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.
7,081
datasets available to search
ShareScore release 0.7.1
Dataset results
7,081 results for “habitat”
Habitat range and phenotypic variation in twelve salt marsh plants on Sapelo Island, Georgia, USA.
We measured traits of twelve salt marsh plant species at sites around Sapelo Island GA in August of 1999. Plant species represent six families - Asteraceae: Aster tenuifolius L., Borrichia frutescens L., Iva frutescens L.; Bataceae: Batis maritima L.; Chenopodiaceae: Salicornia bigelovii Torrey, Salicornia virginica L.; Juncaceae: Juncus roemerianus Scheele; Plumbaginaceae: Limonium carolinianum (Walter) Britton; Poaceae: Distichlis spicata (L.) Greene, Spartina alterniflora Loisel., Spartina patens (Aiton) Muhl., Sporobolus virginicus (L.) Kunth; all nomenclature follows Radford et al. (1968). Soil cores were taken adjacent to each plant to measure soil water content, porewater salinity, and organic content.
Distribution and habitat use of juvenile steelhead and other fishes of the lower Feather River
Understanding how fish presence is related to habitat features is useful in restoration planning and monitoring as better information about how fish use habitat may lead to more impactful restoration projects. The California Department of Water Resources (DWR), conducted a two-year study of microhabitat and mesohabitat in Feather River. The goal of this study was to identify relationships between habitat conditions (depth, substrate, velocity, and cover) and where juvenile Chinook salmon and steelhead occur. Snorkel surveys were conducted monthly March through August in 2001 and 2002 across 29 different sites, which were selected at random (13 in Low Flow Channel, and 16 in High Flow Channel). Each sampling section covered an area 25 meters long by 4 meters wide, running parallel to riverbank. These data were published to support the Healthy Rivers and Landscapes Science Program.
Headwater Habitat Streams in Central Massachusetts 2002-2005
Headwater streams, particularly those that flow only during part of the year, are understudied and underprotected in Massachusetts. Research being conducted elsewhere suggests that these "Headwater Habitat Streams" are important both for aquatic biodiversity and for ecological function of lower stream reaches. We are carrying out baseline research, involving research scientists and volunteers, on hydrology and habitat characteristics in headwater streams in northern Worcester County, MA. We hypothesized that headwater streams exhibit a longitudinal gradient of hydrology, from (1) ephemeral channels that flow only in response to storms, through (2) intermittent sections that flow seasonally until the groundwater table falls below the channel and are dry the rest of the year, to (3) interstitial reaches that flow seasonally and retain pools connected by subsurface flow during the summer, to (4) the perennial stream. Results to date show a high degree of longitudinal heterogeneity in the study streams, with interspersion of perennially flowing reaches among low-gradient sections of vegetated wetland, high-gradient boulder piles, and braided channels. Perennial flow is found high up in some watersheds. We expect our methods and results will have implications throughout the Commonwealth for local conservation commissions and other municipal officials responsible for land-use planning and regulation, state agencies responsible for land management and the protection of wildlife, regulators reviewing projects affecting streams, watershed managers, teachers and their students, private land trusts, conservation advocates, and citizen-naturalists.
