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4,669 results for “environment”

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edi60/100

Light Environment in Hemlock Removal Experiment at Harvard Forest since 2003

The impending loss of hemlock trees due to hemlock woolly adelgid (Adelges tsugae) infestation is expected to lead to changes in the light environment of the forest understory. These changes will both drive succession and will themselves be altered by successional processes. The light reaching the forest floor is measured using hemispherical canopy photographs. Photographs were taken in summer of 2003 and in December 2004 and May 2005 (both deciduous-tree leaf-off condition), prior to the application of the logging and girdling treatments, and then in September 2005 (deciduous tree leaf-on condition) after logging and girdling. Subsequent photographic series will be taken annually in spring (leaf-off) and summer/fall (leaf-on).

openCC0Dec 2023View details →

edi60/100

Environment and History in a Rich Mesic Forest in Western Massachusetts 1999-2001

In rich mesic forests, modern vegetation varies among primary versus post-agricultural, secondary forests, in part as a result of differential rate and ability of forest herbs to colonize after disturbance. Species with seeds lacking morphological adaptations for dispersal (barochores) and those which produce seeds with elaisomes to encourage ant dispersal (myrmecochores) may remain less frequent in secondary forests for decades or more.

openCC0Dec 2023View details →

edi56/100

Long-term Dynamics of Vegetation and Environment in Central Massachusetts 1000-2000

Historical and ecological data from north-central Massachusetts suggest that widespread and intensive human disturbance after European settlement led to a shift in forest composition and obscured regional patterns of species abundance. A paleoecological approach was required to place recent forest dynamics in a long-term context. Pollen and charcoal data from 11 small lakes in north-central Massachusetts were used to reconstruct local vegetation dynamics and fire histories across the region over the past 1000 years. The sites are located across an environmental gradient. Paleoecological data indicate that prior to European settlement, there was regional variation in forest composition corresponding to differences in climate, substrate, and fire regime. Oak, chestnut and hickory were abundant at low elevations, whereas hemlock, beech, sugar maple, and yellow birch were common at high elevations. Fire appears to have been more frequent and/or intense at lower elevations, maintaining high abundances of oak, and archaeological data suggest Native American populations were greater in these areas. A change in forest composition at higher elevations, around 550 years before present, may be related to the Little Ice Age (a period of variable climate), fire, and/or activity by Native Americans, and led to regional convergence in forest composition. After European settlement, forest composition changed markedly in response to human disturbance and there was a sharp increase in rates of vegetation change. Regional patterns were obscured further, leading to homogenization of broad-scale forest composition. There is no indication from the pollen data that forests are returning to pre-European settlement forest composition, and rates of vegetation change remain high, reflecting continuing disturbance to the landscape, despite regional reforestation.

openCC0Dec 2023View details →

zenodo52/100

Benchmarking on Microservices Configurations and the Impact on the Performance in Cloud Native Environments

The peer reviewed publication for this dataset has been published in LCN 2022, 47th Annual IEEE Conference on Local Computer Networks. Please cite this paper when referring to the dataset: https://www.eurecom.fr/publication/6971. Cloud-native and containerization have changed the way to develop and deploy applications. Cloud-native rethinks the application architecture by embracing a microservice approach, where each microservice is packaged into containers to run in a centralized or an edge cloud. When deploying the container running the micro-service, the tenant has to specify the needed computing resources to run their workload in terms of the amount of CPU and memory limit. However, it is not straightforward for a tenant to know in advance the computing amount that allows running the microservice optimally. This will have an impact not only on the service performances but also on the infrastructure provider, particularly if the resource overprovisioning approach is used. To overcome this issue, we conduct an experimental study aiming to detect if a tenant's configuration allows running its service optimally. We run several experiments on a cloud-native platform, using different types of applications under different resource configurations. The obtained results are presented in the accepted IEEE LCN paper (https://www.eurecom.fr/publication/6971) and are shared in this dataset. The datasets are collected for 3 types of applications: Web servers written in python and Golang, RabbitMQ data broker and the OpenAirInterface 5G Core network function AMF (Access and Mobility Management Function).   Web Servers: files:  golang-web-server-performance.csv, python-web-server-performance.csv We used Golang and Python-based web servers for the test. Each request to the web server returns a video of a size 43 MB. For testing we used ApacheBench, a command-line program used for benchmarking HTTP web servers. ApacheBench allows parallel requests from multiple clients. For each web server instance we send a number of requests ranging from 100 to 1000 and a concurrency level between 1 and 100, representing the number of parallel clients performing the requests. The information available in the dataset are as follows: time: timestamp of collection of metrics. ram_limit: the memory allocated to the container in megabytes. cpu_limit: the CPU allocated to the container. ram_usage: the amount of memory used by the container at the time of the metrics collection in byte. cpu_usage: the amount of CPU used by the container at the time of the metrics collection. n: the number of requests sent to the container. c: the concurrency level in the requests. lat50: the least response time for the best 50% requests in microseconds. lat66: the least response time for the best 66% requests in microseconds. lat75: the least response time for the best 75% requests in microseconds. lat80: the least response time for the best 80% requests in microseconds. lat90: the least response time for the best 90% requests in microseconds. lat95: the least response time for the best 95% requests in microseconds. lat98: the least response time for the best 98% requests in microseconds. lat99: the least response time for the best 99% requests in microseconds. lat100: the least response time in microseconds.   5G Core network’s AMF: file: amf-performance.csv For testing we use my5G-RANTester, a tool for emulating control and data planes of the UE and gNB (5G base station). The number of simultaneous registration requests that are sent to each instance of the AMF varies between 10 and 400. The information available in the dataset are as follows: time: timestamp of collection of metrics. ram_limit: the memory allocated to the container in megabytes. cpu_limit: the CPU allocated to the container. ram_usage: the amount of memory used by the container at the time of the metrics collection in byte. cpu_usage: the amount of CPU used by the container at the time of the metrics collection. n: the number of parallel registration requests sent to the AMF. mean: the mean registration time for all the registration requests in microseconds. lat50: the median registration time for registration requests in microseconds. lat75: the least registration time for the best 75% registration requests in microseconds. lat80: the least registration time for the best 80% registration requests in microseconds. lat90: the least registration time for the best 90% registration requests in microseconds. lat95: the least registration time for the best 95% registration requests in microseconds. lat98: the least registration time for the best 98% registration requests in microseconds. lat99: the least registration time for the best 99% registration requests in microseconds. lat100: the least registration time in microseconds.   RabbitMQ data broker: file: rabbitmq-performance.csv For testing we used RabbitMQ PerfTest which is a throughput testing tool that simulates basic workloads and provides the throughput and the time that a message takes to be consumed by a consumer. For each deployed RabbitMQ server we used a number of producers and consumers that ranges from 50 to 500. Each producer sends messages to the broker with a rate of 100 messages per second for a period of time of 90 seconds. The information available in the dataset are as follows: time: timestamp of collection of metrics. ram_limit: the memory allocated to the container in megabytes. cpu_limit: the CPU allocated to the container. ram_usage: the amount of memory used by the container at the time of the metrics collection in byte. cpu_usage: the amount of CPU used by the container at the time of the metrics collection. n: the number of producers sending messages to the RabbitMQ server. Min: the minimum consumption time for the producer messages. lat50: the median consumption time for the producer messages. lat75: the least consumption time for the best 75% messages in microseconds. lat95: the least consumption time for the best 95% messages in microseconds. lat99: the least consumption time for the best 99% messages in microseconds.

