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.
1,121
datasets available to search
ShareScore release 0.9.0
Dataset results
1,121 results for “weathering”
Extreme weather events threaten biodiversity and functions of river ecosystems: dataset for conducting the meta-analysis
<p>This repository contains the code and dataset to replicate the meta-analysis conducted by Sabater et al. entitled "Extreme weather events threaten biodiversity and functions of river ecosystems: evidence from a meta-analysis"</p> <p>Metadata:</p> <p>- metaanalysis_GlobalEvidenceRivers_Rscript.R - R Script to conduct the meta-analysis</p> <p>- structural_resp.csv - table with data to perform the species richness, density, and biomass meta-analysis. It includes the mean, SD (or SE), and sample number of the studies included in the meta-analysis, as well as information on the paper authors, year of publication, and type of study (experimental or observational). It also includes co-variates and the author who subtracts the information from the paper.</p> <p>- functional_resp.csv - table with data to perform the primary productivity, respiration, and decomposition meta-analysis. It includes the mean, SD (or SE), and sample number of the studies included in the meta-analysis, as well as information on the paper authors, year of publication, and type of study (experimental or observational). It also includes co-variates and the author who subtracts the information from the paper.</p> <p>-refMap.csv - Geographical information of the papers included in the meta-analysis.</p>
Data related to NAHAYO et al (2022), to appear in AGU Space Weather Journal (2022SW003092)
<p>Geomagnetic data related to the publication by Nahayo, et al. (2022).</p> <p>The file ending in "event1.csv" is for the first event (October 2003) and the second file, filename ending "event2.csv" for the second event discussed in the paper (March 2015).</p> <p> </p>
What weather variables are important for wet and slab avalanches under a changing climate in low altitude mountain range in Czechia?
<p>datasets and scripts for Avalanche paper figures and<br> avalanche path characteristics: Avalanche_paths_souckova.xlsx<br> </p>
Space Weather Environment During the SpaceX Starlink Satellite Loss in February 2022
<p>All data used in support of the journal article of the same name, including:</p> <ul> <li>operational NCEP/SWPC Whole Atmosphere Model (WAM), NRLMSISE-00, NRLMSIS 2.0, and DTM2020 fixed-height output of neutral atmospheric density in NetCDF format at ten-minute cadence</li> <li>observed and forecasted solar wind and geomagnetic space weather drivers in XML format</li> <li>NOAA Space Weather Prediction Center text advisory of geomagnetic activity</li> <li>select post-launch tracks (minimum of lat/lon/alt in one-minute cadence) of Starlink satellites for three launches: Group 4-4, December 2021; Group 4-5, January 2021; and Group 4-7, February 2021</li> </ul>
Composite geostationary weather satellite images (second time derivative of water vapor channel) for visualizing Lamb waves
<p>Second time derivative of water vapor channel (6.2 micrometer) brightness temperature from geostationary weather satellites (Units: K s<sup>-2</sup>)</p> <p>Himawari-8 (original data obtained from NICT Science Cloud)</p> <p>GOES-16/17 (original data obtained from Amazon AWS)</p> <p>Meteosat-8/9/10/11 (original data obtained from EUMETSAT)</p> <p> </p> <p>Time interval of the files: 5 minutes</p> <p> </p> <p>Time interval of each satellite, dt for time derivative:</p> <p>Himaawri-8, GOES-16/17: 10 minutes, 10 minutes</p> <p>Meteosat-8/9/11: 15 minutes, 15 minutes</p> <p>Meteosat-10: 5 minutes, 10 minutes</p> <p> </p> <p>Each file contains the latest images from those satellites at that time. The time stamp for each satellite represents the beginning of each full-disk scan.</p> <p> </p> <p>Bias correction:</p> <p>Himawari-8: bias removal for each swath</p> <p>GOES-16/17, Meteosat-11: bias removal for each east-west line</p> <p>Meteosat-8/9/10: bias removal for each east-west line (note: satellite attitude was not stable)</p> <p> </p> <p>Smoothing:</p> <p>Band-pass filter for each full-disk image separately: 2-40 degrees on lat-lon coordinate</p> <p>Stronger smoothing at latitudes higher than 60 degrees north/south</p> <p> </p> <p>Down-sampling:</p> <p>Full-disk images were mapped to a 0.04-degree lat-lon coordinate.</p> <p>Then, composite images were produced at the 0.2-degree resolution.</p> <p> </p> <p>Version 2:</p> <p>Improved interpolation algorithm</p> <p>Himawari-8: improved geolocation</p> <p>Meteosat-8/9: improved treatment of noise near the edge of full disk images</p>
Data for "Impact of Gaussian transformation on cloud cover data assimilation for historical weather reconstruction"
<p>This dataset contains the simulation results in "Impact of Gaussian transformation on cloud cover data assimilation for historical weather reconstruction".</p>
Text-fig. 12. a: Spirophycus cf. bicornis (HEER, 1877), BK 19, Layer No. 6; b: Spirophycus isp., field photo, Layer No. 23; c–e: Teichichnus isp., full relief, partly weathered, from the upper side, field photos, Layers No. 23, 10 and 23; f, g: Thalassinoides isp., f – BK 12, Layer No. 4, g – field photo, Layer No. 1. Scale bar = 1 cm; field scale in centimetres. in Early Complex Tiering Pattern: Upper Ordovician, Barrandian Area, The Czech Republic
Text-fig. 12. a: Spirophycus cf. bicornis (HEER, 1877), BK 19, Layer No. 6; b: Spirophycus isp., field photo, Layer No. 23; c–e: Teichichnus isp., full relief, partly weathered, from the upper side, field photos, Layers No. 23, 10 and 23; f, g: Thalassinoides isp., f – BK 12, Layer No. 4, g – field photo, Layer No. 1. Scale bar = 1 cm; field scale in centimetres.