Pika habitat occupancy survey data for Niwot Ridge and Green Lakes Valley, 2016 - ongoing
Long-term monitoring of habitat occupancy can reveal patterns of habitat use, population dynamics, and factors controlling species distribution. The American pika (Ochotona princeps), a small mammal found in rocky habitats throughout western North America, has been targeted for occupancy studies due to its relatively conspicuous behavior and its unusual adaptations for surviving long, cold winters without hibernation. These adaptations include an unusually high resting metabolic rate and maintenance of body temperatures near the lethal maximum for this species, which would appear to compromise the pika's ability to survive warmer summers. Recent monitoring as well as projections based on future climate scenarios have suggested this species is experiencing a period of range retraction due to warming summers and/or loss of insulating winter snow cover. Niwot Ridge is situated ideally to test competing hypotheses about the trajectory and drivers of pika range shift. The pika is still common throughout the Colorado Rockies, but published models differ markedly regarding projections of the pika’s future distribution in this region. Niwot Ridge has experienced warmer summers as well as shorter periods of insulating snow cover in recent years, and there is evidence that pikas are now less common than they once were in at least one area on the ridge. This study is designed to provide robust data on pika population trends through long-term monitoring of occupancy in a spatially balanced random sample of pika habitat patches centered on Niwot Ridge. Survey plots (n = 72) were selected according to a Generalized Random-Tessellation Stratified (GRTS) algorithm, stratified dichotomously by elevation, average annual snow accumulation (SWE), and probabilities of pika occurrence based on previous data. Each plot extends 12 m in radius from a GRTS point. To ensure that each plot contains at least 10% cover of talus, plot coordinates were adjusted (usually less than 50 m) or replaced
Dynamic landscapes of fear and safety alter prey refuge use in freshwater habitats
The non-consumptive effects of predators on prey behavior have been studied in many different systems. However, predator-prey ecology has placed a bulk of emphasis on how fear alters prey behavior, and new studies have begun to shift focus to the importance that safety in the form of refuges has in structuring prey behavioral responses. This project focuses on changes in the safety landscape as well as changes in the fear landscape and how these changes impact crayfish behavior. Using an established bass-crayfish predator prey system, we altered shelter quality and location in relation to the presence of bass odor signals. We measured shelter use by the crayfish in response to this changing landscape.
Mid-winter habitat suitability indices for centrarchids in contiguous lentic areas of the Upper Mississippi River System: 1994-2018
This dataset includes raw measurements and calculated bluegill winter habitat suitability indices for depth (HSID), dissolved oxygen (HSIDO), temperature (HSIT), and flow (HSIF), as well as an overall bluegill winter habitat suitability index (HSIO), for 2915 mid-winter, lentic sampling locations across 208 contiguous lentic areas throughout the Upper Mississippi River System (Upper Mississippi and Illinois Rivers) from 1994-2018. This dataset also includes several spatial and temporal climatic and hydrogeomorphic parameters that were used to assess potential drivers of winter habitat suitability.
Relative predation rates on juvenile Chinook Salmon in the lower Stanislaus River, California, 2012-2024 by habitat suitability informed by juvenile Chinook Salmon and black bass observations in the lower Stanislaus and Merced rivers, California, 2012-2017
Overview The purpose of this work was to estimate relative predation on juvenile Chinook Salmon rearing in tributaries of the San Joaquin River, California in relation to meso- and microhabitat factors. Predation rates were estimated using predation bioassays. Ranges of depth and velocity targeted by the bioassays were informed by habitat suitability indices developed prior to field efforts. Juvenile Chinook and Bass Habitat Suitability Indices The purpose of this dataset is to develop habitat suitability indices for juvenile Chinook Salmon (<120mm) on the lower Stanislaus River and nonnative black bass ( Micropterus spp.). on the lower Merced River, both tributaries of the San Joaquin. This data was used to identify target ranges of depth and velocity during predation fieldwork. Occupancy data was collected via snorkel surveys on the lower Stanislaus River in 2018 and 2019 and on the Merced River in 2012 and 2014-2017. Predation Tethering Bioassay Study The purpose of this field study was to estimate relative rates of predation of juvenile Chinook Salmon. Predation rates were estimated using assays of tethered hatchery Chinook Salmon deployed across a range of mesohabitats on the lower Stanislaus River. Habitat suitability was expected to vary across mesohabitats and across depths and velocities sampled within habitats. Cameras were deployed with tethers to identify predators for a subset of predation events happening within the first 1-2 hours of deployment. Assays were deployed monthly March-May in 2022 and 2024. A supplemental set of assays were deployed in May 2023 under wet water year conditions that varied strongly from conditions sampled in 2022 and 2024.
