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16,872 results for “Differences”
A Comprehensive Global Aquatic N2O Emission Database (GANED): Unravelling N2O Emission Patterns from Different Water Bodies, 1980-2023
The Global Aquatic Nitrous Oxide Emission Database (GANED) is a comprehensive synthesis of empirical observations of N2O concentration measurements and flux records, spanning the period 1980-2023. GANED advances N2O research by providing the first global systematic emission mechanisms among the different aquatic system types, including rivers, streams, estuaries, reservoirs, ponds, lakes, open seas and coastal areas. The N2O data in GANED is further interconnected with biogeochemical metadata on dissolved oxygen, dissolved organic carbon, ammonium, nitrate, nitrite, total nitrogen, water temperature, salinity and pH, along with site data (latitude, longitude, codes of channel type, depth, surface area, elevation). The dataset explains the discrepancy that emission of N2O in aquatic bodies is determined mainly by substrate availability, and not by climatic factors, and reveals the systematic biases of concentration-only measurements, which can result in an underestimation of fluxes in effluent water of dynamically changing aquatic waters. Consequently, GANED constitutes a crucial transition “where” emissions occur to understanding “why” they differ across systems, and thus enabling targeted mitigation interventions. GANED includes 5130 records of N2O concentration and 7386 flux measurements from 3,002 unique sites, most of which are resolved to the daily time scale.
Normalized Difference Vegetation Index (NDVI) derived from 2021 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates two vegetation indices —Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI)— from the National Agriculture Imagery Program (NAIP) remotely sensed imagery. The intent is to make remotely sensed variables and visualizations accessible to stakeholders and researchers studying the Phoenix metropolitan area. NDVI and SAVI are calculated from the 2021 NAIP imagery (1m resolution). This dataset extends the 2010, 2013, 2015, 2017, and 2019 NDVI and SAVI products derived from NAIP imagery (also 1m resolution). All images are cropped to the CAP LTER study area boundary of central Arizona, USA. The materials presented here include NDVI data with SAVI data presented in a companion dataset that is also available through the EDI.
Long-term composited Modified Normalized Difference Water Index (MNDWI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2023
Abstract ======== This data package consists of multiple decades of modified normalized difference water index (MNDWI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona (USA), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). By providing a metric by which to reliably identify bodies of open water, these MNDWI data are intended to facilitate analyses of land-based environmental variables (e.g., urbanization, vegetation, land surface temperature) and can also be used to track long-term and seasonal change in the coarse extent of open water as a land-cover type. MNDWI was derived, following the methods of Xu (2006), from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see \'Methods and Protocols\') and accompanying Javascript code. **Citations:** - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18--27. <https://doi.org/10.1016/j.rse.2017.06.031> - Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. *International Journal of Remote Sensing*, *27*(14), 3025--3033. <https://doi.org/10.1080/01431160600589179>
Soil Moisture at Three Different Dune Elevations on the Hog Island, Northampton County, VA 2023
In summer 2023, soils were collected from swale grasslands (embryonic, Intermediate [swale 1] and Inland [swale 2]) on southern Hog Island. They were kept in plastic bags to quantify soil moisture content, which was determined by weighing cores to obtain water mass before and after drying at 105 deg_C for 72 hours. For details, see: Woods, N.N., Zinnert, J.C. Shrub encroachment of coastal ecosystems depends on dune elevation. Plant Ecol 225, 1047-1057 (2024). https://doi.org/10.1007/s11258-024-01453-2
Valence processing differs across stimulus modalities (Multi-echo)
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Individual Differences in Fluid Reasoning and RAPM-like Problem Solving
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Darwin: an amino acid sequence collection of complete proteomes from eukaryotes with different phylogenetic affinities (v. 03_2020_137)
