Find research datasets worth reusing

opencc-by-4.0Jun 2024View details →

FLOW-Alaiz Benchmark: Baseline Results

This repository hosts documentation and notebooks for the <a href="https://www.flow-horizon.eu/">EU-FLOW</a> Alaiz benchmark. The first stage is finished with the publication of baseline results for PyWAsP, PyWAsP-CFD, and SiteFlow in the Torque-2024 conference. Sanz Rodrigo J, Oxley G and Tobias Olsen B (2024) FLOW-Alaiz benchmark for coupled terrain and array interaction flow models. Baseline Results. J. Phys.: Conf. Ser. 2767 092077, <a href="https://iopscience.iop.org/article/10.1088/1742-6596/2767/9/092077">doi:10.1088/1742-6596/2767/9/092077</a>

opencc-by-4.0Jun 2024View details →

Flow cytometry of mesenteric lymph nodes, small and large intestinal lamina propria, and spinal cord cells from fibre-rich and fiber-free diet-fed gnotobiotic mice at baseline and after experimental autoimmune encephalomyelitis (EAE) induction

We perform profiling of different immune cell populations in the small (SILP) and large intestine lamina propria (CLP), mesenteric lymph nodes (MLN) and spinal cords (SC). We are specifically interested to evaluate the impact of dietary fiber deprivation followed by mucus erosion on the immune cell profiles of T helper cells (Th cells, T cell population) of gnotobiotic mice fed a fiber-rich (FR) or fiber-free (FF) diet. This dataset aims to assess the impact of microbiome and diet on disease course in a mouse model of multiple sclerosis (experimental autoimmune encephalomyelitis, EAE) via T cell populations. Mice are either germ-free or colonized by intragastric gavage with a defined variation of a 14-member synthetic human gut microbiome (doi: 10.1016/j.cell.2016.10.043 and 10.1016/j.xpro.2021.100607): SM01 (Akkermansia muciniphila monocolonisation), SM03 (Bacteroides caccae, Bacteroides thetaiotaomicron, Barnesiella intestinihominis), SM04 (B. caccae, B. thetaiotaomicron, B. intestinihominis, A. muciniphila), SM12 (full community except mucin-specialists B. intestinihominis and A. muciniphila), SM13 (full community except mucin specialist A. muciniphila), or SM14 (full community: Roseburia intestinalis, Faecalibacterium prausnitzii, Marvinbryantia formatexigens, Collinsella aerofaciens, Desulfovibrio piger, B. caccae, B. thetaiotaomicron, Bacteroides ovatus, Bacteroides uniformis, B. intestinihominis, Eubacterium rectale, Clostridium symbiosum, Escherichia coli, and A. muciniphila). At age 5 to 8 weeks, mice were colonized with SM combinations while fed an FR diet. Mice were either maintained on an FR diet or switched to an FF diet at 5 days after initial colonization, until the end of experiment. Baseline samples were collected 20 days following the diet switch. Otherwise, EAE induction was performed 15 days after the diet switch and samples were collected 30 days after the induction.

opencc-by-4.0Jul 2024View details →

Dataset: Baseline for the Northeast Atlantic (58 – 70° N) intertidal Mytilus species complex (Mytilus spp.) 2021-2022

Aim: Mussels (Mytilus spp.) are abundant in the North Atlantic, sessile, and sensitive to environmental change, and suitable as sentinels of environment and climate change of costal ecosystems. We aimed to determine the baseline for the Northeast Atlantic (58 – 70° N) Mytilus species complex, and to show the present distribution to surveys conducted 60 years ago.   Location: Northeast Atlantic Methodology: Baseline was obtained by investigating a total of 509 stations in the intertidal zone, in four regions comprising the environmental gradient from head of fjord to coast, and distributed over the latitudinal gradient from 58 – 70° N. Results: The baseline shows a range in continuous abundance of mussels from 12 to 36 %, patchy abundance from 26 to 57 % and no or very limited mussel abundance from 26 to 46 % between the four regions. The presence of mussels in the southeast and west region was visualized to previous surveys conducted 60 years ago. The data points to similar past and present presence of mussels in both regions, yet past major mussel fields in the inner section of region southeast was not detected in this study. Main conclusions: The baseline of Mytilus spp. in the Northeast Atlantic (58 – 70° N) is now available for future reference. The baseline, plotted to surveys conducted 60 years ago, points to awareness of the population situated in the southeast section of the investigated region. Continued monitoring and modelling are needed to clarify drivers of temporal and spatial variation in the mussel populations along the Northeastern Atlantic coast.