Urban habitat features and patterns of snake removals in the greater Phoenix, Arizona (USA) metropolitan area (March 2021 - March 2022)
In urban and suburban areas, wildlife and people are often in close quarters, leading to human-wildlife interactions (HWI). Understanding how wildlife interact with humans and the built environment is critical as urbanization contributes to habitat change and fragmentation globally. In our study, we partnered with a local business that removes and relocates snakes from homes and businesses in the Phoenix area. The most frequently removed were venomous (family Viperidae, e.g., rattlesnakes) and nonvenomous (family Colubridae, e.g., gophersnakes) snakes. Using these records, we investigated taxa-specific habitat trends at two spatial scales. The neighborhood scale focused on front yard measures of cover and vegetation classes and the landscape scale focused on variables related to vegetation indices and degree of urbanization. Both analyses compared areas where snakes were removed to random locations in the city to represent possible habitat available to snakes. At the neighborhood scale (n=60), we found that removals occurred in yards with abundant cover opportunities. At the landscape scale (n=764), we found species-specific differences with nonvenomous snakes removed from areas of higher urbanization compared to venomous snakes. Understanding these distinct habitat patterns in residential yards can identify areas with potential human-snake conflict.
GPH01 Grazing management effects on pollinator communities and habitat at Konza Prairie, 2024-2025
For all data files, data were collected in Konza Prairie LTER sites: N1A, N1B, C1A, K1B, and 1D during May, July, and August in both 2024 and 2025. The goal was to investigate variation in pollinator foraging and nesting habitat and in foraging and nesting pollinator communities across grazing regimes. Details for each file follow: 1. Plant-pollinator interactions (file = pollinatorNetworks); 2. Bees collected from ground nests (file = nestingBees); 3. Floral resources observed along transects (file = floralResources); 4. Bare ground cover and vegetation height measured along transects (file = otherHabitat); 5. Soil characteristics (file = soils)
CBP01 Variable distance line-transect sampling of bird population numbers in different habitats on Konza Prairie
Records of bird species based on line transect sampling, giving perpendicular distance of sighting from the transect line on 16 separate transects. Bird surveys were conducted 2-4 times per year in January, April, June, and October for a 29-year period from 1981 to 2009. Transects were designed to determine bird communities and population numbers associated with tallgrass prairie habitats with different experimental treatments (fire frequency, grazed by bison vs. ungrazed), riparian habitats on forest edge, and gallery forests dominated by oak woodland.
CSM04 Seasonal summary of numbers of small mammals on the eight LTER seasonal burn traplines in prairie habitats at Konza Prairie
Data set contains seasonal summaries (spring, summer and fall) of the number of individuals of each species of small mammal caught (relative density) on each grassland census line. Each record contains trapline, year of last fire and number of individuals per species. These live trap records are based on daily captures during three 4-day trapping periods, March, July and October, for each of 20 permanent census lines established on 10 fire-grazing treatments (2 lines per treatment). These 10 fire-grazing treatments are one unburned, one annual burn and one 4-year burn site to be grazed by native ungulates and one unburned, one annual burn, four 4-year burn and one 10-year burn site not grazed by ungulates.
CSM02 Seasonal summary of numbers of small mammals on the four LTER gallery forest and limestone ledges traplines in wooded habitats at Konza Prairie
Data set contains seasonal summaries (spring, summer and autumn) of the number of individuals of each species of small mammal captured (relative abundance) on each woodland trapline. Each record contains year, season, trapline and number of individuals captured of each species. These live trap records are based on daily captures during a single 4-day trapping period in spring (early March to early April), summer (early July to late July) and autumn (mid-October to early December) for each of four permanent traplines established in two habitats (two traplines in gallery forest and two on limestone ledges). Bison did not graze any of the treatment units during the period of study.