Text-fig. 2. Kaolin clay pit at hill Hasenberg in Wiesa, Saxony, Germany; view of southern high wall, showing deeply weathered late Early Miocene lignite seam by dark brown color in center (photographed 2015). Fossil-bearing strata were reported (e.g., Mai 1964) as below lignite seam, but this horizon does actually not crop out (also evidenced by new drillings, communicated by Dr. Jochen Rascher, GEOMONTAN GmbH company, Freiberg/Sa., Germany). in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 2. Kaolin clay pit at hill Hasenberg in Wiesa, Saxony, Germany; view of southern high wall, showing deeply weathered late Early Miocene lignite seam by dark brown color in center (photographed 2015). Fossil-bearing strata were reported (e.g., Mai 1964) as below lignite seam, but this horizon does actually not crop out (also evidenced by new drillings, communicated by Dr. Jochen Rascher, GEOMONTAN GmbH company, Freiberg/Sa., Germany).
An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based on Ensemble Weather Forecast Data
<p>This repository contains the data used in "An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based on Ensemble Weather Forecast Data" by Oettli and Kotsuki (submitted to Journal of Geophysical Research: Atmospheres).</p>
HarmonEPS modified routines, post-processing scripts and example data used in Tsiringakis, A., Frogner, I.L., de Rooy, W., Andrae, U., Hally, A., Contreras Osorio, S., van der Veen, S. and Barkmeijer, J., An Update to the Stochastically Perturbed Parametrizations Scheme of HarmonEPS. Monthly Weather Review
<p>This dataset contains:</p> <p>- Modified code routines/configurations files used in the EPS of Harmonie-Arome (HarmonEPS) CY43H2.2 version of the model.</p> <p>- Verification scripts from the HARP verification tool, used to verify model output against SYNOP observations.</p> <p>- Post-processing and plotting scripts in python, used in the manuscript.</p> <p>- A subset of the data produced by this study as example input in the verification and post-processing/plotting scripts.</p> <p>This dataset is used in:</p> <p>~Tsiringakis, A., Frogner, I.L., de Rooy, W., Andrae, U., Hally, A., Contreras Osorio, S., van Der Veen, S. and Barkmeijer, J. An Update to the Stochastically Perturbed Parametrizations Scheme of HarmonEPS. Monthly Weather Review</p>
ARSO weather data for flood prediction
<p>This data was gathered and aggreagated from ARSO - SLOVENIAN ENVIRONMENTAL AGENCY - archive.</p> <p>Column info:</p> <ul> <li>station_id = id of the station used for weather data from ARSO</li> <li>padavine_klicina = amount of rain in mm</li> <li>temperatura_avg = average temperature in C</li> <li>veter_hitrost = wind speed in m/s</li> <li>veter_sunki = max wind speed in m/s</li> <li>month = month number</li> <li>vodostaj_cm = water level in cm</li> </ul> <p>Vir podatkov: Agencije Republike Slovenije za okolje (ali skrajšano: ARSO). ARSO ne prevzema nobene odgovornosti v povezavi z uporabo objavljenih podatkov ali za odločitve, sprejete na osnovi objavljenih podatkov.</p>
Figure 2 in Weathering of Torula Yeast Borax Food Bait and Capture of Oriental, Mediterranean, and Melon Fruit Flies in Hawaii (Diptera: Tephritidae)
Figure 2. Captures of Mediterranean fruit flies (males and females combined) in Multilure traps baited with torula yeast borax weathered 3, 6, 9, or 12 d. Traps operated for 2 d at weekly intervals over 5 consecutive weeks. Symbols represent means + 1 SE (N = 10 traps/treatment/sampling period).
