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.
2,837
datasets available to search
ShareScore release 0.7.1
Dataset results
2,837 results for “climate data”
Data for "Climate change is shifting and narrowing prescribed fire windows in the Western United States"
<p><strong>The data archived here represent two types of information used in the publication “Climate change is shifting and narrowing prescribed fire windows in the Western United States” by Swain et al. 2023:</strong></p> <p>1) The meteorological and vegetation dryness/fuel moisture data (as described within the file) extracted from prescribed fire burn plans around the Western United States (drawn from entities such as the U.S. Forest Service, U.S. National Park Service, and The Nature Conservancy) between 2002 and 2022. This data is provided in tabular form, both as an .xlsx file and a .csv file for the convenience of the user. In addition to the specific values from each burn plan, summaries of median values for forested and non-forested landscapes are provided as well.</p> <p>2) The number of days on which environmental conditions (i.e., weather and vegetation fuel moistures) are acceptable for prescribed fire according to the composite metric described in Swain et al. 2023 (known as “RxDays”). Underlying RxDay definitions are different for forest and non-forested landscapes. This data is provided in geospatially explicit (gridded) form as NetCDF files, which are a self-describing file format. Each file is provided as a single 3-dimensional hypercube (i.e., dimensions of time, latitude, and longitude, respectively; units and details described within the file) corresponding to the number of RxDays per calendar month.</p> <p>One file is provided for each climate model iteration (as identified in each filename); these represent projected RxDays at monthly scale between 1981 and 2060 using an RCP 4.5 climate forcing trajectory. An additional file (rx_gridMET.nc) is provided that represents the same values from an atmospheric reanalysis dataset, which represents a best estimate of observed RxDay values (1981-2020).</p>
WTF Climate dataset: 4 years of weather data from tropical Queensland, Australia
This repository contains the code for constructing a 4-year climate dataset for the WTF project. Time series were constructed for 6 field sites in Queensland, Australia at 1 hour resolution.
MPIC OMI Total Column Water Vapour (TCWV) Climate Data Record
<p>The upload contains a long-term data set of 1°x 1° monthly mean total column water vapour (TCWV) retrieved in the visible "blue" spectral range from global measurements of the Ozone Monitoring Instrument (OMI). The TCWV data set covers the time range from January 2005 to December 2020.</p>
Raw Data for Publication: Fungal colonisation on wood surfaces weathered at diverse climatic conditions
<p>Colour_Changes_Izola.csv</p> <p>This file contains CIE Lab* color coordinates measured on the surface of Scots pine during the natural weathering test in Izola, Slovenia.</p> <p>Colour_Changes_Skelleftea.csv</p> <p>This file contains CIE Lab* color coordinates measured on the surface of Scots pine during the natural weathering test in Skelleftea, Sweden.</p> <p>Contact_Angles_Izola.csv</p> <p>This file contains dynamic contact angle with distilled water measured on the surface of Scots pine during the natural weathering test in Izola, Slovenia.</p> <p>Contact_Angles_Skelleftea.csv</p> <p>This file contains dynamic contact angle with distilled water measured on the surface of Scots pine during the natural weathering test in Skelleftea, Sweden.</p> <p>Gloss.csv</p> <p>This file contains the gloss value measured on the surface of Scots pine during the natural weathering test in Izola, Slovenia and Skelleftea, Sweden.</p> <p><strong>Note:</strong> The sample IDs are structured as follows: The first letter represents the treatment condition, with "R" indicating untreated wood. The second letter (A, B, C) represents the board's ID. The third letter represents the location, with "S" indicating Skelleftea and "I" representing Izola, Slovenia. The number indicates the exposure time in weeks.</p> <p>FUNGAL STRAIS_DNA sequence analysis.xlsx</p> <p>This file contains the Genomic DNA of the fungal strains detected on the surface of Scots pine during the natural weathering test in Izola, Slovenia and Skelleftea, Sweden.</p> <p>Weather_Data_Izola.csv</p> <p>This file contains hourly local weather conditions in Izola, Slovenia</p> <p>Weather_Data_Skelleftea.csv</p> <p>This file contains hourly local weather conditions in Skelleftea, Sweden</p> <p><strong>Note:</strong> The weather conditions including the following parameters:1. Air temperature (°C), 2. Dew point (°C), 3. Relative humidity (%), 4. One-hour precipitation total (mm), 5. Snow depth (mm), 6. Wind direction (°), 7. Average wind speed (km/h), 8.Sea-level air pressure (hPa)</p>