CSM01 Seasonal summary of numbers of small mammals on 14 LTER traplines in prairie habitats at Konza Prairie
Data set contains seasonal summaries (spring and autumn) of the number of individuals of each species of small mammal captured (relative abundance) on each grassland trapline. Each record contains year, season, trapline and number of individuals captured of each species. These live trap records are based on daily captures during two 4-day trapping periods in spring (late February to early April) and autumn (early October to mid-November) for each of 14 permanent traplines established on seven fire-grazing treatments (two traplines per treatment). These seven fire-grazing treatments include three sites that are grazed by bison (1 unburned, 1 annual burn and 1 4-year burn) and four sites that are not grazed by bison (1 unburned, 1 annual burn and 2 4-year burn).
Biocomplexity at North Temperate Lakes LTER; Coordinated Field Studies: Coarse Woody Habitat Data 2001 - 2009
These data were collected to test for changes in the population dynamics and the food webs of the fish populations of Little Rock and Camp lakes, Vilas County, WI, USA. Little Rock Lake was the site of a whole-lake removal of coarse woody habitat in 2002 and Camp Lake was the site of a whole-lake coarse woody habitat addition in 2004. Sampling began in May of 2001 and ended in August of 2006. Some sampling was repeated from 2007 to 2009. Number of sites: 4. Two lakes with reference and treatment basin in each lake.
Migratory shorebird habitat use, diet, and prey selection on mudflats in the Virginia barrier island and lagoon system, 2023-2024
Migratory shorebirds require access to heterogenous resources during migration. Understanding how shorebirds utilize different foraging substrates and food resources across the coastal landscape is important for informing conservation. We compared shorebird habitat use and invertebrate prey communities between barrier island and mudflat foraging substrates. We counted shorebirds and collected prey samples at random points on sand, peat, and mudflat substrates during spring migration (May 14 - June 2), 2023 - 2024. We opportunistically collected fecal samples on mudflats in our study area and used fecal DNA metabarcoding with 18S (invertebrates) and 23S (biofilm) primers to describe the diets of dunlin (Calidris alpina), red knots (Calidris canutus rufa) and semipalmated sandpipers (Calidris pusilla). We then used network null modeling to determine if our focal species were selectively consuming invertebrates on mudflats. Peat banks were the most heavily used intertidal substrate and mudflats supported similar shorebird abundances and species richness to sand. Dunlin and semipalmated sandpipers were more abundant on peat and mudflats, while red knots were more abundant on sand and peat. Invertebrate density was highest on peat banks and similar between mudflat and sand substrate, though mudflats supported a more diverse prey community. Amphipod crustaceans, blue mussels (Mytilus edulis), and polychaete worms were main prey consumed by all species on mudflats. Dunlin and semipalmated sandpipers fed primarily on crustaceans whereas red knots mainly fed on bivalves. All species consumed biofilm and a high proportion of diatoms were observed in fecal samples collected from semipalmated sandpipers. Red knots and dunlin selectively consumed bivalves on mudflats while semipalmated sandpipers showed no dietary preferences. Managing staging sites to preserve a diversity of intertidal habitats is critical for meeting the variable foraging requirements of migratory shorebirds.
