Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,429
datasets available to search
ShareScore release 0.7.1
Dataset results
1,429 results for “Inventories”
A new inventory of High Mountain Asia surging glaciers derived from multiple elevation datasets since the 1970s
<p>Glacier surging is an unusual undulation instability of ice flow and complete surging glacier inventories are important for regional mass balance studies and assessing glacier-related hazards. Glacier surge events in High Mountain Asia (HMA) are widely reported. Through the estimated elevation changes from multiple DEMs sources that acquired from 1970s to 2020, and morphologic changes from 1986 to 2021, here we present a new surging glacier inventory across HMA. The inventory has incorporated 890 surging and 336 surge-like glaciers, each glacier is assigned with indicators of surging feature and surge possibility. Compared to previous surging glacier inventory in HMA, our inventory is theoretically more complete because of the much longer observation period. This data repository contains the surging glacier inventory and glacier elevation change maps. The inventory is stored in the format of GeoPackage (.gpkg) and ESRI Shapefile format (.shp), which is represented by glacier polygon (from GAMDAM2) or surface point with geometric attributes. The multi-temporal elevation change maps of identified surging glaciers were divided into 1×1° tiles, storing in the format of GeoTiff(*.tif). Detailed description of the dataset including the file contents and attributes information can be found in the metadata file (README.txt).</p>
Methylated Amine Gene Inventory of Catabolism database (MAGICdb)
<p>MAGICdb is the Methylated Amine Gene Inventory of Catabolism database, an analysis of 6,341 gut-derived microbial genomes:</p> <p><strong>DataFileS2_FinalGeneCalling.xlsx</strong>- inventory of genes and genomes in MAGICdb</p> <p><strong>6341_genomes.tar.gz</strong>- methylated amine catabolism encoding genomes in MAGICdb (fasta format)</p> <p><strong>DataFileS3_ALL_NUCL_MethylamineGenes.fna</strong>- methylated amine genes in MAGICdb (fasta format)</p> <p><strong>predict_cvd_from_MAGICdb_mapping.html</strong>- input data, code, and models for predicting cardiovascular disease from human metagenomic datasets using MAGICdb</p> <p><strong>metaP and metaT text files</strong>- contain output tables from mapping Abu-Ali, et al. metatranscriptome and Lloyd-Price, et al. metaproteome data to MAGICdb.</p> <p><strong>Node_table_042722_ANNOTATED.csv</strong>- EFI network node file with sequences for MAGIC database (Fig. 6). </p>
Semi-automatic and manual shallow landslide inventories of two extreme rainfall events.
<p>This dataset contains the polygons of automatic ( PL) and manually (ML) based shallow landslides related to two extreme rainfall events. In KML format, the dataset can be visualized on GIS software or Google Earth.</p><p>With more details, it is possible to find:</p><ul><li>AOI_2016: The study area of the extreme rainfall of November 2016, Tanerello and Arroscia Valleys NW Italy.</li><li>The 2016_PL: The inventory of potential shallow landslides semi-automatically mapped on the base of Sentinel-2 images related to extreme rainfall events that hit NW Italy in November 2016</li><li>The 2016_ML: The inventory of shallow landslides manually mapped on high-resolution images of Google Earth, related to extreme rainfall events that hit NW Italy in November 2016</li><li>AOI_2019_large: The study area of the extreme rainfall of October 2019 NW Italy.</li><li>AOI_2019: The testing area of the extreme rainfall of October 2019, Gavi Area NW Italy.