AdriSC Climate Model Data - For the article: Projecting expected growth period of bivalves in a coastal temperate sea
<p>The recent implementation, development and successful runs of the kilometer-scale atmosphere-ocean Adriatic Sea and Coast (AdriSC) climate model for the historical period of 1987-2017 and for an extreme climate projection (RCP 8.5) for the 2070-2100 period, have provided the necessary dataset to better understand the potential impact of climate change within the Adriatic basin. Here, temperature, salinity and ocean currents were extracted and formatted from the AdriSC ocean model at 1 km resolution. This dataset was then used to reproduce in the past (1987-2017 period) and project in the future (2070-2100 period) the expected growth of five bivalve species in the northern Adriatic Sea at two different locations: Barbariga and along the western coast of Istria. </p> <p> </p>
Data for ECHAM-HAM in the Publication "Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions"
<p>This repository contains 3-hourly data of the ECHAM-HAM experiment in the paper:</p> <p>"Saponaro, G., Sporre, M. K., Neubauer, D., Kokkola, H., Kolmonen, P., Sogacheva, L., Arola, A., de Leeuw, G., Karset, I. H. H., Laaksonen, A., and Lohmann, U.: Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions, Atmos. Chem. Phys., 20, 1607–1626, https://doi.org/10.5194/acp-20-1607-2020, 2020."</p>
Data for "Realistic representation of mixed-phase clouds increases future climate warming
<p>Data to reproduce the figures from Hofer et al. (2023) <a href="https://www.researchsquare.com/article/rs-2981113/v1">Realistic representation of mixed-phase clouds increases future climate warming</a></p>
Data and code for Haberle, Hackenberger et al.: Effects of climate change on gilthead seabream aquaculture in the Mediterranean
<p>The submission was prepared to accompany the publication Haberle, Hackenberger et al. "Effects of climate change on gilthead seabream aquaculture in the Mediterranean" in Aquaculture (https://doi.org/10.1016/j.aquaculture.2023.740052).</p> <p>The simulations source code is available through GitHub repository at:<br> https://github.com/QuantEcoLab/SparusSim_Haberle_et_al_2023</p> <p>The Zanodo archive contains GeoTIFF images underlying the figures in the publication, with the corresponding description in the Readme file.</p>
IMAU-FDM v1.2G and RACMO2.3p2 data on ice slabs, aquifers and their climatic drivers
<p>Model results used in the following paper:</p> <p>Brils, M., Kuipers Munneke, P., Jullien, N., Tedstone, A.J., Machguth, H., van de Berg, W. J., & van den<br> Broeke, M. R. Climatic drivers of ice slabs and firn aquifers in Greenland</p> <p> </p> <p>Contains:</p> <p>- 10-daily ice slab fraction within the upper 20 m of firn</p> <p>- 10-daily total irreducible liquid water content firn</p> <p>- monthly snow accumulation and melt</p>
Low-energy Museum Storage Buildings: Climate, Energy Consumption and Air Quality. Data Set for Final Data Report
<p>The 43 txt-files included in this dataset relate to the report: Ryhl-Svendsen, Jensen, Bøhm, and Klenz Larsen (2012): <em>Low-energy Museum Storage Buildings: Climate, Energy Consumption and Air Quality. UMTS Research Project 2007</em>–<em>2011: Final Data Report</em>, Kgs. Lyngby: National Museum of Denmark, 122 pp.</p> <p>The document <a href="https://zenodo.org/api/files/145584b0-46b5-4341-8b02-7dfea90fa97c/00_List-of-data-files.pdf?versionId=a3e9691f-6e73-4a7b-aaab-c8ccee7c419b">00_List-of-data-files.pdf</a> contain a full list of the data files with a description of their structure and content, and is the key to how the individual data files relate to the report. </p> <p>The research project focussed on four modern museum storage facilities in Denmark, for which the indoor climate, air quality, and the energy consumption of the climate control systems was measured at several locations, typically for a period of between two and four years. The storage facilities were Museum of Southwest Jutland’s storage building in Ribe (‘Ribe’), The Shared Storage Facility at The Centre for Preservation of Cultural Heritage in Vejle (‘Vejle’), The Joint Storage Facility for museums in East Jutland/ Museum Østjylland (‘Randers’), and from The National Museum of Denmark the storage building Hall P at the Ørholm Storage Facility (‘Ørholm’). For description of the sites, monitoring campaigns, and graphed data, the report should be consulted.