A global map of terrestrial habitat types
<p>We provide a global spatially explicit characterization of 47 (version 001) terrestrial habitat types, as defined in the International Union for Conservation of Nature (IUCN) habitat classification scheme, which is widely used in ecological analyses, including for assessing species’ Area of Habitat. We produced this novel habitat map by creating a global decision tree that intersects the best currently available global data on land cover, climate and land use. The maps broaden our understanding of habitats globally, assist in constructing area of habitat (AOH) refinements and are relevant for broad-scale ecological studies and future IUCN Red List assessments. We hope that these data and outlined framework will spur further development of biodiversity-relevant habitat maps at global scales. An interactive interface helping to navigate the map can be found at on the Naturemap website ( https://explorer.naturemap.earth/map).</p> <p>Provided is the code to recreate the map (to made available soon), the global composite image at native -100m Copernicus resolution for level 1 and level 2 and layers of aggregated fractional cover (unit: [0-1] * 1000) at 1km for level 1 and level 2.</p> <p>Starting with version 004 there changemasks for the years 2016, 2017, 2018 and 2019 are supplied. Changemasks for the composite masks show the changed grid cells and their new values with earlier years being nested in later years, e.g. using the changemask for 2019 includes all changes up to 2019. For the fractional cover estimates at ~1km resolution, new fractional cover changemasks are supplied as subtraction (before - after) between the previous and current year (unit range: [-1 to 1] * 1000).</p> <p>We highlight that only changes in land cover are considered since most of the ancillary layers (e.g. pasture, forest management, climate, etc...) are static and thus not all changes in habitats can be found. We therefore recommend end users to continue using the 2015 dataset unless specific habitat updates to habitat are needed.</p> <p><strong>Citation:</strong></p> <p>Please cite the published paper and state the used version of the habitat map</p> <p>Jung, M., Dahal, P.R., Butchart, S.H.M., Donald, P.F., De Lamo, X., Lesiv, M., Kapos, V., Rondinini, C., Visconti, P., (2020). A global map of terrestrial habitat types. Sci. Data 7, 256. <a href="https://doi.org/10.1038/s41597-020-00599-8">https://doi.org/10.1038/s41597-020-00599-8</a></p>
Invasion Biology WikiProject Scientific Papers: Text Data Mining and LLM-based Information Extraction of Species, Locations, Habitats, and Ecosystems
<p>This dataset contains the abstract and full-text for publication DOIs from the Invasion Biology WikiProject (DOI: <a href="https://www.doi.org/10.5281/zenodo.12518036">10.5281/zenodo.12518036</a>). The data was retrieved using the <a href="https://ask.orkg.org/">ask.orkg.org</a> <a href="https://api.ask.orkg.org/docs#tag/Semantic-Neural-Search/operation/explore_documents_index_explore_get">API</a>. For the <a href="https://github.com/jd-coderepos/invasion-biology-IE/blob/main/scripts/ask-doi-list-fulltext-search.py">script</a> used to obtain the data, refer to the accompanying GitHub repository: <a href="https://github.com/jd-coderepos/invasion-biology-IE/" target="_blank" rel="noopener">https://github.com/jd-coderepos/invasion-biology-IE/</a>.</p> <p>The resulting CSV file includes the following fields: <code>"ASK ID"</code>, <code>"DOI"</code>, <code>"Title"</code>, <code>"Abstract"</code>, and <code>"Full-text"</code>.</p> <p>Of the 49,438 queried DOIs, the ASK database provided:</p> <ul> <li><strong>Total DOIs processed:</strong> 12,636</li> <li><strong>DOIs with neither abstract nor full-text:</strong> 36 (abstract token count was less than 10)</li> <li><strong>DOIs with abstracts but no full-text:</strong> 12,636</li> <li><strong>DOIs with both abstract and full-text:</strong> 2,834</li> </ul> <p>The second part of the dataset contains structured information extracted from the publications using the GPT-4o Large Language Model. This structured data is included in the zipped folder <code>structured-publications.zip</code>.</p> <p>The accompanying GitHub repository provides access to the code and scripts used at various stages of the information extraction (IE) process.</p> <p><strong>Theme of the Study:</strong><br>"Mining for Species, Locations, Habitats, and Ecosystems from Scientific Papers in Invasion Biology: A Large-Scale Exploratory Study with Large Language Models."</p>