opencc-by-sa-4.0Jan 2017View details →

ScriptNet: ICDAR 2017 Competition on Baseline Detection in Archival Documents (cBAD)

This dataset contains the training and test set for the ICDAR 2017 Competition on Baseline Detection in Archival Documents (cBAD). A newly created freely available real world dataset consisting of 2035 annotated document page images that are collected from 9 different archives and form the basis of cBAD. Two competition tracks test different characteristics of the methods submitted. Track A [Simple Documents] is published with annotated text regions and tests therefore a method's quality of text line segmentation. The more challenging Track B [Complex Documents] provides only the page area. Hence, baseline detection algorithms need to correctly locate text lines in the presence of marginalia, tables, and noise. The dataset comprises images with additional PAGE XMLs. The PAGE XMLs contain text regions and baseline annotations. Competition Website: https://scriptnet.iit.demokritos.gr/competitions/5/ Version 3 is the version of the cBad competition Version 4 contains also the page region and in case of a double-page the page split as separator.

opencc-zeroSep 2024View details →

Monitoring and evaluation of UKRI's Open Access Policy: Exploring the use of open data sources to inform baseline values - Dataset

This dataset accompanies the report "Monitoring and evaluation of UKRI's Open Access Policy: Exploring the use of open data sources to inform baseline values", which is available via Zenodo. It provides record-level data of UKRI-funded and UK-affiliated research output (limited to journal articles with Crossref DOIs) published between 2012 and 2022 - including bibliographic metadata as well as data on open access availability, publisher, national and international collaborations, citations, views and downloads, altmetrics and subjects (fields). All variables are documented in the data dictionary included in this Zenodo record. The code used to generate the dataset from open data sources is available on GitHub.  The following data sources were used: <ul> <li> Gateway to Research (records downloaded between 2023-11-05 and 2023-11-13) </li> <li> Crossref (Metadata Plus snaphot 2023-10-31, Crossref member route API 2024-01-23) </li> <li> OpenAlex (data snapshot 2023-10-18) </li> <li> Unpaywall (data snapshot 2023-11-27) </li> <li> IRUS UK (2024-04-03) </li> <li> Crossref Event Data (2023-04-01) </li> </ul> The project made use of Curtin Open Knowledge Initiative (COKI) infrastructure, which is documented on GitHub: <a href="https://github.com/The-Academic-Observatory">https://github.com/The-Academic-Observatory</a>.

opencc-by-4.0Nov 2022View details →

Dataset for the article EDM-GNSS distance comparison at the EURO5000 calibration baseline: preliminary results

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.

opencc-zeroMar 2023View details →

Belgian baseline distribution of invasive alien species of Union concern (Regulation (EU) 1143/2014)