<p><strong>Background</strong></p> <p>Every time we find an interesting gene in an organism of interest, the first question is often “how widely is this gene distributed in the eukaryotic kingdom?”. Naturally, one could use NCBI BLAST search against the non-redundant sequence database provided by GenBank to answer this question. However, it can be cumbersome to parse the results and assign them to taxonomic units. It is also not straightforward to get an overview of which eukaryotic groups are represented in the results. Top BLAST hits can be crowded with sequences from closely-related organisms making it difficult gain an overview of the overall distribution across eukaryotes. To streamline this process, we developed an in-house database of complete eukaryotic proteomes. We tagged each sequence with a eukaryotic group handle (two-character symbol) and combined them into a single data set searchable by standalone BLAST on one’s own computer. We named this data set “Darwin” to reflect the diverse nature of the sequences it contains. </p> <p><strong>Methods</strong></p> <p>We downloaded predicted proteomes in FASTA format from different sources such as GenBank, Joint Genome Institute (Depart of Energy, USA), Broad Institute (Massachusetts Institute of Technology, USA), Phytozome and a number of other specialized websites catering for a specific organism such as the Arabidopsis Information Resource (TAIR), or the Saccharomyces Genome Database (SGD). All the organisms we included in Darwin are listed in Table 1. To reduce redundancy, we took care not to include the same species more than once unless subspecies were known to show wide diversity. Each sequence header was tagged with a eukaryotic group handle composed of two-character symbols (based on Keeling <em>et al</em>., 2005). These handles clearly appear in BLAST output and can be parsed easily. We combined sequences from all proteomes into a single data set and named it “Darwin”.</p> <p><strong>Results</strong></p> <p>The current version of Darwin (v. 03_2020_137) contains 2,601,132 amino acid sequences from 137 eukaryotes (Table 1, Data file 1). The sizes of the proteomes were diverse, ranging from ~4000 sequences in some alveolates to 60,000-76,000 in plants. Darwin represents most of the supergroups of eukaryotic kingdom described in Keeling <em>et al.,</em> (2005) except those in Rhizaria whose genomes were not available at the time of data set construction. The data set contains larger numbers of proteomes from fungi and plants reflecting areas of interest in our group. </p> <p><strong>Conclusions</strong></p> <p>Darwin is provided as a text fasta file that can be formatted for BLAST searches on standalone computers. The results from the BLAST searches can be parsed to determine how widely a gene of interest is distributed among different eukaryotes. Simple counting of the eukaryotic group handles would also yield an overview of the distribution across taxa. Darwin is also useful for rapidly finding out whether a gene is missing in particular taxa.</p> <p><strong>Reference</strong></p> <p>Keeling PJ, Burger G, Durnford DG, Lang BF, Lee RW, Pearlman RE, Roger AJ, Gray MW (2005) The tree of eukaryotes. <em>Trends Ecol. Evol.</em> <strong>20:</strong> 670-676</p>
Data on anatomy, movement, and foraging behaviour of three cattle breeds of different productivity
<p>Given are</p> <ul> <li>the breed of the cattle (AH: Angus×Holstein, OB: Original Braunvieh, HC: Highland cattle),</li> <li>the age of the cows in months,</li> <li>the body weight at the beginning (Weight_1) and the end (Weight_2) of the experiment in kg,</li> <li>the summarised base of all eight claws of each cow in cm<sup>2</sup>,</li> <li>the average number of steps per hour as recorded by the pedometer,</li> <li>the average speed in m h<sup>-1</sup>,</li> <li>the ratio of the time spent lying as recorded by the pedometer,</li> <li>the evenness of space use calculated as Camargo’s index based on GPS positions,</li> <li>the evenness of forage selection calculated as Pielou’s evenness,</li> <li>the average forage quality indicator value (Briemle, Nitsche, and Nitsche 2002) of the selected diet,</li> <li>the ratio of broad leaved grasses, legumes, thistles and shrubs within the diet of each cow.</li> </ul> <p>All measurements conducted on the pastures are presented as averaged over all pastures (xxx_mean) and separatly for the three pastures (xxx_1, xxx_2, xxx_3).</p>
Surface alkalinity, pH (total scale) and CO2 air-sea flux of the Mediterranean Sea under different alkalinisation scenarios.