</p> <p>For completeness, the report is included with the dataset (<a href="https://zenodo.org/api/files/145584b0-46b5-4341-8b02-7dfea90fa97c/Report_low-energy-museum-storage-buildings.pdf?versionId=44097d39-775b-4031-9e07-6978c68912a9">Report_low-energy-museum-storage-buildings.pdf</a>).</p>
Data and code for the paper "Attention, sentiments and emotions towards emerging climate technologies on Twitter"
<p>This is the code and data for the paper "Attention, sentiments and emotions towards emerging climate technologies on Twitter" by Müller-Hansen et al. (Global Environmental Change, 2023).</p><p>This archive contains the following materials:</p><ul><li>Data set of tweets</li><li>Table of subqueries for searching Twitter</li><li>Code for figure generation</li></ul><p>Please see the Readme for further details.</p><p> </p>
Data and code for "Climate change shapes richness-evenness relationships in a subalpine grassland experiment"
<p>Aggregated Biodiversity metrics derived from the biodiversity data collected from the ClimGrass experiment at the Agricultural Research and Education Centre (AREC) in Raumberg-Gumpenstein. Data and code needed to reproduce the analyses in "Climate change shapes richness-evenness relationships in a subalpine grassland experiment" are included.</p>
Climate data for Mojave National Preserve Granite Mountains 2019
Basic climate data derived from a local weather station. Mean and max temp with humidity relevant to avian abundance surveys conducted at that location during those specific time blocks.
LAGOS-NE v.1.054.1 Lake water clarity time series (1987-2011), climate, and geophysical data for 601 lakes across a 17-state region of the United States
Time series of median summer water clarity (secchi) values from 601 unique lakes in the Midwest and Northeast United States. Water clarity observations were derived from the Lake Multi-Scaled Geospatial and Temporal Database LAGOS-NELIMNO version 1.054.1. These data were used to assess long-term changes in water clarity from 1987-2011, and the potential drivers of those trends (Lottig et al. in press). Summer open water period was used to approximate the stratified period in the study lakes, which was defined as June 15 to September 15. Over the 25-year time period, each lake had to have at least a single summer water clarity observation for 22 of 25 years. The median number of secchi measurements that were used to derive a single annual median value for each lake was approximately 9. Of the over 14,000 annual estimates of water clarity that we generated, only two percent of those annual values were generated from a single observation and median number of observations for each lake over the 25-year study period was 223. Each unique lake with water clarity data also has supporting geophysical data, including climate, land use, hydrology, and topography derived at multiple spatial scales. Lake-specific characteristics, such as depth and area, are also reported. The geospatial data came from LAGOS-NEGEO version 1.03 except for the annual climate data which was aggregated at the HUC8 spatial scale from monthly PRISM data. For more specific information on how LAGOS-NE was created, see Soranno et al. 2015. Citations: Lottig, N.R., P-N. Tan, T. Wager, K.S. Cheruvelil, P.A. Soranno, E.H. Stanley, C.E Scott, C.A. Stow, and S. Yuan. in press. Macroscale patterns of synchrony identify complex relationships among spatial and temporal ecosystem drivers. Ecosphere Soranno P.A., Bissell E.G., Cheruvelil K.S., Christel S.T., Collins S.M., Fergus C.E., Filstrup C.T., Lapierre J.-F., Lottig N.R., Oliver S.K., Scott C.E., Smith N.J., Stopyak S., Yuan S., Bremigan M.T., Downing J.A., G
Data from Citizen science data reveal regional heterogeneity in phenological response to climate in the large milkweed bug, Oncopeltus fasciatus
These data include annotations for life stage, mating behavior, and plant part occupancy of large milkweed bug observations in North America as well as information about climate and environment.