</li><li>The 2019_PL_all: The inventory of potential shallow landslides semi-automatically mapped on the base of Sentinel-2 images related to extreme rainfall events that hit NW Italy in October 2019 (whole Study area)</li><li>The 2019_PL: The inventory of potential shallow landslides semi-automatically mapped on the base of Sentinel-2 images related to extreme rainfall events that hit NW Italy in October 2019 (Gavi test area)</li><li>The 2019_ML: The inventory of shallow landslides manually mapped on high-resolution images of Google Earth, related to extreme rainfall events that hit NW Italy in October 2019</li></ul><p>GEE_Script: A list of codes used in Google Earth Engine to produce NDVI time series or averaged NDVI on some sample studied areas are reported in the attached PDF. The code may be pasted and copied to the Google Earth Engine console. </p><p>The codes (if an account on Google Earth Engine is active) may be reached directly from the following URLs: </p><p><strong>Script 1. </strong>NDVI time series of some sampled areas to select the best pair of images for the PL creation (Tanarello and Arroscia Valley and GAVI AOIs; Fig. 16 of the paper). Link to GEE: <a href="https://code.earthengine.google.com/998af951fcb74519589bf8e722bb30b0?noload=true">https://code.earthengine.google.com/998af951fcb74519589bf8e722bb30b0?noload=true</a></p><p><strong>Script 2.</strong> sampled NDVI time series from different intersection cases for the Tanarello and Arroscia Valley study area (2016 Event). Link to GEE: <a href="https://code.earthengine.google.com/b622cb64f90771ced78ef73bad9cc50f?noload=true">https://code.earthengine.google.com/b622cb64f90771ced78ef73bad9cc50f?noload=true</a></p><p><strong>Script 3. </strong>Sampled NDVI time series from different land-use cases for the Gavi study area (2019 Event). Link to GEE: <a href="https://code.earthengine.google.com/f686c60b78a3dee0b2a2c94a259ccff2?noload=true">https://code.earthengine.google.com/f686c60b78a3dee0b2a2c94a259ccff2?noload=true</a></p><p><strong>Script 4.</strong> Multi-temporal-averaged NDVIvar Link to GEE Script: <a href="https://code.earthengine.google.com/bfc2e570bb675372c4c482eef682be4a?noload=true">https://code.earthengine.google.com/bfc2e570bb675372c4c482eef682be4a?noload=true</a> for the whole Gavi study area (2019 flood) and <a href="https://code.earthengine.google.com/89e1c0a1361860cd407b7e6ab8bb95de?noload=true">https://code.earthengine.google.com/a3390b262cef1b5f42837c88d8791b5b?noload=true</a> for the entire Arroscia-Tanarello study area</p><p>The full description of the methodology can be found in the paper of Notti et al., 2023</p><p>Notti, D., Cignetti, M., Godone, D., and Giordan, D.: Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth Engine, Nat. Hazards Earth Syst. Sci., 23, 2625–2648, <a href="https://doi.org/10.5194/nhess-23-2625-2023">https://doi.org/10.5194/nhess-23-2625-2023</a>, 2023</p>
Prospective Life Cycle Inventory Datasets for conventional and hybrid electric aircraft technologies
<p><strong><em>Supplementary Material - Filled LCI data collection schemes </em></strong>from the publication <em><strong>"Prospective Life Cycle Inventory Datasets for conventional and hybrid electric aircraft technologies"</strong></em>. This repository includes LCI data for three time horizons; short-term, medium-term, and long-term.</p> <p>In the <strong>short-term time horizon</strong>, LCI data for the following technologies distinguished according to two different configurations (conventional and GT-bat) are covered in this repository:</p> <ul> <li>Airframe conventional (GENESIS_LCI_airframe_short-term_conventional_v01.xlsx)</li> <li>Airframe GT-bat (GENESIS_LCI_airframe_short-term_GT-bat_v01.xlsx)</li> <li>Airport (GENESIS_LCI_airport_short-term_v01.xlsx)</li> <li>Battery EOL (GENESIS_LCI_battery_EoL_Li-ion_short-term_GT-bat_v01.xlsx)</li> <li>Battery Li-ion (GENESIS_LCI_battery_Li-ion_short-term_GT-bat_v01.xlsx)</li> <li>Battery charging station (GENESIS_LCI_battery-charging-station_short-term_v01.xlsx)</li> <li>Power electronics and drives (GENESIS_LCI_power_elec_drives_short-term_v01.xlsx)</li> <li>Powerplant conventional (GENESIS_LCI_powerplant_short-term_conventional_v01.xlsx)</li> <li>Powerplant GT-bat (GENESIS_LCI_powerplant_short-term_GT-bat_v01.xlsx)</li> <li>SAF (GENESIS_LCI_SAF_short-term_v01.xlsx)</li> </ul> <p>In the <strong>medium-term time horizon</strong>, LCI data for the