<p>Surface maps and basin mean/total of annual mean surface alkalinity, pH (total scale) and CO2 air-sea flux of the Mediterranean Sea under different alkalinisation scenarios and for underlying the baseline projection (RCP4.5).</p> <p>Details on simulations and alkalinisation strategies are given in the reference article below.</p> <p> </p> <p>Reference:</p> <p>Butenschön, M., Lovato, T., Masina, S., Caserini, S., Grosso, M., 2021. Alkalinization Scenarios in the Mediterranean Sea for Efficient Removal of Atmospheric CO2 and the Mitigation of Ocean Acidification. Front. Clim. 3. <a href="https://doi.org/10.3389/fclim.2021.614537">https://doi.org/10.3389/fclim.2021.614537</a></p>
Detailed abundances based on different nuclear physics for theoretical r-process scenarios
<p>This data set contains detailed abundances (at a time t=10^6 years after the event) for individual trajectories for seven different simulations of potential r-process sites, and based on nine different combinations of nuclear mass models and fission fragment distribution models. The data have been used and are discussed in Cote, Eichler, Yagüe, et al. (https://ui.adsabs.harvard.edu/abs/2020arXiv200604833C/abstract) to determine the isotopic ratios of I129/Cm247 and compare them to meteoritic data.</p> <p>Furthermore, a code is included which samples a subset of trajectories reproducing the measured meteoritic I129/Cm247 abundance ratio of 438 +- 92. See the README file and the publication (https://ui.adsabs.harvard.edu/abs/2020arXiv200604833C/abstract) for more details.</p>
Pre-trained models for segmentation and tracking of Coronal Bright Fronts from SDO AIA Base Difference images
<p>Here we present pretrained U-NET-based models followed by SDO AIA Base Difference(BD) validation set after intensity tresholding [-50;150] with predicted feature masks samples. <br>We provide a command-line Python utility for image segmentation using our CNNs designed to process images of solar eruptive phenomena. The https://gitlab.com/iahelio/helios_cnn repository includes regularly updated and newly published models. </p> <p>First model we present is designed to predict the likelihood of each pixel belonging to a certain class or feature in the solar image. A probabilistic output allows for a more nuanced interpretation of ambiguous region. The output can be converted into binary masks through thresholding. The range of values also gives insights into the model's confidence</p> <p>We also present sample segmentation results and the second model designed to produce binary masks.</p>
BST/NOAA PSL Level 3 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH
<p>This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). While Level 2 provides each product at their highest retrieved spatial resolution, Level 3 provides all four products on a common grid at each flight location. These data were collected near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 18th, 2023. Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies, Inc. </p> <p> </p> <p>Each Level 3 NetCDF file contains all four UAS parameters at a flight location interpolated to a common rectilinear grid at ~50 cm resolution. Soil moisture retrievals were downscaled to a higher resolution grid using bilinear interpolation while surface temperature, NDVI, and digital elevation were upscaled to a lower resolution grid using conservative interpolation. The data was regridded using the Python package xESMF which is based on code developed for the Earth System Modeling Framework (ESMF) project. </p> <p> </p> <p>The file name convention for the Level 3 NetCDF files is as follows.</p> <p> </p> <p>uas_L3_yyyymmdd_hhmmss_vx.x.nc</p> <p>where</p> <p>L3 = Level 3 data </p> <p>yyyymmdd = year,month,day</p> <p>hhmmss = hour,minute,second</p> <p>x.x = version number </p> <p>Time is the flight start time in UTC.</p> <p>Version number description is provided in the NetCDF global attributes.</p> <p> </p> <p>Note that each flight location using the E2 aerial platform required two flights with different starting flight times for the soil moisture and the other three products. The flight start time is the time of the first flight. The total time for the two flights at each location was ~1 hour. </p> <p><strong>November 2023 update</strong>: Version 2.0 added flight data from 2023. Version 2.0 includes an updated calibration of the soil moisture retrieval that has been applied to 2023 data, and a mask was applied to the soil moisture retrieval over water surfaces for both 2022 and 2023 data. Version 2.1 adds data file uas_L3_20221018_171650_v2.1.nc that was missing in Version 2.0.</p> <p><strong>December 2023 update</strong>: Version 2.2 updated soil moisture data with a wet bias in v2.1 for flights #2 (17:40:35 UTC) and #3 (19:24:45 UTC) on July 27, 2022.</p>