opencc-by-4.0Aug 2022View details →

zenodo52/100

COgnitive intervention to Restore attention using nature Environment (CORE) study data

The COgnitive intervention to Restore attention using nature Environment (CORE) study is single-blinded, two-group randomized-controlled pilot trial among patients with heart failure. The aims to test the preliminary efficacy of the newly developed Nature-VR, a virtual reality-based cognitive intervention that is based on the restorative effects of nature. The Nature-VR intervention group viewed 3-dimensional nature pictures using a virtual reality headset for 10 minutes per day, 5 days per week for 4 weeks (a total of 200 minutes). The active comparison group, Urban-VR, viewed 3-dimensional urban pictures using a virtual reality headset to match the Nature-VR intervention in intervention dose and delivery mode, but not in content. In this study, 73 participants with heart failure completed the baseline and randomized to either Nature-VR or Urban-VR. The target outcomes were attention, self-care of heart failure, and health-related quality of life (HRQoL). After baseline data collection, 4 follow-up data were collected at 4, 8, 26, and 52 weeks.

opencc-by-4.0Dec 2023View details →

edi52/100

Efficacy and fate of fluridone applications for control of invasive submersed aquatic vegetation in the estuarine environment of the Sacramento-San Joaquin Delta

We conducted a study in the Sacramento-San Joaquin Delta to determine efficacy of the widely used herbicide fluridone in an estuarine ecosystem. The primary goal of SAV removal was restoration of open water habitat for endangered Hypomesus transpacificus (Delta Smelt). Over 18 months and multiple sets of multi-week fluridone applications, we monitored concentrations of fluridone and responses by SAV across pairs of treated and reference sites. Fluridone concentrations in the water were generally below the 2-5 parts per billion required for SAV control. Monitoring demonstrated these low water concentrations were likely due to dissipation by tides, despite use of pelleted fluridone formulations marketed for flowing water environments. Fluridone did, however, accumulate in sediment at concentrations hundreds of times higher than those measured in the water. Nonetheless, we did not observe lasting reductions in SAV abundance or changes in SAV community composition. By demonstrating lack of efficacy of one of the few herbicides permitted for use in this estuary, this study highlights the need for development of SAV management tools tailored to the challenges of hydrologically complex environments like estuaries.

openCC0Aug 2023View details →

edi52/100

Examination of protein-like fluorophores in chromophoric dissolved organic matter (CDOM) in a wetland and coastal environment for the wet and dry seasons of the years 2002 and 2003 (FCE)

Water samples are collected at the end of the dry and the wet season from all LTER sites and stored on ice until return to the lab. They are pre-filtered through pre-combusted GF/F filters and ultrafiltered and concentrated with a Pellicon 2 Mini tangential flow ultrafiltration system.Concentrated samples were then analyzed using fluorescence and SEC-HPLC. This CDOM optical study revealed the presence of two classes of compounds associated with the protein-like peak (peak T; excitation/emission (Ex/Em) maxima at around 280 nm/325 nm), which have very different chemical structures and ecological roles. In addition to proteins, we propose phenolic compounds as possible origins of peak T in coastal and wetland environments. In this study, natural water samples were obtained from subtropical rivers and estuarine environments within the Florida Coastal Everglades (FCE) ecosystem. The samples were ultra-filtered and excitation-emission fluorescence matrices (EEMs) were obtained. The EEMs showed the presence of four peaks with Ex/Em maxima at around 280 nm/325 nm (T), less than 260 nm/460 nm (A), 300 nm/412nm (M), and 350 nm/470 nm (C). To better understand the nature of peak T, the components originating this peak were separated using size exclusion chromatography (SEC) and detected by fluorescence emission at Ex/Em = 280 nm/325 nm. The elution curves revealed the presence of two elution peaks at a molecular weight of greater than 50K (void volume; T1) and around 7.6K (T2). This result suggested the need of cautious interpretation in the use of peak T as a proxy for the detection of proteinaceous materials in wetland and estuarine environments, since significant amounts of potentially interfering phenolic compounds are leached from senescent biomass in wetland and coastal ecosystems. As such EEM spectra of gallic acid an important component of hydrolysable tannins, and condensed tannins extracted from red mangroves (Rhizophora mangle) showed the presence of a peak maxima

openCC (other)Feb 2024View details →

zenodo48/100

Computational Supporting Information for How Chemical Environment Activates Anthralin and Molecular Oxygen for Direct Reaction

The updated version of the dataset contains all original computational results, including validation of the level of theory, molecular structures, and analysis spreadsheets that are in support of our experimental observations of spontaneous reactivity of anthralin/dithranol molecule with molecular oxygen without any catalyst or co-substrate. The paper was published in Journal of Organic Chemistry, 2020, 85(2), 1315–1321 (DOI: 10.1021/acs.joc.9b03133). In the meantime, the science was also also presented at the 8th ELSI Symposium, Tokyo Institute of Technology, Tokyo (Japan); February 3-7, 2020 in the context of molecular catalysis and their role in the chemical evolution of the building blocks of life. This version also has an important update that is being exclusively published here on Zenodo. The selected level of theory (MN15 functional with triple-zeta quality basis set supplemented with BOTH diffuse and polarization basis functions) is further confirmed to be one of the most reasonable one among 98 commonly used functionals.