Holdridge Life Zones Classification in New Caledonia Habitats
<h1>Description</h1> <p>This dataset aims to represent, in geographic space, the distribution of life zones as first defined by Holdridge in 1947 and updated in 1967. Life zones are delineated through three parameters:</p> <ul> <li>Mean Annual Biotemperature (°C): This axis represents the average annual temperature, considering only temperatures above 0°C, as it influences biological activity. It determines the thermal regime of the environment.</li> <li>Annual Precipitation (mm): This axis measures the total annual precipitation, indicating moisture availability. It is crucial for determining the hydric regime and supporting different types of vegetation and ecosystems.</li> <li>Potential Evapotranspiration Ratio (PET): This axis is the ratio of potential evapotranspiration to annual precipitation. It reflects the balance between water demand and supply, indicating aridity or humidity levels and influencing vegetation types and ecosystem dynamics.</li> </ul> <p>We used a combination of WorldClim datasets (Biotemperature and potential evapotranspiration) and Météo-France Aurelhy datasets (Annual Precipitation) specifically designed for New Caledonia to produce the raster with a 1 km² resolution.</p> <h1>Content</h1> <p>This dataset was produced, analyzed, and verified using a combination of open-source software, including QGIS, PostgreSQL, PostGIS, Python, R and the GDAL library, all running on Linux.</p> <ul> <li>amap_raster_holdridge_nc.tif is a GeoTIFF, utilizing the WGS84 international coordinate system, and consists of a single band with three major classes coded as <ul> <li>Dry life zone (rast = 1)</li> <li>Moist life zone (rast = 2)</li> <li>Rain life zone (rast = 3)</li> </ul> </li> <li>holdridge_3classes_NC.png is an image illustrating the valid domain of life zones in New Caledonia and the classification used in the dataset.</li> </ul> <h1>Limitations</h1> <p>Strictly, the classification leads to five distinct classes (very dry, dry, moist, wet, and rain), but as the two extreme classes cover less than 0.5% of New Caledonia, we merged very dry and dry into the "dry" class, as well as wet and rain into the "rain" class as illustrated in the Figure <a href="../api/records/12731521/draft/files/holdridge_3classes_NC.png/content" target="_blank" rel="noopener noreferrer">holdridge_3classes_NC.png</a>.</p>
EUNIS-ESy: Expert system for automatic classification of European vegetation plots to EUNIS habitats
<p><strong>EUNIS-ESy</strong> is an expert system for automatic classification of European vegetation plots to habitat types of the EUNIS Habitat Classification. The EUNIS classification and the principles of the expert system are described by <a href="https://doi.org/10.1111/avsc.12519">Chytrý et al. (2020)</a>. The classification of a set of vegetation plots can be run using the JUICE program (<a href="https://doi.org/10.1111/j.1654-1103.2002.tb02069.x">Tichý 2002</a>; <a href="https://www.sci.muni.cz/botany/juice/">https://www.sci.muni.cz/botany/juice/</a>), TURBOVEG 3 program (Hennekens 2015) and an R script (<a href="https://doi.org/10.1111/avsc.12562">Bruelheide et al. 2021</a>).</p> <p>This dataset contains two parts: (1) the expert system and related files necessary for running it; (2) characterization of EUNIS habitats based on the results of the expert system classification.