please refer to the following link (login required): <a href="https://next-gui.goldenai.opt-net.eu/ ">https://next-gui.goldenai.opt-net.eu/ </a>

opencc-by-4.0Jul 2022View details →

An Open-Source Automatic Survey of Green Roofs in London using Segmentation of Aerial Imagery: Dataset

This archive contains code and data to go with the paper *An Open-Source Automatic Survey of Green Roofs in London using Segmentation of Aerial Imagery*.   This archive contains geospatial data, as well as the code used to generate the geospatial data. The geospatial data consists of georeferenced polygons identifying areas which are covered by green roofs in London (GBR) generated from 2019 aerial imagery. The data is described in detail in the manuscript *An Open-Source Automatic Survey of Green Roofs in London using Segmentation of Aerial Imagery*. See abstract below.   GeoJSON format: GeoJSON is a format for encoding geospatial data, see https://geojson.org/. GeoJSON can be read using GIS programs including ArcGIS, QGIS, OGR.   Contents: `geospatial_data/buffered_polygons_2021.zip` a zip archive containing a geojson file. It is the estimated locations of green roofs in London in 2021 and is the main result, which can be opened in any GIS program after being unzipped. `geospatial_data/buffered_polygons_2019.zip` a zip archive containing a geojson file. It is the estimated locations of green roofs in London in 2019 and is a secondary result, which can be opened in any GIS program after being unzipped. The predictions were made with the same model as the 2021 results. `geospatial_data/labelled_area.zip` a zip archive containing a geojson file. Identifies the area which was hand-labelled. `geospatial_data/manual_2021.zip` a zip archive containing a geojson file. Manually labelled green roof from 2021 imagery. `geospatial_data/manual_2019.zip` a zip archive containing a geojson file. Manually labelled green roof from 2019 imagery. `segmentation_code` contains the code used to produce the segmentation from the aerial imagery. `analysis_code` contains the code used to produce the plots and tables for the paper.   Imagery availability: Unfortunately the aerial imagery and building footprint data cannot be shared directly, as you will require the proper license. Both can be found at [Digimap](https://digimap.edina.ac.uk) provided your institution has the license.   Abstract: Green roofs can mitigate heat, increase biodiversity, and attenuate storm water, giving some of the benefits of natural vegetation in an urban context where ground space is scarce. To guide the design of more sustainable and climate resilient buildings and neighbourhoods, there is a need to assess the existing status of green roof coverage and explore the potential for future implementation. Therefore, accurate information on the prevalence and characteristics of existing green roofs is needed, but this information is currently lacking. Segmentation algorithms have been used widely to identify buildings and land cover in aerial imagery. Using a machine-learning algorithm based on U-Net to segment aerial imagery, we surveyed the area and coverage of green roofs in London, producing a geospatial dataset \cite[]{simpson_charles_2022_6861929}. We estimate that there was 0.23 km^2 of green roof in the Central Activities Zone (CAZ) of London, (1.07 km^2) in Inner London, and (1.89 km^2) in Greater London in the year 2021. This corresponds to 2.0% of the total building footprint area in the CAZ, and 1.3% in Inner London. There is a relatively higher concentration of green roofs in the City of London, covering 3.9% of the total building footprint area. Test set accuracy was 0.99, with an f-score of 0.58. When tested against imagery and labels from a different year (2019), the model performed just as well as a model trained on the imagery and labels from that year, showing that the model generalised well between different imagery. We improve on previous studies by including more negative examples in the training data, and by requiring coincidence between vector building footprints and green roof patches. We experimented with different data augmentation methods, and found a small improvement in performance when applying random elastic deformations, colour shifts, gamma adjustments, and rotations to the imagery. The survey covers 1558 km^2 of Greater London, making this the largest open automatic survey of green roofs in any city. The geospatial dataset is at the single-building level, providing a higher level of detail over the larger area compared to what was already available. This dataset will enable future work exploring the potential of green roofs in London and on urban climate modelling.