opencc-by-4.0Apr 2020View details →

zenodo48/100

Ecology and environment predict spatially stratified risk of H5 highly pathogenic avian influenza clade 2.3.4.4b in wild birds across Europe

The data in this repository were used to conduct the analysis outlined in the following bioRxiv preprint: <ul> <li>Sarah Hayes, Joe Hilton, Joaquin Mould-Quevedo, Christl Donnelly, Matthew Baylis, Liam Brierley (2025) "Ecology and environment predict spatially stratified risk of H5 highly pathogenic avian influenza clade 2.3.4.4b in wild birds across Europe" bioRxiv doi:10.1101/2024.07.17.603912</li> </ul> The codes used for the analyses are available at https://github.com/sarahhayes/avian_flu_sdm/  The following lookup table can be used to cross-reference between the variable descriptions in Tables 1 and 2 of the preprint and the files in this repository:   <table> <tbody> <tr> <td> <h3> Variable description </h3> </td> <td> <h3> Filename </h3> </td> </tr> <tr> <td> Minimum elevation (metres above sea level)  </td> <td> elevation_min_10kres.tif </td> </tr> <tr> <td> Maximum elevation (metres above sea level)  </td> <td> elevation_max_10kres.tif </td> </tr> <tr> <td> Difference between minimum and maximum elevation   </td> <td> elevation_diff_10kres.tif </td> </tr> <tr> <td> Modal elevation (metres above sea level)  </td> <td> elevation_mode_10kres.tif </td> </tr> <tr> <td> Normalised Difference Vegetation Index (NDVI)  </td> <td> ndvi_*_quart_2022_eco_rasts.tif </td> </tr> <tr> <td> Land cover </td> <td> landcover_output_full_2022_10kres.tif </td> </tr> <tr> <td> Distance to coast </td> <td> dist_to_coast_10kres.csv </td> </tr> <tr> <td> Distance to inland water </td> <td> dist_to_water_output_10kres.csv </td> </tr> <tr> <td> Relative humidity </td> <td> mean_relative_humidity_q*_10kres_eco_quarts.tif </td> </tr> <tr> <td>Seasonal weighted mean of the month-wise difference in the minimum temperature and maximum temperature (degrees Celsius)  </td> <td> mean_diff_*_quart_eco_rasts.tif  </td> </tr> <tr> <td>Seasonal weighted mean of monthly mean temperatures (degrees Celsius) (Mean monthly temperature for each month calculated using: Mean temperature = Minimum temperature + diurnal range/2)</td> <td> mean_mean_*_quart_eco_rasts.tif  </td> </tr> <tr> <td>Seasonal temperature variation (degrees Celsius) (Difference between the maximum and minimum of mean monthly temperature values across months majority-represented within the season)</td> <td> variation_in_quarterly_mean_temp_q*_eco_rasts.tif </td> </tr> <tr> <td> Precipitation  </td> <td> mean_prec_*_quart_eco_rasts.tif  </td> </tr> <tr> <td>Seasonal mean of daily zero-degree isotherm (metres above sea level) </td> <td> isotherm_mean_q*_eco_rasts.tif </td> </tr> <tr> <td>Number of days the zerodegree isotherm was below 1 metre at midday at Coordinated Universal Time (UTC)  </td> <td> isotherm_midday_days_below1_q*_eco_quarts.tif </td> </tr> <tr> <td> Chicken density </td> <td> chicken_density_2010_10kres.tif </td> </tr> <tr> <td> Duck density </td> <td> duck_density_2010_10kres.tif </td> </tr> <tr> <td> Anatinae (dabbling ducks) </td> <td> anatinae_rast_eco_bds.tif </td> </tr> <tr> <td> Anserinae (swans and geese) </td> <td> anserinae_rast_eco_bds.tif </td> </tr> <tr> <td> Ardeidae (herons)  </td> <td> ardeidae_rast_eco_bds.tif </td> </tr> <tr> <td> Arenaria/Calidris (turnstones and sandpipers) </td> <td> arenaria_calidris_rast_eco_bds.tif </td> </tr> <tr> <td> Aythyini (diving ducks)</td> <td> aythyini_rast_eco_bds.tif</td> </tr> <tr> <td> Laridae (gulls) </td> <td> laridae_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage time spent feeding within 2m of water surface </td> <td> around_surf_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage time spent feeding >2m below water surface </td> <td> below_surf_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage diet plants </td> <td> plant_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage diet scavenging </td> <td> scav_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage diet endothermic vertebrates </td> <td> vend_rast_eco_bds.tif </td> </tr> <tr> <td> Congregative </td> <td> cong_rast_eco_bds.tif </td> </tr> <tr> <td> Migratory </td> <td> migr_rast_eco_bds.tif </td> </tr> <tr> <td> Below threshold phylogenetic distance to known host species  </td> <td> host_dist_rast_eco_bds.tif </td> </tr> <tr> <td> Species richness </td> <td>  species_richness_rast_eco_bds.tif </td> </tr> </tbody> </table>

opencc-by-4.0Aug 2024View details →

zenodo48/100

Curated mode-of-action data and effect concentrations for chemicals relevant for the aquatic environment

Chemicals in the aquatic environment can be harmful to organisms and ecosystems. Knowledge on effect concentrations as well as on mechanisms and modes of interaction with biological molecules and signaling pathways is necessary to perform chemical risk assessment and identify toxic compounds. To this end, we developed criteria and a pipeline for harvesting and summarizing effect concentrations from the US ECOTOX database for the three aquatic species groups algae, crustaceans, and fish and researched the modes of action of more than 3,300 environmentally relevant chemicals in literature and databases. We provide a curated dataset ready to be used for risk assessment based on monitoring data and the first comprehensive collection and categorization of modes of action of environmental chemicals. Authorities, regulators, and scientists can use this data for the grouping of chemicals, the establishment of meaningful assessment groups, and the development of in vitro and in silico approaches for chemical testing and assessment.

openMay 2023View details →

zenodo48/100

Thermal infrared emissivity spectral library of silicates measured under the Mercury simulated environment