following technologies distinguished according to three different configurations (conventional, GT-bat, and PEMFC-bat) are covered in this repository:</p> <ul> <li>Airframe conventional (GENESIS_LCI_airframe_medium-term_conventional_v01.xlsx)</li> <li>Airframe GT-bat (GENESIS_LCI_airframe_medium-term_conventional_v01.xlsx)</li> <li>Airframe PEMFC-bat (GENESIS_LCI_airframe_medium-term_PEMFC-bat_v01.xlsx)</li> <li>Airport (GENESIS_LCI_airport_medium-term_v01.xlsx)</li> <li>Battery EOL Li-S GT-bat (GENESIS_LCI_battery_EoL_Li-S_medium-term_GT-bat_v01.xlsx)</li> <li>Battery EOL Li-S PEMFC-bat (GENESIS_LCI_battery_EoL_Li-S_medium-term_PEMFC-bat_v01.xlsx)</li> <li>Battery Li-S GT-bat (GENESIS_LCI_battery_Li-S_medium-term_GT-bat_v01.xlsx)</li> <li>Battery Li-S PEMFC-bat (GENESIS_LCI_battery_Li-S_medium-term_PEMFC-bat_v01.xlsx)</li> <li>Battery charging station (GENESIS_LCI_battery-charging-station_medium-term_v01.xlsx)</li> <li>Fuel cell PEM (GENESIS_LCI_fuel cell_PEM_medium-term_PEMFC-bat_v01.xlsx)</li> <li>H<sub>2</sub> onboard storage (GENESIS_LCI_H2_onboard_storage_medium-term_PEMFC_v01.xlsx)</li> <li>Power electronics and drives GT-bat (GENESIS_LCI_power_elec_drives_medium-term_GT-bat_v01.xlsx)</li> <li>Power electronics and drives PEMFC-bat (GENESIS_LCI_power_elec_drives_medium-term_PEMFC-bat_v01.xlsx)</li> <li>Powerplant conventional (GENESIS_LCI_powerplant_medium-term_conventional_v01.xlsx)</li> <li>Powerplant GT-bat (GENESIS_LCI_powerplant_medium-term_GT-bat_v01.xlsx)</li> <li>Powerplant PEMFC-bat (GENESIS_LCI_powerplant_medium-term_PEMFC-bat_v01.xlsx)</li> </ul> <p>In the <strong>long-term time horizon</strong>, LCI data for the following technologies distinguished according to three different configurations (conventional, PEMFC-bat, and SOFC-bat) are covered in this repository:</p> <ul> <li>Airframe conventional (GENESIS_LCI_airframe_long-term_conventional_v01.xlsx)</li> <li>Airframe PEMFC-bat (GENESIS_LCI_airframe_long-term_PEMFC-bat_v01.xlsx)</li> <li>Airframe SOFC-bat (GENESIS_LCI_airframe_long-term_SOFC-bat_v01.xlsx)</li> <li>Airport (GENESIS_LCI_airport_long-term_v01.xlsx)</li> <li>Battery EOL Li-Air PEMFC-bat (GENESIS_LCI_battery_EoL_Li-air_long-term_PEMFC-bat_v01.xlsx)</li> <li>Battery EOL Li-Air SOFC-bat (GENESIS_LCI_battery_EoL_Li-air_long-term_SOFC-bat_v01.xlsx)</li> <li>Battery Li-Air PEMFC-bat (GENESIS_LCI_battery_Li-air_long-term_PEMFC-bat_v01.xlsx)</li> <li>Battery Li-Air SOFC-bat (GENESIS_LCI_battery_Li-air_long-term_SOFC-bat_v01.xlsx)</li> <li>Battery charging station (GENESIS_LCI_battery-charging-station_long-term_v01.xlsx)</li> <li>Fuel cell PEM (GENESIS_LCI_fuel cell_PEM_long-term_PEMFC-bat_v01.xlsx)</li> <li>Fuel cell SO (GENESIS_LCI_fuel cell_SO_long-term_SOFC-bat_v01.xlsx)</li> <li>H<sub>2</sub> onboard storage PEMFC-bat (GENESIS_LCI_H2_onboard_storage_long-term_PEMFC-bat_v01.xlsx)</li> <li>H<sub>2</sub> onboard storage SOFC-bat (GENESIS_LCI_H2_onboard_storage_long-term_SOFC-bat_v01.xlsx)</li> <li>Power electronics and drives PEMFC-bat (GENESIS_LCI_power_elec_drives_long-term_PEMFC-bat_v01.xlsx)</li> <li>Power electronics and drives SOFC-bat (GENESIS_LCI_power_elec_drives_long-term_SOFC-bat_v01.xlsx)</li> <li>Powerplant conventional (GENESIS_LCI_powerplant_long-term_conventional_v01.xlsx)</li> <li>Powerplant PEMFC-bat (GENESIS_LCI_powerplant_long-term_PEMFC-bat_v01.xlsx)</li> <li>Powerplant SOFC-bat (GENESIS_LCI_powerplant_long-term_SOFC-bat_v01.xlsx)</li> </ul> <p>Additionally, the following file is used for <strong>all time horizons</strong>:</p> <ul> <li>H<sub>2</sub> production and supply (GENESIS_LCI_H2_production_&_supply_v01.xlsx)</li> </ul>
Psychometric Properties of the Maslach Burnout Inventory in Healthcare Professionals, Ancash Region, Peru