Open synthetic data on travel and charging demand of battery electric cars: An agent-based simulation on three charging behavior archetypes

Background 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. 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). <a href="https://doi.org/10.1016/j.trd.2023.103645">https://doi.org/10.1016/j.trd.2023.103645</a> Methodology 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. 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). Data description The current data product covers seven data files. (1) Agents' experienced activity plans File name: 1_activity_plans.csv <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> act_id </td> <td> Activity index of each agent </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> deso </td> <td> Zone code of Demographic statistical areas (DeSO)1 </td> <td> String </td> <td> - </td> </tr> <tr> <td> POINT_X </td> <td> Coordinate X of activity location (SWEREF99TM) </td> <td> Float </td> <td> meter </td> </tr> <tr> <td> POINT_Y </td> <td> Coordinate Y of activity location (SWEREF99TM) </td> <td> Float </td> <td> meter </td> </tr> <tr> <td> act_purpose </td> <td> Activity purpose (work, home, other) </td> <td> String </td> <td> - </td> </tr> <tr> <td> mode </td> <td> Transport mode to reach the activity location (car) </td> <td> String </td> <td> - </td> </tr> <tr> <td> dep_time </td> <td> Departure time in decimal hour (0-23.99) </td> <td> Float </td> <td> hour </td> </tr> <tr> <td> trav_time </td> <td> Travel time to reach the activity location </td> <td> String </td> <td> hour:minute:second </td> </tr> <tr> <td> trav_time_min </td> <td> Travel time in decimal minute </td> <td> Float </td> <td> minute </td> </tr> <tr> <td> speed </td> <td> Travel speed to reach the activity location </td> <td> Float </td> <td> km/h </td> </tr> <tr> <td> distance </td> <td> Travel distance between the origin and the destination </td> <td> Float </td> <td> km </td> </tr> <tr> <td> act_start </td> <td> Start time of activity in minute (0-1439) </td> <td> Integer </td> <td> minute </td> </tr> <tr> <td> act_time </td> <td> Activity duration in decimal minute </td> <td> Float </td> <td> minute </td> </tr> <tr> <td> act_end </td> <td> End time of activity in decimal hour (0-23.99) </td> <td> Float </td> <td> hour </td> </tr> <tr> <td> score </td> <td> Utility score of the simulation day given by MATSim </td> <td> Float </td> <td> - </td> </tr> </tbody> </table> 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>   (2) Travel trajectories File name: 2_input_zip 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. <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> time </td> <td> Time in second in a simulation day (0-86399) </td> <td> Integer </td> <td> Second </td> </tr> <tr> <td> type </td> <td> Event type defined by MATSim simulation2 </td> <td> String </td> <td> - </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> link </td> <td> Nearest road link consistent with (5) </td> <td> String </td> <td> - </td> </tr> <tr> <td> vehicle </td> <td> Vehicle ID identical to person </td> <td> Integer </td> <td> - </td> </tr> </tbody> </table> 2 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)   (3) Overnight charger access File name: 3_home_charger_access.csv <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> home_charger </td> <td> Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes) </td> <td> Integer </td> <td> - </td> </tr> </tbody> </table>   (4) Car fleet composition File name: 4_car_fleet.csv <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> income_class </td> <td> Income group (0=None, 1=below 180K, 2=180K-300K, 3=300K-420K, 4=above 420K) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> car </td> <td> Car model class (B=40 kWh, C=60 kWh, D=100 kWh) </td> <td> String </td> <td> - </td> </tr> </tbody> </table>   (5) Road network with slope information File name: 5_road_network_with_slope.shp (5 files in total) <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> length </td> <td> The length of road link </td> <td> Float </td> <td> meter </td> </tr> <tr> <td> freespeed </td> <td> Free speed </td> <td> Float </td> <td> km/h </td> </tr> <tr> <td> capacity </td> <td> Number of vehicles </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> permlanes </td> <td> Number of lanes </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> oneway </td> <td> Whether the segment is one-way (0=no, 1=yes) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> modes </td> <td> Transport mode (car) </td> <td> String </td> <td> - </td> </tr> <tr> <td> link_id </td> <td> Link ID </td> <td> String </td> <td> - </td> </tr> <tr> <td> from_node </td> <td> Start node of the link </td> <td> String </td> <td> - </td> </tr> <tr> <td> to_node </td> <td> End node of the link </td> <td> String </td> <td> - </td> </tr> <tr> <td> count </td> <td> Aggregated traffic (number of cars travelled per day) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> slope </td> <td> Slope in percent from -6% to 6% </td> <td> Float </td> <td> - </td> </tr> <tr> <td> geometry </td> <td> LINESTRING (SWEREF99TM) </td> <td> geometry </td> <td> meter </td> </tr> </tbody> </table>   (6) Simulation scenarios specifying the parameter sets File name: 6_scenarios.txt <table> <tbody> <tr> <td> Parameter set (paraset) </td> <td> Strategy 1 </td> <td> Strategy 2 </td> <td> Strategy 3 </td> <td> Fast charging power (kW) </td> <td> Minimum parking time for charging (min) </td> <td> Intermediate charging power (kW) </td> </tr> <tr> <td> 0 </td> <td> 0.2 </td> <td> 0.2 </td> <td> 0.9 </td> <td> 150 </td> <td> 5 </td> <td> 22 </td> </tr> <tr> <td> 1 </td> <td> 0.2 </td> <td> 0.2 </td> <td> 0.9 </td> <td> 50 </td> <td> 5 </td> <td> 22 </td> </tr> <tr> <td> 2 </td> <td> 0.3 </td> <td> 0.3 </td> <td> 0.9 </td> <td> 150 </td> <td> 5 </td> <td> 22 </td> </tr> <tr> <td> 3 </td> <td> 0.3 </td> <td> 0.3 </td> <td> 0.9 </td> <td> 50 </td> <td> 5 </td> <td> 22 </td> </tr> </tbody> </table>   (7) Time history of travel trajectories and charging of the simulated BEVs File name: 7_output.zip 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. <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> home_charger </td> <td> Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> car </td> <td> Car model class (B=40 kWh, C=60 kWh, D=100 kWh) </td> <td> String </td> <td> - </td> </tr> <tr> <td> seq </td> <td> Sequence ID of time history by agent </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> time </td> <td> Time (0-86399) </td> <td> Integer </td> <td> Second </td> </tr> <tr> <td> purpose </td> <td> Valid for activities (home, work, school, other) </td> <td> String </td> <td> - </td> </tr> <tr> <td> type </td> <td> Event type defined by MATSim simulation </td> <td> String </td> <td> - </td> </tr> <tr> <td> link </td> <td> Link ID (link_id in File 5) </td> <td> String </td> <td> - </td> </tr> <tr> <td> distance_driven </td> <td> Cumulative driven distance in the simulation day </td> <td> Float </td> <td> km </td> </tr> <tr> <td> energy_1 </td> <td> Energy consumed while driving (-) or charging (+) (Strategy 1) </td> <td> Float </td> <td> kWh </td> </tr> <tr> <td> energy_2 </td> <td> Energy consumed while driving (-) or charging (+) (Strategy 2) </td> <td> Float </td> <td> kWh </td> </tr> <tr> <td> energy_3 </td> <td> Energy consumed while driving (-) or charging (+) (Strategy 3) </td> <td> Float </td> <td> kWh </td> </tr> <tr> <td> charger_1 </td> <td> Power rating of the charger (Strategy 1) </td> <td> Float </td> <td> kW </td> </tr> <tr> <td> charger_2 </td> <td> Power rating of the charger (Strategy 2) </td> <td> Float </td> <td> kW </td> </tr> <tr> <td> charger_3 </td> <td> Power rating of the charger (Strategy 3) </td> <td> Float </td> <td> kW </td> </tr> <tr> <td> soc_1 </td> <td> State of charge (0-1, Strategy 1) </td> <td> Float </td> <td> - </td> </tr> <tr> <td> soc_2 </td> <td> State of charge (0-1, Strategy 2) </td> <td> Float </td> <td> - </td> </tr> <tr> <td> soc_3 </td> <td> State of charge (0-1, Strategy 3) </td> <td> Float </td> <td> - </td> </tr> </tbody> </table>