Dataset: Label-free detection of methicillin resistance in Staphylococcus aureus using different Raman-spectroscopy approaches
<p>This is the dataset accompanying the submission of the manuscript: Label-free detection of methicillin resistance in Staphylococcus aureus using different Raman-spectroscopy approaches in the journal Microbiology Spectrum.</p> <p>The data description is the following:</p> <p>Strains<br> 16859MRSA= Strain AUSTR-07-16859 MRSA<br> 16859MSSA= Strain AUSTR-07-16859 MSSA<br> CC8MRSA= Strain 08V15773<br> CC8MSSA= Strain MRSA2010-174<br> AUSTR05MRSA= Strain AUSTR-05-15441 MRSA<br> AUSTR05MSSA= Strain AUSTR-05-15441 MSSA<br> CC361MRSA= Strain UAE-Abu Dhabi-020<br> CC361MSSA= Strain UAE-Dubai-80-MS 1368.9/09</p> <p>Datasets<br> UVRR: UV-Resonance Raman with 244 nm excitation on bulk samples, calibration standard Polystyrene, measurements were time series of 10 consecutive spectra, for each strain and batch 25 time series were collected from 3 different slides<br> 532nm: Single cell analysis with 532nm excitation, calibration standard 4AAP, one spectrum per bacterial cell was collected<br> 785nm: Bulk analysis of bacterial colonies using 785 nm excitation and a Raman fibre probe, calibration standard 4AAP, bulk analysis, individual spectra of colonies were collected</p> <p>Data structure is in the metadata files.<br> Individual spectra are in the folders sorted by the date they were measured.</p>
Dataset - paper: Parental feeding practices and parental involvement in child feeding in Denmark: gender differences and predictors
<p>Dataset corresponding to a paper that has been accepted for publication in Appetite (Philippe, K., Chabanet, C., Issanchou, S., Grønhøj, A., Aschemann-Witzel, J., & Monnery-Patris, S. (2022, in press). <em>Parental feeding practices and parental involvement in child feeding in Denmark: gender differences and predictors</em>. Appetite).</p> <p>The objectives of the study were (1) to examine possible differences between Danish mothers and fathers with regard to their involvement in child feeding and their feeding practices, and (2) to identify possible parent-related predictors of parental feeding practices and of parental involvement in child feeding at home.</p> <p>Information about the dataset and the corresponding documents can be found in the document "Metadata-paper-Denmark.docx".</p>
Artisanal and farmer bread making practices differently shape fungal species community composition in French sourdoughs
<p>Datasets describing the fungal species diversity, microbial density and acidity of French sourdoughs, phenotypic variation of Kazachstania bulderi and Kazachstania humilis strains as well as the diversity of bread-making practices of 40 bakers and farmers-bakers.The data were collected, analyzed, and reported within the following publication :</p> <p>Elisa Michel, Estelle Masson, Sandrine Bubbendorf, Léocadie Lapicque, Thibault Nidelet, Diego Segond, Stéphane Guézenec, Thérèse Marlin, Hugo deVillers, Olivier Rué, Bernard Onno, Judith Legrand, Delphine Sicard and the participating bakers: <strong>Artisanal and farmer bread making practices differently shape fungal species community composition in French sourdoughs</strong>. PCI Evol. Biol.</p> <p> </p>
Matching results between landmark in different sources and landmark in a referenced dataset (BDTOPO)