Aims and scope The European Alien Species Information Network team (EASIN, http://easin.jrc.ec.europa.eu) of the Joint Research Centre (JRC) requests the European member states to provide and verify the baseline distribution data of invasive alien species of Union Concern (Tsiamis et al. 2017) as provided by the EASIN mapping system (Katsanevakis et al. 2012). These are species with documented biodiversity impacts sensu the European Union Regulation on the prevention and management of the introduction and spread of Invasive Alien Species in Europe (IAS Regulation No 1143/2014) (European Union 2014). The purpose of this baseline is to set a representative geographic account of the distribution of these species at (i) country and (ii) 10km2 grid level before the entry into force of the Regulation (and the listing of species through implementing regulations). This distribution provides the baseline for subsequent reporting by the member states as required by the IAS Regulation. The dataset provides a shapefile on the baseline distribution of the invasive species of EU concern in Belgium based on an aggregated dataset (ias_belgium_t0_xxxx). Data were compiled from various datasets holding invasive species observations such as data from research institutes and research projects (76%), citizen science observatories (23%) and a range of other sources (1%) such as governmental agencies, water managers, invasive species control companies, angling and hunting organizations etc. Data were normalized using a custom mapping of the original data files to Darwin Core (Wieczorek et al. 2012) where possible. Species names were mapped to the GBIF Backbone Taxonomy (GBIF 2016) using the species API (http://www.gbif.org/developer/species). Appropriate selection of records was performed based on predefined cut-off dates (see data range) and record content validation (see validation procedure). Data were then joined with GRID10k layer Belgium based on GRID10k cellcodes (ETRS_1989_LAEA). File description The dataset contains two types of data: <ol> <li> Shapefiles (ias_belgium_t0_2016.zip, ias_belgium_t0_2018.zip, ias_belgium_t0_2020.zip and ias_belgium_t0_2023.zip) providing the presence of the species of EU concern at 10km2 (European Terrestrial Reference System projection - 1989 ETRS_1989_LAEA) level (resp. for 1st, 2nd, 3rd and 4th batch of species added to the Union List). The attributes table field “ACCEPTED” provides coded information on the distribution validation: correct squares (Y) represent data overlapping between the collated baseline data for Belgium and the EASIN maps. Incorrect data (N) can represent records mapped on wrong 10km2 squares, non-validated records or records that fall outside of the date range applied. New squares (New) represent previously unpublished data that were absent from EASIN. The work was supervised and validated by the Belgian national scientific council on invasive alien species, an official consultative structure coordinating scientific input and data aggregation between Belgian regions and institutions with regards to technical implementation of the Regulation No 1143/2014 on invasive alien species. </li> <li> A geojson version of the same shapefiles (ias_belgium_t0_2016.geojson, ias_belgium_t0_2018.geojson, ias_belgium_t0_2020.geojson, ias_belgium_t0_2023.geojson), in WGS84 projection. </li> </ol> Date range The baseline distribution reflects the current status and situation of the IAS of Union concern in Belgium at 10km2 grid level. Historical records were not taken into consideration for the baseline. The choice of cut-off date was based on an analysis of the relative contribution of a year in defining the total distribution of the species at 1km2 grid level (calculated as [the sum of unique UTM 1km2 grid squares year-1/total number of unique UTM  1km2 grid squares for that species]) based on the complete dataset.  The dataset comprises observations of Union List invasive species from 2000 until the entry into force for every species, hence between January 2000 (2000-01-01) and February 2016 (2016-01-31) for the species of the first batch (ias_belgium_t0_2016.zip), between January 2000 (2000-01-01) and August 2017 (2017-08-31) for the species of the first update of the Union List (ias_belgium_t0_2018.zip), between January 2000 (2000-01-01) and August 2019 (2019-08-31) for the species of the second update of the Union List (ias_belgium_t0_2020.zip), between January 2000 (2000-01-01) and August 2022 (2022-08-2) for the species of the third update (ias_belgium_t0_2023.zip). For raccoon dog (Nyctereutes procyonoides), included in the second update (ias_belgium_t0_2020.zip) the date cut-off is 01/01/2000 to 31/01/2019. Note that Pistia stratiotes, Xenopus laevis and Fundulus heteroclitus enter into force only as from 2 August 2024, Celastrus orbiculatus on 2 August 2027 because of prolonged transitionary measures. However, these species are already included in the baseline now with a cut-off date set on August 2022. The data include both casual records as well as established populations and also comprise data from eradicated populations for the period 2000-2022. Validation procedure Record validation was performed to exclude dubious records, wrong identifications etc. This was done based on the IdentificationVerificationStatus field (to which validation information from original data were mapped) if available. In general, non-validated data were not considered for ias_belgium_t0_xxxx. Data were validated in the original datasets based on evidence (e.g. pictures), on the observer’s experience, or based on a set of predefined rules (e.g. automated validation based on geographic filtering). Data from research institutes were generally considered validated. A few casual records of EU list species that were clearly planted were discarded manually. When the original dataset did not mention any validation status, records were not considered validated and therefore not taken into account for ias_belgium_t0_xxxx, unless for Chinese mitten crab Eriocheir sinensis, ruddy duck Oxyura jamaicensis, raccoon Procyon lotor, Siberian ground squirrel Tamias sibiricus, sacred ibis Threskiornis aethiopicus, and red-eared slider Trachemys spp. For these species, we assumed all records were correct as they originate from dedicated sampling (E. sinensis) within research projects or represent species that are readily recognizable by people in the field. Likewise, for the second batch species, all records of Egyptian goose Alopochen aegyptiaca, Himalayan balsam Impatiens glandulifera, giant hogweed Heracleum mantegazzianum and muskrat Ondatra zibethicus (mostly derived from public eradication services) were considered validated and taken into account. For the third batch species, records of the widespread tree of heaven Ailanthus altissima and pumpkinseed Lepomis gibbosus were also considered validated. For species with less than 10 records (Salvinia molesta, Acridotheres tristis), every record was manually checked. A visual check was performed on the resulting distribution maps by representatives of the Belgian scientific council on IAS and the Belgian Comittee on IAS, two official bodies created in response to the EU Regulation within the framework of a cooperation agreement between the Belgian regions and the Federal Authority. Data in the distribution maps provided by EASIN but not present in ias_belgium_t0_xxxx were carefully checked and kept/rejected accordingly. Data providers The providers of the invasive species data for this exercise (individuals and their respective organizations) are listed in the "data providers" section of the dataset metadata. Much of the primary occurrence data that formed the basis for this aggregated dataset will be published as open data on the Global Biodiversity Information Facility (GBIF) within the framework of the Tracking Invasive Alien Species project (TrIAS, https://osf.io/7dpgr/, 2017-2020).