ASSIST-IOT Open Call Project RAZOR DATASETS (INSIGHIO)

Example datasets for road anomaly detection produced in the context of RAZOR Open Call ASSIST-IoT Project, carried out by INSIGHIO.

Data from the OPERAS business models survey on open access books

OPERAS (the European Research Infrastructure for the development of open scholarly communication in the social sciences and humanities) has conducted a survey of publishing organisations throughout Europe to identify and better understand existing and potential business models to support the Open Access publication of research monographs. The results of the survey are used to inform the formulation of recommendations about how to create a sustainable open access book publishing ecosystem within Europe. The survey was designed to serve two core aims:  1. To further, better or improve our understanding of the scholarly publishing landscape and of the challenges that publishers face in the context of publishing OA monographs; 2. To identify main trends (including opportunities and challenges) and the knowledge of collaborative funding and infrastructure models in OA publishing in SSH.  The survey was open between 16 February and 14 April 2021. The results are presented in two versions of the white paper of the Open Access Business Models Special Interest Group:  Stone, Graham, Błaszczyńska, Marta, Lebon, Chloé, Morka, Agata, Mosterd, Tom, Mounier, Pierre, Proudman, Vanessa, Speicher, Lara, & Melin&scaron;čak Zlodi, Iva. (2021). Collaborative models for OA book publishers (1.0). Zenodo. https://doi.org/10.5281/zenodo.5494731 and the second version to be published in Spring 2023.

Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound - Elastic Wave Simulations (Open Scanning Paths)

Elastic Wave Simulations - Open Scanning Paths This dataset is part of a larger repository (DOI: 10.5281/zenodo.5248082) which houses links to the data used in the publication "Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound" <a href="http://www.science.org/doi/10.1126/sciadv.adf2037">(Reardon et al., 2023)</a>. If you use these simulated data please cite our publication (<a href="http://www.science.org/doi/10.1126/sciadv.adf2037">http://www.science.org/doi/10.1126/sciadv.adf2037</a>) and the software package k-Wave (DOI: 10.1109/ULTSYM.2014.0037). This dataset contains the normal shear surface velocity in a cylindrical slab of tissue-like material excited by an acoustic source with a Gaussian spatial profile simulated via a pseudo-spectral numerical method. The data is provided as .mat files. The files are separated by the type of scanning path, the scanning speed of the acoustic source, and the parameters of the scanning path. Details of the simulation parameters can be found in our publication. Line Paths - The acoustic source scanned along a linear trajectory at speeds ranging from 2 m/s to 12 m/s (scanning speed is indicated in the filename). Zigzag Paths - The acoustic source scanned along a zigzag path on the surface of the simulated medium with x-axis scanning speed vx = 3, 4, 5, 6 m/s. At all speeds, the ultrasound focus was modulated transverse to its primary motion direction at a speed, vy, of +-2.5 m/s yielding a zigzag path (2 cm path width). The x-axis scanning speed is designated in the filename. Letter Paths - The acoustic source scanned the a trajectory in the shape of the letter "Z." Scanning speeds ranged from 2 m/s to 12 m/s (scanning speed is designated in the filename). Focus Control Rate - The acoustic source scanned along a linear trajectory at 7 m/s but at different focus control sample rates fc. These paths amount to a courser sampling of the linear trajectory. In lieu of updating the location of the acoustic source at each timepoint in the simulation, we specified a rate at which the location of the acoustic source would be updated. We set fc to approximately 0.7, 1.4, and 4.2 kHz (designated at the end of the filename as VeryCoarse, Coarse, and Fine, respectively). (Compare with Line_07, which has the finest path sampling and an *fc* of approximately 200 kHz.)   Data Fields surfaceData (NxNxM) - 3D array containing the normal shear velocity of the simulated medium (in m/s) on a NxN Cartesian grid of locations at M timepoints. The simulated tissue medium was cylindrical, so locations outside the circular top surface are NaN. sourceSignals (NxNxQ) - 3D array containing the acoustic source distribution on the NxN Cartesian grid of locations used to excite the surface of the simulated tissue medium for Q timepoints. sourceEnvelope (Qx1) - Vector containing the amplitude envelope that was applied to sourceSignals at each timestep Q dt - The time between adjacent timepoints in seconds (i.e. fs = 1/dt) dx/dy - The distance between adjacent grid locations in the x/y direction of the Cartesian grid (in m)