This is the thermal emissivity spectral library of silicates measured as a function of temperature under Mercury simulated environment. Data is measured at the Planetary Spectroscopy Laboratory (PSL), Institute of Planetary Research, German Aerospace Center (DLR), Berlin. The spectral library will be used for mineral identification of Mercury surface using MERTIS datasets. The manuscript related to this work is submitted to Icarus on the title "Thermal Infrared Spectroscopy (7-14 µm) of Silicates under Simulated Mercury Daytime Surface Conditions and their Detection: Supporting MERTIS onboard the BepiColombo Mission".

opencc-by-4.0Apr 2023View details →

zenodo48/100

Collection of spatial information and maps of human past and environment in the Uralic languages speaker area

The collection of spatial information and maps of the past and environment in the Uralic languages speaker area consists excessive amount of multidisciplinary data related to the vast region extending from Eastern Europe to Siberia, encompassing countries like Russia, Finland, and parts of Scandinavia. Uralic speakers are predominantly found in this region, with historical roots in areas around the Ural Mountains and adjacent territories. These datasets can be integrated for multidisciplinary purposes, allowing to explore human-environment interactions, migration patterns, and cultural evolution over time. Datasets are collected initially by the BEDLAN team <a href="https://bedlan.net/">https://bedlan.net/</a>  - a research group specialized in various disciplines - linguists, archaeologists, geneticists, and geographers. The data collection and mapmaking have grown beyond the initial stages (publications, applications, exhibitions), hence collaborative effort for data publishing is now crucial. As the data collections and mapmaking continue to evolve dynamically together with ongoing projects, the current repository will be updated accordingly.

opencc-by-4.0Oct 2023View details →

zenodo48/100

Simulated severe convective wind events and environments from the Bureau of Meteorology Atmospheric Regional Projections for Australia (BARPA)