opencc-by-4.0Aug 2022View details →

Paired differential gene expression and splicing analyses results of 199 baseline vs. case comparisons across 100 datasets

This dataset contains results from paired differential expression and differential splicing analyses as well as gene-set over-representation analysis results for 199 baseline vs. case comparisons across 100 randomly curated datasets with accompanying metadata (<a href="https://doi.org/10.1186/s12915-023-01724-w" target="_blank" rel="noopener">article</a>). All results were computed using the R package <a href="https://github.com/shdam/pairedGSEA">pairedGSEA</a>, which utilized DESeq2 (Love et al., 2014), DEXSeq (Anders et al., 2012), and fgsea (Korotkevich et al., 2019). See limma results here: <a href="https://doi.org/10.5281/zenodo.8162214">https://doi.org/10.5281/zenodo.8162214</a> Each .RDS file contains a list with four objects: A 'metadata' object with the metadata of the respective raw data, a 'genes' object with gene-level differential splicing and expression results, a 'gene_set' object with over-representation results, and 'experiment' with the experiment title. The filenames follow this pattern: "[dataset ID]_[GEO accession number]_[Manually assigned comparison title].RDS". All datasets were obtained from a local copy of the ARCHS4 v11 database of  transcript counts (Lachmann et al., 2018).

opencc-by-4.0Jul 2023View details →

Simulated spatially explicit dataset (300 m) on future forest cover changes in Southeast Asia projected under the baseline shared socioeconomic pathways

This is a simulated spatially explicit dataset on future forest cover changes in Southeast Asia projected under the baseline shared socioeconomic pathways. It includes six raster maps at a spatial resolution of 300 m: (1) 2015 baseline forest and non-forest map; (2) SSP1 2050 projected net forest gain map; (3) SSP2 2050 projected net forest gain map; (4) SSP3 2050 projected net forest loss map; (5) SSP4 2050 projected net forest gain map; and SSP5 2050 projected net forest loss map. This dataset is the result of a study published in Nature Communications (2019) (https://doi.org/10.1038/s41467-019-09646-4).

edi44/100

Baseline soil inorganic and organic property data for Saddle snowfence, 1993.

Soil cores were collected during the construction of the 100+year snowfence on the Niwot Ridge Saddle in the autumn of 1993. Organic matter determinations were made on the samples in January 1994. The samples were also measured for total phosphorus, nitrogen, and carbon. These 1993 samples were representative of the baseline (pre-snowfence) soil conditions.

openCC (other)Jan 2020View details →

edi44/100

Turf Transplant baseline soil biogeochemistry, 2024.