<p><strong>Background:</strong> Burnout syndrome (BS) among healthcare professionals in Peru demands immediate attention. Consequently, there is a need for a validated and standardized instrument to measure and address it effectively. This study aimed to determine the psychometric properties of the Maslach Burnout Inventory (MBI) among healthcare professionals in the Ancash region of Peru.</p> <p><strong>Methods:</strong> Using an instrumental design, this study included 303 subjects of both sexes (77.56% women), ranging in age from 22 to 68 years (M = 44.46, SD = 12.25), selected via purposive non-probability sampling. Appropriate content validity, internal structure validity, and item internal consistency were achieved through confirmatory factor analysis, and discriminant validity for the three dimensions was obtained. Evidence of convergent validity was found for the Emotional Exhaustion (EE) and Personal Accomplishment (PA) dimensions, with reliability values (ω > .75).</p> <p><strong>Results:</strong> The EE and PA dimensions exhibited acceptable levels of reliability (ω and α > .80). However, the Depersonalization (DP) dimension demonstrated significantly lower reliability (α < .60 and ω < .50).</p> <p><strong>Conclusions:</strong> A correlated three-factor model was confirmed, with most items presenting satisfactory factor loadings and inter-item correlations. Nonetheless, convergent validity was not confirmed for the DP dimension.</p> <p><strong>Keywords: </strong>Burnout; Psychometrics; Validity; Reliability; Healthcare Professionals.</p>
Murchison Falls National Park Uganda Woody Plant and Palm Inventory Plots 2022
Murchison Falls National Park (MFNP) is a protected area in northern Uganda along the border with the Democratic Republic of the Congo and straddling the Victoria Nile. This project was designed to assess the accuracy of woody cover maps developed in (Nagelkirk & Dahlin, 2020). We determined an area of interest and then identified 40 plots that we expected would range from zero to nearly 100% woody cover. Due to restrictions related to the COVID-19 pandemic, we could only spend six days in the field, and so our sampling time was limited. We were able to collect 36 30x30 m square plots (four plots were not measured due to safety or accessibility issues). In each, we collected data describing woody plant species, when possible, diameter at breast height (DBH) or basal diameter depending on the size of the plant, and two crown diameter measurements: one at the widest width and another approximately perpendicular to the first. Together these measurements allow us to estimate woody plant canopy cover and basal area, along with species diversity both by count and by basal area. With additional information, aboveground biomass, functional diversity, and phylogenetic diversity could also be estimated in the future. Although this project was limited in scope, since eastern African savannas are underrepresented in global databases of woody cover and aboveground biomass, this data set will contribute to our overall understanding of vegetation patterns and processes.
Coarse woody debris volume and mass from line transect inventory from reference stands and inventory plots of the Pacific Northwest, 1997 to 2005
These data can be used to provide an inventory of the volume of coarse woody debris stored in forests.
Landslide inventory (1953-1996), Andrews Experimental Forest and Blue River Basin.
Landslide inventory (1953-1996), Andrews Experimental Forest and Blue River Basin. This layer is a combination of data from four landslide inventory efforts that have been conducted in the area beginning in the late 1960's. Data represent landslide occurrences between 1953 and 1996. Data are in the UTM coordinate system; zone 10, NAD27. The data table that describes the characteristics of the landslides inventoried in the following: Individual layers tied to inventories by Ted Dyrness, Fred Swanson, Dan Marion, and Matt Wallenstein were appended and linked to the shape file. This file is associated with the slideinv layer, which is a combination of data from four landslide inventory efforts that have been conducted in the area beginning in the late 1960's. Points have been screen digitized from field maps using streams, roads, contour lines, and harvest units as reference layers.