<p>The four datasets represent the results of a two sequentials processus. The first processus consists on a automatic matching between landmark in different sources and landmark in a referenced dataset (french national topographic data: BDTOPO). Then the links 1:1 are manually validated by experts in the second processus.</p> <p>The four different datasets and the BDTOPO dataset are archived <a href="https://doi.org/10.5281/zenodo.6480986">here</a>.</p> <p>The data matching algorithm is described in this <a href="http://dx.doi.org/10.5311/JOSIS.2015.10.194">paper</a>.</p> <p>Each file represents the result matching for features belonging to a data source with:</p> <p>- the name of file depending on the data source</p> <p>- the column "id_source" corresponds to the identifier of the landmark in data source</p> <p>- the column "types_of_matching_results" describes the type of matching result:</p> <ul> <li>« 1:0 »: means that a landmark from a data source (e.g. Camptocamp) has no homologue landmark in BDTOPO</li> <li>« 1:1 validated »: means that a homologous feature exist in BDTOPO and the link was validated</li> <li>« 1:1 non validated »: means that the matching link was not validated</li> <li>« without candidates »: represents the non-matched landmarks because there are no candidates in BDTOPO or because the landmark in data source is far away from its homologous in BDTOPO</li> <li>« uncertain »: uncertainty cases are complex cases where any decision is taken by the data matching algorithm</li> </ul> <p>- the column "id_candidat" corresponds to the identifier of the landmark in BDTOPO if and only if there is a validated matching link</p> <p>- the column "samal" corresponds to the <a href="https://doi.org/10.1080/13658810410001658076">Samal distance</a></p> <p>The matching results are obtained using an ontology application named <a href="http://choucas.ign.fr/doc/ontologies/index-fr.html">OOR</a>. These specific results are obtained using the version of OOR V1.0.1 which is an improved version and contains new concepts compared to the first release 1.0.0. The new version of OOR (i.e. 1.0.1) will be released by the end of May 31 2022. The new link will be added here.</p> <p>This archive is released for transparency and reproducibility purposes.</p>
Alignment between type of landmark in different sources and the concept in the spatial reference objects ontology
<p>The five datasets represent a manually alignment between the landmark type of five different datasets archived <a href="https://doi.org/10.5281/zenodo.6480986">here</a> and a common vocabulary extracted from an application ontology defined for mountain rescue purposes, named <a href="https://hamac.ign.fr/owa/redir.aspx?C=cjlWje9SCaYsVOTLbxbOoIBLZUCS56nVb248cRSMTEDSENDFzybaCA..&URL=http%3a%2f%2fchoucas.ign.fr%2fdoc%2fontologies%2foor.owl%2f">Ontology of landmarks</a> (OOR).</p> <p>Each file represents the alignment for features belonging to a data source with the same OOR ontology.</p> <p>For example, the type «bivouac» from camptocamp.org source is aligned with the uri <a href="http://purl.org/choucas.ign.fr/oor#abri">http://purl.org/choucas.ign.fr/oor#abri</a> of the corresponding class «Shelter » in the ontology of landmark. The alignments models can be considered as a ground truth data.</p> <p>The alignments results are obtained using an ontology application named <a href="http://choucas.ign.fr/doc/ontologies/index-fr.html">OOR</a>. These specific results are obtained using the version of OOR V1.0.1 which is an improved version and contains new concepts compared to the first release 1.0.0. The new version of OOR (i.e. 1.0.1) will be released by the end of May 31 2022. The new link will be added here.</p> <p>This archive is released for transparency and reproducibility purposes.</p>
Comparison of pandemic excess mortality in 2020-2021 across different empirical calculations