</p> <p><strong>1. Expert system and related files necessary to run it</strong></p> <p>1.1. <strong>EUNIS-ESy-2025-10-03.txt </strong>– a file containing the script for the classification of vegetation plots by EUNIS-ESy. This version contains tested definitions for the revised EUNIS classification of vegetated Marine (MA), Coastal (N), Wetland (Q), Grassland (R), Shrubland (S), Forest (T), Inland sparsely vegetated (U) and Man-made (V). It also contains tested definitions of Aquatic plant communities (P3) and Springs (P2N). This file is different from the analogous file in the previous versions.</p> <p>1.2. <strong>Nomenclature-translation-from-Turboveg-2-databases.zip </strong>– an archive containing the scripts for automatic translation of taxon concepts and names used in individual European Turboveg 2 databases (<a href="https://doi.org/10.2307/3237010">Hennekens & Schaminée 2001</a>; <a href="https://www.synbiosys.alterra.nl/turboveg/">https://www.synbiosys.alterra.nl/turboveg/</a>) to the nomenclature that can be used as an input for EUNIS-ESy. This file is the same as in the previous versions.</p> <p>1.3. <strong>EUNIS-ESy-User-Guide.pdf </strong>– a brief user guide to the classification of vegetation plots by EUNIS-ESy using the JUICE program. Please read this guide carefully before running the expert system to avoid misclassifications. This file is the same as in the previous versions.</p> <p><strong>2. Characterization of the EUNIS habitats based on the results of the EUNIS-ESy classification</strong></p> <p>2.1. <strong>EUNIS-habitats-2025-10-03.xlsx </strong>– the current list of EUNIS habitats. This file is different from the analogous file in the previous versions.</p> <p>2.2. <strong>EUNIS-EuroVegChecklist-crosswalk-2025-10-03.xlsx</strong> – a crosswalk between the EUNIS habitat classification and phytosociological alliances of EuroVegChecklist (<a href="http://doi.org/10.1111/avsc.12257">Mucina et al. 2016</a>; <a href="https://floraveg.eu/vegetation/">https://floraveg.eu/vegetation/</a>).</p> <p>2.3. <strong>EUNIS-habitats-Characteristic-species-combintation-2025-10-03.xlsx </strong>– a database of habitats' characteristic species combinations in a spreadsheet format. These species combinations are based on the analysis of vegetation plots from the European Vegetation Archive (EVA; <a href="https://doi.org/10.1111/avsc.12191">Chytrý et al. 2016</a>; <a href="http://euroveg.org/eva-database">http://euroveg.org/eva-database</a>) and other databases classified by EUNIS-ESy v2025-10-03. Analytical methods are described in <a href="https://doi.org/10.1111/avsc.12519">Chytrý et al. (2020)</a>. This file is different from the analogous file in the previous versions.</p> <p>2.4. <strong>EUNIS-habitats-Distribution-maps-2025-10-03.xlsx </strong>– a set of distribution maps in the TIFF format based on the analysis of vegetation plots from the European Vegetation Archive (EVA; <a href="https://doi.org/10.1111/avsc.12191">Chytrý et al. 2016</a>; <a href="http://euroveg.org/eva-database">http://euroveg.org/eva-database</a>) and other databases classified by EUNIS-ESy v2025-10-03.</p> <p>2.5. <strong>Data-sources-EUNIS-classification-2025-10-03.pdf </strong>– a list of data sources used to produce the distribution maps and characteristic species combinations.</p> <p>-----------------------------------------------------------------------------------------------------</p> <p><strong>Differences from the previous version (2021-06-01)</strong></p> <p>Aquatic plant communities (P3), spring (P2N), some wetland (Q61-Q63) and some inland sparsely vegetated (U71-U72) habitats were added to the EUNIS-ESy expert system. Plant taxon concepts and nomenclature were extensively revised. Some previously included habitat definitions were slightly refined. New vegetation-plot records added to the EVA database by 8 August 2025 were used to characterize habitat types. Unlike in the previous version, this version does not provide Habitat factsheets because summarized information about each habitat is now available in the FloraVeg.EU database at <a href="https://floraveg.eu/habitat/">https://floraveg.eu/habitat/</a>.