Willamette National Forest soil resource inventory (SRI 1992) clipped to the Andrews Experimental Forest
This layer contains the delineations of the soil landtype mapping units defined in the 1992 update to the SRI (Soil Resource Inventory) The mapping units are derived and defined on the basis of soil, landform, geology and vegetation characteristics. The average delineation size for each mapping unit is 50 to 600 acres. This data was originally mapped at the 1:24000 scale. Due to the reconnaissance nature of this survey, it lacks detail for use in high-intensity, small-area projects. These projects require additional on-site study by various technical specialists, including soil scientists. The dominant landtype of the mapping unit is described in the mapping unit description and identified by the same number as used for the mapping unit. Within the mapping unit, other landtypes occur. Those most commonly associated with the dominant landtype of the mapping unit are included in the descriptions as inclusions. These inclusions of other landtypes account for no more than 30 percent of the mapping unit. (for further descriptions of mapping unit symbology see SOIL RESOURCE INVENTORY" Willamette National Forest, Basic Soil Information and Interpretive Tables
Dynamics of large wood in streams: Tagged log inventory, Mack Creek, Andrews Experimental Forest, 1985 to 2008
Although many studies have identified the characteristics of wood stored in streams, few have attempted to measure the long-term dynamics of large wood. From 1982-1985, we developed a long-term study of input, storage, decomposition, and redistribution of large wood in Mack Creek. Each year from 1985 to the present, we have surveyed a 1.1 km section of this stream. This annual survey has allowed us to quantify the standing stocks and characteristics of large wood within the stream and floodplain of an old-growth forest and an older (ca. 1963) clear-cut. In addition, these data allow us to measure rates of input, fragmentation and movement.
Mass of forest floor litter from cores in reference stands and inventory plots in the Pacific Northwest, 1992 to 2003
These data provide an inventory of the mass of forest floor organic matter stored within various forest types. This data is used to determine total organic matter, carbon, and nutrient stores in forests.
Fine woody detritus volume and mass from line transect inventory in WS06, WS07, WS08, and WS09 at Andrews Experimental Forest, 2002 to 2003
Three small watersheds (WS06, WS07, WS08, and WS09) at the Andrews Experimental Forest were inventoried for fine woody detritus (FWD) using the line transect method as described in Guidelines for measurements of woody detritus in forest ecosystems (Harmon and Sexton 1996). In each watershed plot, four transects of 4 meters in length were inventoried. Slopes of the transect were determined as the plots were not laid out slope corrected. The number of pieces of FWD were tallied along each transect for two size classes, 0.63-2.54 cm and 2.54-7.62 cm. Volume calculations were determined by use of the formula in Harmon and Sexton 1996. Mean bulk density was determined from samples collected and measured. Field measurements used to determine density included diameter and length. In the lab, the samples were weighed to determine wet weight and dried.
Inventory and description of thermokarst features observed along the Umiat Corridor in July 2009.
Using a combination of aerial imagery and ~1m resolution airborne lidar (collected July, 2009), we use manual visual inspection of the two datasets to identify point locations of over 7000 thermal erosion features (thermokarst) of varying maturity. For each feature we report its x,y position, the facing direction of the feature, the local topographic setting, the geologic unit it occurs on, the relative age of the feature and the specific type of thermal erosion feature.
White spruce tree diameter vs age in mid-successional and late-successional long-term Bonanza Creek LTER inventory plots
This data lists age and diameter for white spruce trees of varying diameters growing in mid and late successional long-term BNZ LTER inventory plots in floodplain and upland landscapes.
Aspen tree diameter vs age in mid-successional long-term Bonanza Creek LTER inventory plots
This data lists age and diameter for aspen trees of varying diameters growing in mid successional long-term BNZ LTER inventory plots in upland landscapes.
Alaskan paper birch tree diameter vs age in mid-successional and late-successional long-term Bonanza Creek LTER inventory plots
This data lists age and diameter for Alaskan paper birch trees of varying diameters growing in mid- and late-successional long-term BNZ LTER inventory plots in upland and floodplain landscapes.
Balsam poplar tree diameter vs age in mid-successional and late-successional long-term Bonanza Creek LTER inventory plots
This data lists age and diameter for balsam poplar trees of varying diameters growing in mid and late successional long-term BNZ LTER inventory plots in floodplain and upland landscapes.