Contacts for further details: <ul> <li>This data record and associated research: Andrew Brown (andrewb1@student.unimelb.edu.au)</li> <li>BARPA data: Chun Hsu Su (chunhsu.su@bom.gov.au), Christian Stassen (Christian.Stassen@bom.gov.au), Harvey Ye (harvey.ye@bom.gov.au)</li> </ul> <h1>Introduction</h1> This record contains data in support of Brown et al. (2024), including post-processed regional climate model data, automatic weather station observations, and post-processed reanalysis data over southeastern Australia for various time periods over December-Febrary months only (see descriptions below). This data relates to analysis of severe convective wind gusts in historical and future climate, with analysis scripts in <a href="https://github.com/andrewbrown31/BARPA/tree/main/wind_gust_analysis">this repository</a>. The data are described here according to the directory structure of this record (noting the files and directories have been compressed into <code>barpa_data.tgz</code>), as well as the relevant data sources.  <h1>Data sources</h1> <ul> <li>The Bureau of Meteorology Atmospheric Regional Projections for Australia (BARPA). A regional climate model containing a regional (BARPA-R) and convection-permitting (BARPAC-M) configuration, with large-scale forcing from ERA-Interim (1990-2015) and ACCESS1-0 using a historical (1985-2005) and RCP8.5 (2039-2059) forcing. See Brown et al. (2024) and Su et al. (2021) for more details. Note that any reuse of this data should properly acknowledge the Australian Bureau of Meteorology. Note also that the BARPA data used here was produced as part of the <a href="https://www.climatechangeinaustralia.gov.au/en/projects/esci/">Electricity Sector Climate Information project</a> (with licence and disclaimers <a href="https://www.climatechangeinaustralia.gov.au/en/projects/esci/risk-assessment/#Disclaimer">here</a>), with more current BARPA versions (not used here) available at <a href="https://dx.doi.org/10.25914/z1x6-dq28" target="_blank" rel="noopener">https://dx.doi.org/10.25914/z1x6-dq28</a>. </li> <li>Measured wind gusts from automatic weather stations (AWS). Gust data is provided by the <a href="http://www.bom.gov.au/climate/data/stations/">Australian Bureau of Meteorology</a>, from 272 AWS locations over 2005-2015. Note that any reuse of this data should properly acknowledge the Australian Bureau of Meteorology.</li> <li>The ERA5 reanalysis from the European Center for Medium Range Weather Forecasting (Hersbach 2020).</li> <li>The ERA-Interim reanalysis from the European Center for Medium Range Weather Forecasting (Dee 2011).</li> </ul> <h1>/10min_points</h1> This directory contains .csv files, with wind gust and related environmental data at 10-minute intervals at point locations, corresponding to automatic weather station locations. Data is available over 2005-2015. Files are named in the form <code>barpac_m_aws_<state>.csv</code> and <code>barpac_m_aws_<state>_barpa_r_interp.csv</code>. Here, <state> represents different administrative regions in southeast Australia, including New South Wales (nsw), Victoria (vic), South Australia (sa) and Tasmania (tas). See Figure 1 in Brown et al. (2024) for a map of station locations, that is also included in the /meta directory. The <code>barpa_r_interp</code> suffix indicates that the BARPAC-M wind gusts have been interpolated to the BARPA-R grid for comparison. This data is used in Brown et al. (2024) for evaluation and analysis of BARPA wind gusts in the historical climate (forced by ERA-Interim). The user is directed to that paper for more information on data processing. For the .csv files here, column descriptions are provided in Table 1, below. <h1>/daily_points</h1> This directory contains .csv files, with data associated with daily maximum wind gusts at point locations. This data is derived from the 10min_points data described above, with the same column descriptions in Table 1, below. The different files in this directory are as follows: <ul> <li> <code>barpac_m_aws_dmax_obs.csv</code> Daily maximum observed wind gust from AWS measurements, with associated wind gust ratio and environmental conditions from ERA5. </li> <li> <code>barpac_m_aws_dmax_2p2km.csv</code> Daily maximum simulated wind gust from BARPAC-M at closest grid point to AWS location, with associated wind gust ratio, lighting flash count, and environmental conditions from BARPA-R. </li> <li> <code>barpac_m_aws_dmax_12km.csv</code> Daily maximum simulated wind gust from BARPA-R at closest grid point to AWS location, with associated wind gust ratio, lightning flash count, and environmental conditions. </li> <li> <code>barpac_m_aws_dmax_erai.csv</code> Daily maximum simulated wind gust from ERA-Interim at closest grid point to AWS location, with associated wind gust ratio and environmental conditions from ERA5. </li> <li> <code>barpac_m_aws_dmax_2p2km_barpa_r_interp.csv</code> As in <code>barpac_m_aws_dmax_2p2km.csv</code>, but wind gusts are interpolated to the BARPA-R grid prior to calculating the daily maximum. </li> </ul> <h1>/monthly_nc</h1> This directory contains monthly netcdf files at 12 km horizontal grid spacing, with post-processed BARPA data, relating to simulated severe convective wind gusts (from BARPAC-M), and their associated large-scale environments (from BARPA-R). This includes BARPAC-M and BARPA-R data that has been forced by the ACCESS1-0 global climate model, that is intended for analysis of future changes in severe convective wind events and environments. For further information, the user can refer to the internal file metadata, as well as Brown et al. (2024). The files in this directory are as follows (where <experiment> is either <code>hist</code> for historical climate forcing (1985-2005) or <code>rcp</code> for RCP8.5 climate forcing (2039-2059)): <ul> <li> <code>barpac_scws_<experiment>_monthly.nc</code> Monthly counts of simulated severe convective wind events from BARPAC-M, for each type of environment (see <code>cluster</code> in Table 1). </li> <li> <code>barpar_<experiment>_monthly.nc</code> Monthly counts of favouable severe convective wind environments from BARPA-R (using <code>bdsd</code>, see Table 1), for each type of environment (see <code>cluster</code> in Table 1). </li> <li> <code>barpac_scws_bdsd_<experiment>.nc</code> Monthly counts of simulated severe convective wind events from BARPAC-M, that occur under favourable environmental conditions from BARPA-R. For each type of environment (see <code>cluster</code> in Table 1). </li> <li> <code>barpac_max_<experiment>_monthly.nc</code> Monthly maximum simulated severe convective wind gust, from BARPAC-M, for each type of environment (see <code>cluster</code> in Table 1). </li> </ul> <h1>/daily_nc</h1> This directory contains monthly netcdf files at 12 km horizontal grid spacing, with daily maximum wind gusts from BARPAC-M, as well as the wind gust ratio (see <code>wgr_4</code> in Table 1) and the type of convective environment (from BARPA-R, see <code>cluster</code> in Table 1). This includes BARPA data that has been forced by ERA-Interim and by ACCESS1-0. For further information, the user can refer to the file metadata, as well as Brown et al. (2024). The files in this directory are as follows (where <code><experiment></code> is either <code>historical</code> for historical climate forcing or <code>rcp85</code> for RCP8.5 climate forcing, <code><forcing_model></code> is either <code>erai</code> for ERA-Interim or <code>ACCESS1-0</code>, <<code>date1></code> is the file start date and <code><date2></code> is the file end date): <ul> <li><code>barpa_scw_<forcing_model>_<experiment>_0_<date1>_<date2>.nc</code></li> </ul> <h1>/meta</h1> Lists of AWS stations, for each administrative state, with a file containing column descriptions. Note that not all of the stations listed in these files are used for analysis. Fig1.jpeg is from Brown et al. (2024), showing the BARPAC-M domain (also defines the netcdf file spatial extents), and the location of AWS. <h3>Table 1</h3> <table> <tbody> <tr> <td>Column</td> <td>Name</td> <td>Notes</td> </tr> <tr> <td>stn_id</td> <td>Automatic weather station (AWS) identifier</td> <td> </td> </tr> <tr> <td>time</td> <td>Wind gust time (UTC)</td> <td> </td> </tr> <tr> <td>gust</td> <td>Observed wind gust speed from AWS (m/s)</td> <td>Observed wind gusts are measured at a height of 10 m, and represent a 3-second average. Data is provided as a one-minute maximum, and is resampled to a 10-minute maximum here for comparison with BARPA</td> </tr> <tr> <td>wgr_4</td> <td>Wind gust ratio</td> <td>The observed wind gust ratio, defined as the ratio between <code>gust</code>, and the 4-hour mean from the 10-minute data here.</td> </tr> <tr> <td>time_6hr</td> <td>6-hourly time (UTC)</td> <td>The most recent 6-hourly time step prior to <code>time</code>, associated with environmental diagnostics.</td> </tr> <tr> <td>mu_cape</td> <td>Most unstable convective available potential energy (J/kg)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>s06</td> <td>Bulk vertical wind shear from the surface to 6 km above ground level (m/s)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>dcape</td> <td>Downdraft convective available potential energy (J/kg)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>bdsd</td> <td>Brown and Dowdy (2021) Statistical Diagnostic (BDSD) for identifying favourable severe convective wind environments</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>qmean01</td> <td>Mass-weighted mean mixing ratio from the surface to 1 km (g/kg)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>Umean06</td> <td>Mass-weighted mean wind speed from the surface to 6 km above ground level (m/s)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>lr13</td> <td>Temperature lapse rate from 1 km above ground level to 3 km above ground level (◦C/km)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>cluster</td> <td>Environment type</td> <td> Based on statistical clustering of severe convective wind environments by Brown et al. 2023 0: Strong background wind cluster 1: Steep lapse rate cluster 2: High moisture cluster Derived from BARPA-R </td> </tr> <tr> <td>s06_era5</td> <td>See s06</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>qmean01_era5</td> <td>See qmean01</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>Umean06_era5</td> <td>See Umea06</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>lr13_era5</td> <td>See lr13</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>bdsd_era5</td> <td>See bdsd</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>dcape_era5</td> <td>See dcape</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>mu_cape_era5</td> <td>See mu_cape</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>cluster_era5</td> <td>See cluster</td> <td> Based on statistical clustering of severe convective wind environments by Brown et al. 2023 0: Strong background wind cluster 1: Steep lapse rate cluster 2: High moisture cluster Derived from ERA5 </td> </tr> <tr> <td>wg10_12km_point</td> <td>Simulated wind gust from BARPA-R (m/s)</td> <td>Intended to represent a 10 meter wind gust. See Ma et al. (2018) for gust parameterisation details.</td> </tr> <tr> <td>wgr_12km_point</td> <td>Wind gust ratio from BARPA-R </td> <td>See wgr_4</td> </tr> <tr> <td>wg10_2p2km_point</td> <td>Simulated wind gust from BARPC-M (m/s)</td> <td>Intended to represent a 10 meter wind gust. See Ma et al. (2018) for gust parameterisation details.</td> </tr> <tr> <td>wgr_2p2km_point</td> <td>Wind gust ratio from BARPAC-M (see wgr_4)</td> <td>See wgr_4</td> </tr> <tr> <td>n_lightning_fl</td> <td>Number of daily lightning flashes from BARPAC-M</td> <td>See Brown et al. (2024) for more information.</td> </tr> <tr> <td>erai_wg10</td> <td>Simulated wind gust from ERA-Interim (m/s).</td> <td>Intended to represent a 10 meter wind gust. Note that ERA-Interim is provided in 3-hourly intervals, rather than 10-minute intervals for BARPA.</td> </tr> </tbody> </table>

opencc-by-4.0Jan 2024View details →

zenodo48/100

A Danish high-resolution dataset for six office rooms with occupancy, indoor environment , heating, ventilation, lighting and room control monitoring