The Turf Transplant Experiment was set up in the summer of 2024. Paired experimental sites were established in two tundra community types - dry meadow and moist meadow - with one site of each community type pair in a lower elevation/warmer area and one site in a higher elevation/cooler area. Subplot turfs (25 cm^2) were transplanted (1) between sites of the same community type at different elevations/temperatures, (2) between plots within the same site or (3) left in place as non-transplant controls. Biogeochemical data from time 0 was taken in pre-established plots outside of the subplots. Coordinates to the biogeochemical plots can be found in this package. This data package contains gravimetric soil moisture measurements.

openCC (other)Jul 2025View details →

edi44/100

Parramore Island of the Virginia Coast Reserve Permanent Plot Baseline Data : Plot Coordinates 1992-1993

This contains coordinates (UTM zone 18N, NAD27) for permanent vegetation plots on Parramore Island. Plots were established using a uniform random number generator to generate random coordinates. One hundred of the random points that fell on the upland portion of Parramore Island (based on the McCaffrey vegetation maps) were selected as plot centers.

openCustomDec 2004View details →

opencc-by-4.0Feb 2019View details →

ICDAR 2019 Competition on Baseline Detection (cBAD)

This dataset contains the training, evaluation, and test set for the ICDAR 2019 Competition on Baseline Detection (cBAD). A newly created freely available real world dataset consisting of 3021 annotated document page images that are collected from seven European archives and form the basis of cBAD. The baselines in all images were manually annotated. The training and the evaluation sets contain PAGE XMLs with annotated text regions and baselines. Competition Website: https://scriptnet.iit.demokritos.gr/competitions/11/

Brainport, Platooning, Baseline

Scenario description: Driving without speed advice or any services to measure base-line. Session description: Baseline Datasets descriptions: AUTOPILOT_BrainPort_Platooning_DriverVehicleInteraction: Data extracted from the CAN of the vehicle This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle AUTOPILOT_BrainPort_Platooning_EnvironmentSensorsAbsolute: Data extracted from the vehicle environment sensors This dataset contains information about detected object, with absolute coordinates AUTOPILOT_BrainPort_Platooning_EnvironmentSensorsRelative: Data extracted from the vehicle environment sensors This dataset contains information about detected object, with relative coordinates AUTOPILOT_BrainPort_Platooning_IotVehicleMessage: Data sent between all devices, vehicles and services Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle. AUTOPILOT_BrainPort_Platooning_PlatoonFormation: Data sent from PlatoonService to vehicle This dataset contains information about the route and speed for a specific vehicle for forming a platoon AUTOPILOT_BrainPort_Platooning_PlatooningAction: Data logged by vehicle This dataset contains information about the current status of the platooning AUTOPILOT_BrainPort_Platooning_PlatooningEvent: Data logged by vehicle This dataset contains information about the identifiers used for each specific platooning event AUTOPILOT_BrainPort_Platooning_PlatoonStatus: Data sent by vehicle to PlatoonService This dataset contains information about the current status of the platooning AUTOPILOT_BrainPort_Platooning_PositioningSystem: Data from GPS on the vehicle This dataset contains speed, longitude, latitude, heading from the GPS AUTOPILOT_BrainPort_Platooning_PositioningSystemResample: Data from GPS on the vehicle This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds AUTOPILOT_BrainPort_Platooning_PSInfo: Data sent by PlatoonService to the vehicle This dataset contains speed and route information for the vehicle to create a platoon AUTOPILOT_BrainPort_Platooning_Target: Data from sensors on the vehicle Target detection in the vicinity of the host vehicle, by a vehicle sensor or virtual sensor AUTOPILOT_BrainPort_Platooning_Vehicle: Data from the CAN and sensors about the state of the vehicle This dataset contains a.o temperature and battery state of the vehicles AUTOPILOT_BrainPort_Platooning_VehicleDynamics: Data from the CAN and sensors about the state of the vehicle This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors AUTOPILOT_BrainPort_Platooning_VehicleDynamics: Data from the CAN and sensors about the state of the vehicle This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors

Brainport, Platooning, baseline platoon formation without IoT

Scenario description: Platoon formation and platooning, from Helmond to Eindhoven and back to the Automotive Campus. - Starting in urban area with speed limits of 15 and 30 km/h. - Driving East on the Europaweg with speed limits of 50 and 70 km/h. This includes 3 crossings with traffic lights. - Driving on the the N270, along the Automotive Campus. One crossing with traffic lights, just before the A270. - Driving on the A270 (speed limit 100 km/h). Interrupted by one traffic light. - U-turn at the fly-over or at the end of the A270, to return the same way to the Automotive Campus. Session description: Baseline of platoon formation and platooning, without any IoT, so no speed advices. - No live traffic light data available for planner - Starting at default locations - Meeting at the location of vehicle2 on the Automotive Campus - Platooning (CACC and lane keeping) on the A270 when possible. Datasets descriptions: AUTOPILOT_BrainPort_Platooning_DriverVehicleInteraction: Data extracted from the CAN of the vehicle This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle AUTOPILOT_BrainPort_Platooning_EnvironmentSensorsAbsolute: Data extracted from the vehicle environment sensors This dataset contains information about detected object, with absolute coordinates AUTOPILOT_BrainPort_Platooning_EnvironmentSensorsRelative: Data extracted from the vehicle environment sensors This dataset contains information about detected object, with relative coordinates AUTOPILOT_BrainPort_Platooning_IotVehicleMessage: Data sent between all devices, vehicles and services Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle. AUTOPILOT_BrainPort_Platooning_PlatoonFormation: Data sent from PlatoonService to vehicle This dataset contains information about the route and speed for a specific vehicle for forming a platoon AUTOPILOT_BrainPort_Platooning_PlatooningAction: Data logged by vehicle This dataset contains information about the current status of the platooning AUTOPILOT_BrainPort_Platooning_PlatooningEvent: Data logged by vehicle This dataset contains information about the identifiers used for each specific platooning event AUTOPILOT_BrainPort_Platooning_PlatoonStatus: Data sent by vehicle to PlatoonService This dataset contains information about the current status of the platooning AUTOPILOT_BrainPort_Platooning_PositioningSystem: Data from GPS on the vehicle This dataset contains speed, longitude, latitude, heading from the GPS AUTOPILOT_BrainPort_Platooning_PositioningSystemResample: Data from GPS on the vehicle This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds AUTOPILOT_BrainPort_Platooning_PSInfo: Data sent by PlatoonService to the vehicle This dataset contains speed and route information for the vehicle to create a platoon AUTOPILOT_BrainPort_Platooning_Target: Data from sensors on the vehicle Target detection in the vicinity of the host vehicle, by a vehicle sensor or virtual sensor AUTOPILOT_BrainPort_Platooning_Vehicle: Data from the CAN and sensors about the state of the vehicle This dataset contains a.o temperature and battery state of the vehicles AUTOPILOT_BrainPort_Platooning_VehicleDynamics: Data from the CAN and sensors about the state of the vehicle This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors AUTOPILOT_BrainPort_Platooning_VehicleDynamics: Data from the CAN and sensors about the state of the vehicle This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors

Brainport, Platooning, baseline, rush hour

Scenario description: Driving without speed advice or any services to measure base-line. Session description: Baseline (rush hour) Datasets descriptions: AUTOPILOT_BrainPort_Platooning_DriverVehicleInteraction: Data extracted from the CAN of the vehicle This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle AUTOPILOT_BrainPort_Platooning_EnvironmentSensorsAbsolute: Data extracted from the vehicle environment sensors This dataset contains information about detected object, with absolute coordinates AUTOPILOT_BrainPort_Platooning_EnvironmentSensorsRelative: Data extracted from the vehicle environment sensors This dataset contains information about detected object, with relative coordinates AUTOPILOT_BrainPort_Platooning_IotVehicleMessage: Data sent between all devices, vehicles and services Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle. AUTOPILOT_BrainPort_Platooning_PlatoonFormation: Data sent from PlatoonService to vehicle This dataset contains information about the route and speed for a specific vehicle for forming a platoon AUTOPILOT_BrainPort_Platooning_PlatooningAction: Data logged by vehicle This dataset contains information about the current status of the platooning AUTOPILOT_BrainPort_Platooning_PlatooningEvent: Data logged by vehicle This dataset contains information about the identifiers used for each specific platooning event AUTOPILOT_BrainPort_Platooning_PlatoonStatus: Data sent by vehicle to PlatoonService This dataset contains information about the current status of the platooning AUTOPILOT_BrainPort_Platooning_PositioningSystem: Data from GPS on the vehicle This dataset contains speed, longitude, latitude, heading from the GPS AUTOPILOT_BrainPort_Platooning_PositioningSystemResample: Data from GPS on the vehicle This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds AUTOPILOT_BrainPort_Platooning_PSInfo: Data sent by PlatoonService to the vehicle This dataset contains speed and route information for the vehicle to create a platoon AUTOPILOT_BrainPort_Platooning_Target: Data from sensors on the vehicle Target detection in the vicinity of the host vehicle, by a vehicle sensor or virtual sensor AUTOPILOT_BrainPort_Platooning_Vehicle: Data from the CAN and sensors about the state of the vehicle This dataset contains a.o temperature and battery state of the vehicles AUTOPILOT_BrainPort_Platooning_VehicleDynamics: Data from the CAN and sensors about the state of the vehicle This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors AUTOPILOT_BrainPort_Platooning_VehicleDynamics: Data from the CAN and sensors about the state of the vehicle This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors

Brainport, Urban driving, baseline test, camera detection

Scenario description: Base line test: no CEMA, no GeoFencing enabled. Vehicle only brakes on camera detection, when vehicle is blocked on its route by a crowd Datasets descriptions: AUTOPILOT_BrainPort_UrbanDriving_DriverVehicleInteraction: Data extracted from the CAN of the vehicle Dataset Description This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle AUTOPILOT_BrainPort_UrbanDriving_EAI2Mobile: Data from the service to the mobile Dataset Description This dataset contains information sent to the mobile about the Estimated Arrival time and position AUTOPILOT_BrainPort_UrbanDriving_EnvironmentSensorsAbsolute: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with absolute coordinates AUTOPILOT_BrainPort_UrbanDriving_EnvironmentSensorsRelative: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with relative coordinates AUTOPILOT_BrainPort_UrbanDriving_IOT_CEMA_Message: Data from the service to the vehicle Dataset Description This dataset contains information from the Crowd Estimation and Mobility Analytics service AUTOPILOT_BrainPort_UrbanDriving_IOT_FlowRadar_Message: Data from the vehicle to the service Dataset Description This dataset contains the GPS informaton (speed,position,heading) from the vehicle AUTOPILOT_BrainPort_UrbanDriving_IotVehicleMessage: Data sent between all devices, vehicles and services Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle. AUTOPILOT_BrainPort_UrbanDriving_IOT_VehicleStatus: Data sent from the vehicle to the service Dataset Description This dataset contains the current status of the vehicle AUTOPILOT_BrainPort_UrbanDriving_PositioningSystem: Data from GPS on the vehicle Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS AUTOPILOT_BrainPort_UrbanDriving_SmartphoneGPS: Data sent by the mobile to the service Dataset Description This dataset contains the GPS informaton (speed,position,heading) from the mobile AUTOPILOT_BrainPort_UrbanDriving_SmartphoneStatus: Data sent from the mobile to the service Dataset Description This dataset contains the current status of the mobile AUTOPILOT_BrainPort_UrbanDriving_TaxiRequest: Data sent from the mobile to the service Dataset Description This dataset contains the requests for a taxi from the mobile phones AUTOPILOT_BrainPort_UrbanDriving_Vehicle: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o temperature and battery state of the vehicles AUTOPILOT_BrainPort_UrbanDriving_VehicleDynamics: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors

opencc-by-4.0Jul 2020View details →

A large-scale wide-baseline light field dataset - Part I

This dataset is the Part I of a large-scale, synthetic wide-baseline light field dataset (called WLF), including 345 light fields.  Each light field provides 9x9 angular (RGB) images and ground truth disparities. This light field dataset involves the spatial resolution (512x512) images only. The dataset is originally created for training the deep learning-based models for depth estimation. You might use this dataset for other tasks if possible. You might also have a try to play with this dataset using our code in https://github.com/YanWQ/LLF-Net.