</p> <p>-----------------------------------------------------------------------------------------------------</p> <p> </p> <p><strong>Recommended citation of this version of the EUNIS-ESy expert system</strong></p> <p>Chytrý et al. (2020), version 2025-10-03</p> <p>Chytrý M., Tichý L., Hennekens S.M., Knollová I., Janssen J.A.M., Rodwell J.S., Peterka T., Marcenò C., Landucci F., Danihelka J., Hájek M., Dengler J., Novák P., Zukal D., Jiménez-Alfaro B., Mucina L., Abdulhak S., Aćić S., Agrillo E., Attorre F., Bergmeier E., Biurrun I., Boch S., Bölöni J., Bonari G., Braslavskaya T., Bruelheide H., Campos J.A., Čarni A., Casella L., Ćuk M., Ćušterevska R., De Bie E., Delbosc P., Demina O., Didukh Y., Dítě D., Dziuba T., Ewald J., Gavilán R.G., Gégout J.-C., Giusso del Galdo G.P., Golub V., Goncharova N., Goral F., Graf U., Indreica A., Isermann M., Jandt U., Jansen F., Jansen J., Jašková A., Jiroušek M., Kącki Z., Kalníková V., Kavgacı A., Khanina L., Korolyuk A.Yu., Kozhevnikova M., Kuzemko A., Küzmič F., Kuznetsov O.L., Laiviņš M., Lavrinenko I., Lavrinenko O., Lebedeva M., Lososová Z., Lysenko T., Maciejewski L., Mardari C., Marinšek A., Napreenko M.G., Onyshchenko V., Pérez-Haase A., Pielech R., Prokhorov V., Rašomavičius V., Rodríguez Rojo M.P., Rūsiņa S., Schrautzer J., Šibík J., Šilc U., Škvorc Ž., Smagin V.A., Stančić Z., Stanisci A., Tikhonova E., Tonteri T., Uogintas D., Valachovič M., Vassilev K., Vynokurov D., Willner W., Yamalov S., Evans D., Palitzsch Lund M., Spyropoulou R., Tryfon E., Schaminée J.H.J. (2020) EUNIS Habitat Classification: expert system, characteristic species combinations and distribution maps of European habitats. Applied Vegetation Science, 23, 648–675. https://doi.org/10.1111/avsc.12519</p>
Supplemental Information to Climate-driven habitat shifts of high-ranked prey species structure Late Upper Paleolithic hunting
<p>The data provided here are the supplemental information accompanying Yaworsky et al, 2023 in the journal <em>Scientific Reports</em>. These data represent the following, which are referenced in the published work at DOI: 10.1038/s41598-023-31085-x.</p> <p><strong>Below is the legend for the Supplementary Information</strong>, including how it is referenced within the text of the publication, the file name, and a brief description. More thorough descriptions of the data can be found within the publication in <em>Scientific Reports</em>.</p> <p><strong>Supplementary 1</strong> – <em>UpperPaleoDietV4.html</em> – HTML document of the analyses performed and presented in the paper. This is a Markdown document compiled in R with R code chunks and descriptions.</p> <p><strong>Supplementary 2</strong> – <em>Support Information 2.docx</em> – Word document containing supplementary tables 2 and 3.</p> <p><strong>Supplementary 3</strong> – <em>ArchaeoloigcalDataset_v8.csv</em> – Archaeological data referenced in the Material and Methods. These data are necessary for running the code presented in SI 1.</p> <p><strong>Supplementary 4</strong> – <em>EuroUpperPaleoFaunas_v6.csv</em> – Zooarchaeological data referenced in the Material and Methods. These data are necessary for running the code presented in SI 1.</p> <p><strong>Supplementary 5 </strong>– <em>Lupo2016.csv</em> – Data of Arficant fauna weight derived from table in Lupo and Schmitt 2016 (Table 2). These data are necessary for running the code presented in SI 1.</p> <p><strong>Supplementary 6</strong> – <em>PushkinaRaia_FaunaWeights.csv</em> – Data of Pleistocene fauna weights derived from table in Pushkina and Raia 2008 (Table 1). These data are necessary for running the code presented in SI 1.</p> <p><strong>Supplementary 7</strong> – <em>environmental_BG.csv</em> – Data representing background environmental conditions derived from the CHELSA TRaCE21k data. These data are necessary for running the code in SI 1.</p> <p>For more information on the data, methods, and results, please see the main paper. </p> <p> </p> <p> </p>
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.