A dataset containing measurement data for six office rooms in Aalborg Denmark. All the measurements have been resampled to 5 minute resolution The measurements consists of: <ul> <li>BMS data for the rooms</li> <li>Occupancy for the rooms (from cameras)</li> <li>BMS data for the AHU supplying the rooms</li> <li>BMS data for the Heating system supplying the rooms</li> </ul> Changes from v2 It was found that the pressure difference measurements across the exhaust fan was faulty and the following variables have therefore been removed: <ul> <li>Ventilation:Fan__air_flow__exhaust</li> <li>Ventilation:Fan__pressure_difference__exhaust</li> </ul> More data has been added, now increasing the dataset to span the rest of 2023. To better handle the changes between standard time and daylight-saving time the column named "timestamp" has been adjusted so the datetime format now follows the ISO 8601 format YYYY-MM-DDThh:mm:ss+hhmm. the +hhmm changes between 0100 (Danish standard time) and 0200 (Danish daylight-saving time).

opencc-by-4.0Oct 2023View details →

zenodo48/100

Robot@Home2, a robotic dataset of home environments

The Robot-at-Home dataset (Robot@Home, paper <a href="http://mapir.uma.es/papersrepo/2017/2017-raul-IJRR-Robot_at_home_dataset.pdf">here</a>) is a collection of raw and processed data from five domestic settings compiled by a mobile robot equipped with 4 RGB-D cameras and a 2D laser scanner. Its main purpose is to serve as a testbed for semantic mapping algorithms through the categorization of objects and/or rooms. This dataset is unique in three aspects: <ul> <li>The provided data were captured with a rig of 4 RGB-D sensors with an overall field of view of 180°H. and 58°V., and with a 2D laser scanner.</li> <li>It comprises diverse and numerous data: sequences of RGB-D images and laser scans from the rooms of five apartments (87,000+ observations were collected), topological information about the connectivity of these rooms, and 3D reconstructions and 2D geometric maps of the visited rooms.</li> <li>The provided ground truth is dense, including per-point annotations of the categories of the objects and rooms appearing in the reconstructed scenarios, and per-pixel annotations of each RGB-D image within the recorded sequences</li> </ul> During the data collection, a total of 36 rooms were completely inspected, so the dataset is rich in contextual information of objects and rooms. This is a valuable feature, missing in most of the state-of-the-art datasets, which can be exploited by, for instance, semantic mapping systems that leverage relationships like pillows are usually on beds or ovens are not in bathrooms. Robot@Home2 <a href="https://github.com/goyoambrosio/RobotAtHome2">Robot@Home2</a>, is an enhanced version aimed at improving usability and functionality for developing and testing mobile robotics and computer vision algorithms. It consists of three main components. Firstly, a <a href="#bottom">relational database</a> that states the contextual information and data links, compatible with Standard Query Language. Secondly,a <a href="https://pypi.org/project/robotathome/">Python package</a> for managing the database, including downloading, querying, and interfacing functions. Finally, learning resources in the form of <a href="https://drive.google.com/drive/folders/1ENnxbKP5MJdlGl2Q93WTbIlofuy6Icxq">Jupyter notebooks</a>, runnable locally or on the Google Colab platform, enabling users to explore the dataset without local installations. These freely available tools are expected to enhance the ease of exploiting the Robot@Home dataset and accelerate research in computer vision and robotics. If you use Robot@Home2, please cite the following <a href="https://www.sciencedirect.com/science/article/pii/S2352711023001863">paper</a>: Gregorio Ambrosio-Cestero, Jose-Raul Ruiz-Sarmiento, Javier Gonzalez-Jimenez, The Robot@Home2 dataset: A new release with improved usability tools, in SoftwareX, Volume 23, 2023, 101490, ISSN 2352-7110, <a href="https://doi.org/10.1016/j.softx.2023.101490">https://doi.org/10.1016/j.softx.2023.101490</a>. <blockquote>@article{ambrosio2023robotathome2, title = {The Robot@Home2 dataset: A new release with improved usability tools}, author = {Gregorio Ambrosio-Cestero and Jose-Raul Ruiz-Sarmiento and Javier Gonzalez-Jimenez}, journal = {SoftwareX}, volume = {23}, pages = {101490}, year = {2023}, issn = {2352-7110}, doi = {https://doi.org/10.1016/j.softx.2023.101490}, url = {https://www.sciencedirect.com/science/article/pii/S2352711023001863}, keywords = {Dataset, Mobile robotics, Relational database, Python, Jupyter, Google Colab} }  </blockquote> Version history <a href="../record/3901564">v1.0.1</a> Fixed minor bugs. <a href="../record/4495821">v1.0.2</a>  Fixed some inconsistencies in some directory names. Fixes were necessary to automate the generation of the next version. <a href="../record/4499043">v2.0.0</a> SQL based dataset. Robot@Home v1.0.2 has been packed into a <a href="https://www.sqlite.org/index.html">sqlite</a> database along with RGB-D and scene files which have been assembled into a hierarchical structured directory free of redundancies. Path tables are also provided to reference files in both v1.0.2 and v2.0.0 directory hierarchies. This version has been automatically generated from version 1.0.2 through the <a href="https://github.com/goyoambrosio/RobotAtHome2">toolbox</a>. <a href="../record/4530453">v2.0.1</a> A forgotten foreign key pair have been added. <a href="../records/7811783">v.2.0.2</a> The views have been consolidated as tables which allows a considerable improvement in access time. <a href="../records/7811795">v.2.0.3</a> The previous version does not include the database. In this version the database has been uploaded. v.2.1.0 Depth images have been updated to 16-bit. Additionally, both the RGB images and the depth images are oriented in the original camera format, i.e. landscape.