Aspen Fungal Canker Inventory at Previoulsy Established Monitoring and Inventory Plots Across Interior Alaska, Sampled 2016-2018
This dataset contains characterization of 88 study sites and inventory data for individual trees at these study sites recorded between 2016-2018. Canker was inventoried within previously established networks of long-term monitoring and inventory plots scattered across interior Alaska, and within additional sites selected specifically for this study. These included sites within the Bonanza Creek (BNZ) Experimental Forest (BCEF), and sites within the BNZ Regional Site Network (RSN), both maintained by the BNZ Long-Term Ecological Research program (BNZ LTER). We also surveyed plots within the Cooperative Alaska Forest Inventory (CAFI) network, a network of 203 sites representing a wide range of mature boreal forest stands scattered across interior and south-central Alaska including the Kenai Peninsula.
Inventory of soil prokaryotic microbiome (via 16S based on rRNA gene amplicons) in freshwater and brackish water marshes following saltwater intrusion along Shark River Slough boundary, Everglades National Park (FCE LTER), Florida, USA, September 2018
Global sea-level rise is transforming coastal ecosystems, especially freshwater wetlands, in part due to increased episodic or chronic saltwater exposure, leading to shifts in microbial communities and related ecological services. Soil prokaryotes play a fundamental role in regulating important biogeochemical processes in coastal wetland ecosystem. Yet, it is still difficult to predict how soil prokaryotic communities respond to the saltwater exposure because of poorly understood prokaryotic sensitivity within complex wetland soil microbial communities, as well as the high heterogeneity of wetland soils and saltwater exposure. To address this, a four-year experimental simulation of saltwater intrusion in a pristine freshwater site and a previously saltwater-impacted site was conducted. The saltwater addition started in October 2014 on a monthly basis and continued through October 2018. The dataset contains amplicon sequencing date of 16S rRNA gene obtained from saltwater-exposed soils and unmanipulated native soils in both sites (collected in September 2018). The 2018 data are published in Zhao et al. 2023. A detailed list of sequence data and their accession numbers in GenBank is provided, and data collection is complete. This data package is an inventory of sequence read archive (SRA) entries available through GenBank BioProject PRJNA804545 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA804545). This data package is associated with the following publication: Zhao, J., Chakrabarti, S., Chambers, R., Weisenhorn, P., Travieso, R., Stumpf, S., Standen, E., Briceno, H., Troxler, T., Gaiser, E., Kominoski, J., Dhillon, B., & Martens-Habbena, W. (2023). Year-around survey and manipulation experiments reveal differential sensitivities of soil prokaryotic and fungal communities to saltwater intrusion in Florida Everglades wetlands. Science of The Total Environment, 858, 159865. https://doi.org/10.1016/j.scitotenv.2022.159865 Instead of citing this package, which is an
Inventory of soil prokaryotic and fungal microbiome (via 16S rRNA gene amplicons and ITS sequencing) from Shark River Slough and Taylor Slough, Everglades National Park (FCE LTER), Florida, USA, February 2019 - October 2020
Global sea-level rise is transforming coastal ecosystems, especially freshwater wetlands, in part due to increased saltwater exposure, leading to change in soil microbial communities and many important biogeochemical processes. Given the high spatial and temporal heterogeneity in coastal wetlands, especially in tropical or subtropical climates characterized by seasonal temperature, precipitation, and tidal fluctuations, it remains unclear which environmental factors influence the compositions of soil microbial communities in wetlands affected by varying degrees of sea-water intrusion. To address this, a two-year survey was conducted on microbial community structure in submerged surface soils from 14 wetland sites across the Florida Everglades, representing three major ecosystem types, i.e. freshwater marshes, mangrove forests, and seagrass meadows. Bulk surface soil samples of each site were collected from February 2019 to October 2020 to cover dry and wet seasons. In addition to bulk soil samples, soil cores were collected from each site in August 2020 to assess vertical gradients of microbial communities. The dataset contains amplicon sequencing data of 16S rRNA gene (both bulk soil and soil cores) and ITS gene (only the bulk soil). The 2019 to 2020 data are published in Zhao et al. 2023. A detailed list of sequence data and their accession numbers in GenBank is provided, and data collection is complete. This data package is an inventory of sequence read archive (SRA) entries available through GenBank BioProject PRJNA804243 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA804243), PRJNA804246 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA804246), and PRJNA804228 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA804228). This data package is associated with the following publication: Zhao, J., Chakrabarti, S., Chambers, R., Weisenhorn, P., Travieso, R., Stumpf, S., Standen, E., Briceno, H., Troxler, T., Gaiser, E., Kominoski, J., Dhillon, B., & Martens-H
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.