<p>Different modeling approaches can be used to calculate excess deaths for the COVID-19 pandemic period. We compared 6 calculations of excess deaths (4 previously published and two new ones that we performed with and without age-adjustment) for 2020-2021. With each approach, we calculated excess deaths metrics and the ratio R of excess deaths over recorded COVID-19 deaths. The main analysis focused on 33 high-income countries with weekly deaths in the Human Mortality Database (HMD at mortality.org) and reliable death registration. Secondary analyses compared calculations for other countries, whenever available. Across the 33 high-income countries, excess deaths were 2.0-2.8 million without age-adjustment, and 1.6-2.1 million with age-adjustment with large differences across countries. In our analyses after age-adjustment, 8 of 33 countries had no overall excess deaths; there was a death deficit in children; and 0.478 million (29.7%) of the excess deaths were in people <65 years old. In countries like France, Germany, Italy, and Spain excess death estimates differed 2 to 4-fold between highest and lowest figures. The R values’ range exceeded 0.3 in all 33 countries. In 16 of 33 countries, the range of R exceeded 1. In 25 of 33 countries some calculations suggest R>1 (excess deaths exceeding COVID-19 deaths) while others suggest R<1 (excess deaths smaller than COVID-19 deaths). Inferred data from 4 evaluations for 42 countries and from 3 evaluations for another 98 countries are very tenuous Estimates of excess deaths are analysis-dependent and age-adjustment is important to consider. Excess deaths may be lower than previously calculated. </p>
Supporting Data for: McKenna et al. (2018), Arctic sea-ice loss in different regions leads to contrasting Northern Hemisphere impacts
<p>This is a dataset of output from version 4 of the Reading Intermediate Global Circulation Model (IGCM4) that was used in the article: </p> <p>McKenna, C. M., Bracegirdle, T. J., Shuckburgh, E. F., Haynes, P. H., & Joshi, M. M. (2018). Arctic sea ice loss in different regions leads to contrasting Northern Hemisphere impacts. <em>Geophysical Research Letters</em>, 45, 945-954. <a href="https://doi.org/10.1002/2017GL076433">https://doi.org/10.1002/2017GL076433</a></p> <p> </p> <p>Files required to setup the IGCM4 simulations are given in the directory 'IGCM4_setup'.</p> <p>All other directories contain netcdf files of timeseries of various monthly mean fields for each IGCM4 simulation (see paper for details on these simulations). The available variables are:</p> <ul> <li>ua: zonal winds</li> <li>zg: geopotential height</li> <li>ts: surface temperature</li> <li>hfls, hfss, rlds, rlus: surface heatfluxes</li> <li>Flat, Fz, divF: Eliassen-Palm flux vectors and their divergence (only for months November-February)</li> </ul> <p>The ua and zg variables are given for different pressure levels indicated in the filenames (e.g., ua500 is ua at 500 hPa). ua is additionally given in terms of the zonal mean with latitude and pressure. zg is additionally given in terms of longitude and pressure, averaged over latitudes between 60N-80N. All files follow CF conventions in terms of metadata, variable names, etc. </p> <p>Note that the CTL, ATL, PAC, and ATLandPAC simulations were all run continuously in time (i.e., every year starts from the end of the previous year). The 0.5ATL and 0.5PAC simulations, however, were run for 300 years in three separate 100-year chunks (i.e., the initial conditions used to start each 100-year chunk were different). The three 100-year chunks have been appended together in the netcdf files. </p>
Battery-less Environment Sensor Using Thermoelectric Energy Harvesting From Soil-Ambient Air Temperature Differences
<p>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. </p> <ul> <li><strong>cde_mseasurements_oct2020-nov2020.csv</strong> <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><strong>cde_teg_measurements.csv</strong> <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><strong>cde_energy_simulated.csv</strong> <ul> <li>Energy production capacity estimated using the temperature data collected from Campus Drie Eiken at the University of Antwerp.</li> </ul> </li> <li><strong>aui_measurements_nov-2021.csv</strong> <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><strong>aui_teg_measurements.csv</strong> <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><strong>aui_energy_simulated.csv</strong> <ul> <li>Energy production capacity estimated using the temperature data collected from the Forhot research site in Iceland.</li> </ul> </li> <li><strong>cde_capacitor_voltage.csv</strong> <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>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.