opencc-by-4.0Jul 2023View details →

zenodo48/100

STREAM - Sub-THz Radar sensing of the Environment for future Autonomous Marine platforms: Multi-Perspective Sensing - Maritime Environment - Side-looking Perspective

This dataset contains the files corresponding to which results have been included in the journal paper titled 'High-Resolution Multi-Modal Sensing of Distributed Radar Network'. The full description of the conducted trials and data structure is mentioned in the attached PDF document. The trials were conducted at the Gosport Marina, Portsmouth, UK with a sea state of approximately 3 according to the Douglas Scale. The experiments were performed with automotive radars operating in the 79 GHz band to investigate the Doppler and imaging capabilities of these radars. A multi-sensory suite distributed around Valkyrie VI was mounted in front, corner, side and backward-looking orientations. This dataset contains data from the side-looking orientation, where the installation angle of radar is 90 degrees respective to the platform velocity vector. Radar Data: The radar data is stored in the file 'GM2_Out1_240522_160925.h5'. The methodology to process the data in MATLAB is presented in the attached pdf. document. Inertial Measurement Unit: Three xSens 680G IMU were mounted on the roof, front and back of the boat. They have been included in the corresponding zip folders. PC3_Corner_RLG: IMU at the corner of the boat. PC4_Forward_RLG: IMU at the roof of the boat. PC5_Backward_RLG: IMU at the back of the boat. The IMU data is converted to .txt files that can be directly loaded into MATLAB. Timestamped Velocity: The file 'Corner_160925.mat' contains the time-stamped velocity for each radar frame. Here, the integration interval is 128 ms with 512 radar chirps. The file 'CommonFramesCornner_160925.mat' contains the timestamped velocity for the frames that are synchronised with the frames of front-looking radar. (The dataset for the front-looking radar is stored in another repository with DOI: 10.5281/zenodo.14215115) Camera: Each radar also has a camera for ground truth. The time-stamped camera frames for each radar frame are stored in 'CommonFramesCornner_160925.mat'. Processed camera frames and video of the scene are available in: 'GM2_Corner_240522_160925_CameraFrames.zip'.   For more information, please contact: Anum Pirkani: a.a.a.pirkani@bham.ac.uk, anum.apirkani@gmail.com Marina Gashinova: m.s.gashinova@bham.ac.uk

opencc-by-4.0Nov 2024View details →

zenodo48/100

STREAM - Sub-THz Radar sensing of the Environment for future Autonomous Marine platforms: Multi-Perspective Sensing - Maritime Environment - Front-looking Perspective

This dataset contains the files corresponding to which results have been included in the journal paper titled 'High-Resolution Multi-Modal Sensing of Distributed Radar Network'. The full description of the conducted trials and data structure is mentioned in the attached PDF document. The trials were conducted at the Gosport Marina, Portsmouth, UK with a sea state of approximately 3 according to the Douglas Scale. The experiments were performed with automotive radars operating in the 79 GHz band to investigate the Doppler and imaging capabilities of these radars. A multi-sensory suite distributed around Valkyrie VI was mounted in front, corner, side and backward-looking orientations. This dataset contains data from the front-looking orientation, where the installation angle of radar is 0 degrees respective to the platform velocity vector. Radar Data: The radar data is stored in the file 'GM2_Lab_240522_160943.h5'. The methodology to process the data in MATLAB is presented in the attached pdf. document. Inertial Measurement Unit: Three xSens 680G IMU were mounted on the roof, front and back of the boat. They have been included in the corresponding zip folders. PC3_Corner_RLG: IMU at the corner of the boat. PC4_Forward_RLG: IMU at the roof of the boat. PC5_Backward_RLG: IMU at the back of the boat. The IMU data is converted to .txt files that can be directly loaded into MATLAB. Timestamped Velocity: The file 'Front_160943.mat' contains the time-stamped velocity for each radar frame. Here, the integration interval is 128 ms with 512 radar chirps. The file 'CommonFramesFront_160943.mat' contains the timestamped velocity for the frames that are synchronised with the frames of side-looking radar. (The dataset for the side-looking radar is stored in another repository with DOI: 10.5281/zenodo.14174138) Camera: Each radar also has a camera for ground truth. The time-stamped camera frames for each radar frame are stored in 'CommonFramesFront_160943.mat'. Processed camera frames and video of the scene are available in: 'GM2_Front_240522_160943_CameraFrames.zip'.   For more information, please contact: Anum Pirkani: a.a.a.pirkani@bham.ac.uk, anum.apirkani@gmail.com Marina Gashinova: m.s.gashinova@bham.ac.uk

opencc-by-4.0Nov 2024View details →

zenodo48/100

Soil visible–near infrared (vis–NIR) spectra for the Biomes of Australian Soil Environments (BASE) soil microbial diversity database

Visible–near infrared spectra of 695 soil samples collected in the Biomes of Australian Soil Environments (BASE) soil microbial diversity project (Bissett et al., 2016). The spectra represent reflectance values from 2151 wavelengths that range from 350 nm to 2500 nm with a 1 nm interval. The dataset has unique sample identification numbers and the date of sampling, which can be related to the BASE (Australian Microbiome) database (https://data.bioplatforms.com/organization/australian-microbiome)