Net primary production (NPP) and climate data from Sevilleta LTER core and control sites in desert grassland and shrubland ecosystems, 1999 - 2017
This dataset and R code were used to create the figures, tables and statistical analyses for the following publication: Rudgers, JA et al. 2018. Climate sensitivity functions and net primary production: A framework for incorporating climate mean and variability. Ecology. Data were collected by the Sevilleta LTER program, which is located in the Sevilleta National Wildlife Refuge (SNWR), New Mexico. These are long-term, continuing data sets. Data collection started in 1999 at the black grama grassland and creosote shrubland, and in 2002 for blue grama grassland. Meteorological stations started recording data as early as 1989. The study abstract from Rudgers et al. 2018 is: Understanding controls on net primary production (NPP) has been a long-standing goal in ecology. Climate is a well-known control on NPP, although the temporal differences among years within a site are often weaker than the spatial pattern of differences across sites. Climate sensitivity functions describe the relationship between an ecological response (e.g., NPP) and both the mean and variance of its climate driver (e.g., aridity index), providing a novel framework for understanding how climate trends in both mean and variance vary with NPP over time. Nonlinearities in these functions predict whether an increase in climate variance will have a positive effect (convex nonlinearity) or negative effect (concave nonlinearity) on NPP. The influence of climate variance may be particularly intense at ecosystem transition zones, if species reach physiological thresholds that create nonlinearities at these ecotones. Long-term data collected at the confluence of three dryland ecosystems in central New Mexico revealed that each ecosystem exhibited a unique climate sensitivity function that was consistent with long-term vegetation change occurring at their ecotones. Our analysis suggests that rising temperatures in drylands could alter the nonlinearities that determine the relative costs and benefits of varia
Data and code from: A mixture of grass-legume cover crop species may ameliorate water stress in a changing climate, a greenhouse experiment at Dickinson College in Carlisle, PA, USA, 2021.
Data and R code associated with a greenhouse study investigating the influence of water stress on growth, root traits, and biomass of rye and crimson clover seedlings grown separately or together. Data were collected in the Dr. Inge P. Stafford Greenhouse of Dickinson College (Carlisle PA, USA) in June 2021.
Plant aboveground biomass data: BAC: Biodiversity and Climate (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/124/5, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-cdr/386/8. The abstract below was extracted from the Level 0 data package and is included for context: Climate changes forecast for our region by GCM???s and shifts in biodiversity and composition each have the potential to alter ecosystem functioning; their interactive effects are unknown. The "BAC" experiment is designed to determine the direct and interactive effects of plant species numbers, plant community composition, temperature, and precipitation on 11 productivity, C and N dynamics, stability, and plant, microbe, and insect species abundances in CDR grassland ecosystems.
Mountain Lake Biology, Chemistry, Physics, and Climate Data since 1959 at Castle Lake
This data set contains long-term limnology data from Castle Lake (located 5440 ft above sea level in Northern California) on primary productivity, zooplankton and phytoplankton measurements, dissolved oxygen, nutrient chemistry, lake morphology, and fish measurements. Short-term research projects on benthic invertebrates composition and a bathymetric map can also be found within the data package. Ecological, watershed and climatological measurements taken characterize the limnology of Castle Lake, a pristine glacial cirque that is the largest (by volume) of the 25 alpine and sub-alpine lake within the larger Upper Sacramento River Watershed. Sampling Frequency: Varies depending on survey conducted. During summer months- multiple times during each month. Less frequent during winter months. This data collection part of an ongoing project funded by the US National Science Foundation, private donors, US AID, and the University of Nevada's Global Water Center.
Climate data from Arctic LTER Toolik Inlet Wet Sedge site, Toolik Field Station, Alaska 2012 to 2018.
Two Figaro TGS 2600 sensors were installed at the Toolik Wet Sedge site in late June 2012 to 2018. The TGS 2600 was the first low-cost solid state sensor that shows a weak response to ambient levels of methane (i.e., range ~1.8–2.7 µmol mol–1). Reference CH4 mole fractions were measured by a Fast Methane Analyser during the years 2012–2016 which was replace by a Fast Greenhouse Gas Analyzer (Los Gatos Research, Inc., San Jose, CA, U.S.A.) in 2016. Data were used in the Eugster et al. (2020) study on Long-term reliability of the Figaro TGS 2600 solid-state methane sensor under low Arctic conditions at Toolik lake, Alaska (doi:10.5194/amt-2019-402)
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.