opencc-by-4.0Feb 2022View details →

zenodo48/100

Virtual Research Environments Ethnography: a Preliminary Study

Datasets accompanying the paper “Virtual Research Environments Ethnography: a Preliminary Study”, a systematic mapping study on the literature about Science gateways, Virtual Research Environments, and Virtual Laboratories. While for legal reasons we can not share the original datasets obtained by querying the databases, since they include copyrighted data, we can share the two datasets derived from the query results and the two topic modelling datasets. The dataset “main_dataset.csv” consists of the merged query results from ACM Digital Library, IEEEXplore, ScienceDirect, Scopus, and SpringerLink databases. It is structured into six columns: (i) doi; (ii) title; (iii) content_type; (iv) publication year; (v) keyword_search; (vi) DB. The ‘doi’, ‘title’, and ‘publication_year’ labels are self-describing, and are used for the DOIs, titles, and publication years (in the yyyy format) respectively. The ‘content_type’ label refers to the different and normalised typologies of resources: (a) Article; (b) Book, (c) Book Chapter; (d) Chapter; (e) Chapter ReferenceWorkEntry; (f) Conference Paper; (g) Conference Review; (h) Early Access Articles; (i) Editorial; (j) Erratum; (k) Letter; (l) Magazines; (m) Masters Thesis; (n) Note; (o) Ph.D. Thesis; (p) Retracted; (q) Review; (r) Short Survey; (s) Standards. (c) and (d) refer to the same type of entry (they are used in different databases), while in the case of (e) we observed that it is used in the Springer database to refer mainly to encyclopaedic entries. The ‘keyword_search’ label is used for identifying the keyword group used for formulating the query: (a) science gateway | scientific gateway; (b) virtual laboratory | Vlab; or (c) virtual research environment. The ‘DB’ label indicates the provenance of the entries from one of the five databases we selected for our study: (a) ACM; (b) IEEE; (c) ScienceDirect; (d) scopus; and (e) Springer, identifying the ACM Digital Library, IEEEXplore, ScienceDirect, Scopus, and SpringerLink respectively. The dataset “filtered_dataset.csv” consists of the deduplicated and filtered entries (journal articles and conference papers from 2010 onward, with a DOI assigned) from the “main_dataset.csv” we used as the final dataset for answering our research questions. It is structured into ten columns: (i) doi; (ii) title; (iii) venue; (iv) publication_year; (v) content_type; (vi) abstract; (vii) keywords; (viii) science gateway | scientific gateway; (ix) virtual laboratory | Vlab; and (x) virtual research environment. As for the previous dataset, the ‘doi’, ‘title’, and ‘publication_year’ labels are self-describing, and are used for the DOIs, titles, and publication years (in the yyyy format) respectively. The ‘venue’ label is used for indicating the conference or the journal the entries refer to. The values derive from the original query results. The ‘abstract’ and ‘keyword’ labels are used for the abstracts and the keywords associated with the entries. The values are mainly derived from the original query results, as we integrated the missing ones by querying OpenAIRE. The ‘science gateway | scientific gateway’, ‘virtual laboratory | Vlab’ and ‘virtual research environment’ labels indicate the connection between the entries and the keyword group used for denoting them. The values are binary (1 if the keywords belong to the group, 0 if they do not). The datasets “sg_vlab_vre_topics_datasets.csv” and “sgvlabvre_topics_dataset.csv” consist of the three datasets and of the unique dataset resulting from topic modelling, the first (corpus divided into three datasets) and the second analysis (corpus as a whole) respectively. They share the same structure: (i) Topic; (ii) #studies; (iii) Representative word; (iv) Representative word weight. The ‘Topic’ label is used for the topic denomination and the values consist of an alphanumeric string indicating the dataset and the progressive topic number: (a) SG, for the scientific gateway dataset; (b) VRE, for the virtual research environment dataset; (c) VLAB, for the virtual laboratory dataset; and (d) A, for the corpus as a whole. The ‘#studies’ label indicates the number of studies contributing to each topic. The ‘Representative word’ and ‘Representative word weight’ labels are used for denoting the keywords describing each topic and their weights respectively.

opencc-by-4.0Dec 2021View details →

zenodo48/100

Battery-less Environment Sensor Using Thermoelectric Energy Harvesting From Soil-Ambient Air Temperature Differences

The data set contains the data collected from experiments sites in Belgium ( Campus Drie Eiken, University of Antwerp, 51.161° N, 4.408° W) and Iceland ( Forhot, 64.008° N, 21.178° W) for the research and evaluation of a battery-less environment sensor powered by energy harvesting. The device uses the temperature difference between soil and air to produce energy with the help of a Thermoelectric Generator (TEG) and powers a wireless sensor node. The data set includes data collected from 2 phases of the study. One during the initial evaluation phase where we collected soil temperatures at 15 cm and air temperature to evaluate the possibilities of producing energy from the temperature differences. Using these data, we estimated the energy production capacity for both sites. Further, a proof-of-concept device was developed, and its performance was evaluated with field experiments. During this process, we collected the voltage level of the storage unit, i.e,  the capacitor, air and soil temperatures and the TEG output voltage. During both phases, the same methods were employed to collect data. The voltage values were measured with a 12-bit ADC and the temperature was measured with 1-Wire temperature sensor. Further, the collected data were transferred to cloud storage in real-time for further analysis and evaluation.  <ul> <li>cde_mseasurements_oct2020-nov2020.csv <ul> <li> Soil temperature and air temperature data from the Campus Drie Eiken at the  University of Antwerp, Belgium. The data were collected from 2 Oct 2020 to 17 Nov 2020.</li> </ul> </li> <li>cde_teg_measurements.csv <ul> <li>Soil temperature, ambient temperature and the open-circuit voltage of TEG  from Campus Drie Eiken at the  University Antwerp, Belgium from 21 Apr 2021 to 25 Apr May 2021. Also includes the difference calculated between the two temperature values.</li> </ul> </li> <li>cde_energy_simulated.csv <ul> <li>Energy production capacity estimated using the temperature data collected from Campus Drie Eiken at the University of Antwerp.</li> </ul> </li> <li>aui_measurements_nov-2021.csv <ul> <li>Soil temperature and air temperature data from the Forhot research site in Iceland for the month of November 2021.</li> </ul> </li> <li>aui_teg_measurements.csv <ul> <li>Soil temperature, ambient temperature and the open-circuit voltage of TEG collected from the Forhot research site in Iceland. Also includes the difference calculated between the two temperature values. The data were collected from 18 Nov 2021 to 30 Nov 2021</li> </ul> </li> <li>aui_energy_simulated.csv <ul> <li>Energy production capacity estimated using the temperature data collected from the Forhot research site in Iceland.</li> </ul> </li> <li>cde_capacitor_voltage.csv <ul> <li>The voltage level of the capacitor used by the battery-less device to buffer the harvested energy.  The device was deployed at the Campus Drie Eiken and the data collection was carried out from 1 Mar 2022 to 12 Apr 2022. A 15 mF supercapacitor was used. </li> </ul> </li> </ul>

opencc-by-4.0Jun 2022View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record