Figure 1 in Weathering of Torula Yeast Borax Food Bait and Capture of Oriental, Mediterranean, and Melon Fruit Flies in Hawaii (Diptera: Tephritidae)
Figure 1. Captures of oriental fruit flies (males and females combined) in Multilure traps baited with torula yeast borax weathered 3, 6, 9, or 12 d. Traps operated for 2 d at weekly intervals over 8 consecutive weeks. Symbols represent means + 1 SE (N = 10 traps/treatment/sampling period).
Figure 3 in Weathering of Torula Yeast Borax Food Bait and Capture of Oriental, Mediterranean, and Melon Fruit Flies in Hawaii (Diptera: Tephritidae)
Figure 3. Captures of melon flies (males and females combined) in Multilure traps baited with torula yeast borax weathered 3, 6, 9, or 12 d. Traps operated for 2 d at weekly intervals over 4 consecutive weeks. Symbols represent means + 1 SE (N = 10 traps/treatment/sampling period).
Figure 1 in Testing the Temporal Limits of Lures and Toxicants for Trapping Fruit Flies (Diptera: Tephritidae): Additional Weathering Studies of Solid Bactrocera and Zeugodacus Male Lures and Associated Insecticidal Strips
Figure 1. Captures of Zeugodacus cucurbitae males in Jackson traps containing toxicants of variable age deployed at Aloun Farm, Oahu, Hawaii. The lures were fresh in all traps and were prepared in Hawaii at the start of the test. Two fresh toxicants were included: naled in liquid CL (bar labelled L) and a DDVP strip with a CL plug (bar labelled P). The DDVP strips weathered in Arizona and Florida were tested during the same 1-day period (December 9–10, 2015). Values represent means (+ 1 SE); 12 traps were deployed per treatment. Bars marked by different letters were significantly different (Student-Newman-Keuls multiple comparisons test).
Figure 1 in The effects of weather and reed management on nesting parameters of the Great Reed Warbler, Acrocephalus arundinaceus (Aves: Sylviidae)
Figure 1. The relationship between mean water depth and nest density of the Great Reed Warbler at the Bager Pond for the period 2008-2016.
Figure 2 in The effects of weather and reed management on nesting parameters of the Great Reed Warbler, Acrocephalus arundinaceus (Aves: Sylviidae)
Figure 2. The relationship between the amount of precipitation and nesting success of the Great Reed Warbler at the Bager Pond for the period 2008-2016.
Data from: Impacts of weathered microplastic ingestion on gastrointestinal microbial communities and health endpoints in fathead minnows (Pimephales promelas)
<p>Microplastics are a ubiquitous presence in the world's aquatic environments and their threat to aquatic biota is poorly understood, especially in freshwater ecosystems. In the environment, microbial biofilms can form on the surface of microplastics, and these plastics have the potential to adsorb harmful toxins. Because lab-based studies on microplastics are often conducted with clean polymers, in ecologically unrealistic conditions and concentrations, the impact of these weathered microplastics on aquatic organisms in ecologically realistic conditions is still unclear. To help address the need for ecologically relevant microplastic exposure data, we incubated 500 μm polyethylene microplastic beads in Muskegon Lake, Michigan, USA and used them to conduct a 28-day ingestion study with male and female fathead minnows (<em>Pimephales promelas</em>). We examined the effects of microplastic ingestion on the fish gut microbial community along with hepatic gene expression and health parameters. We found that microplastic ingestion had statistically significant impacts on growth in male fathead minnows. Microplastic treatment did not significantly alter the beta diversity of the gut microbial community for either males or females, but there were clear differences between sexes and over time, indicating that these factors may outweigh the impacts of microplastic ingestion on beta diversity in the gut. The expression of immune response genes was not altered in males. It did, however, cause some changes to alpha diversity metrics in both sexes and there were several differentially abundant taxa among treatments. These data suggest that microplastic ingestion has health effects, but these effects may be sex specific across certain species and they are likely not being solely driven by changes in gut microbial communities.</p>
FIGURE 4 in Towards sustainable treatments to preserve fossils from weathering, as part of the garden redevelopment project at the Natural History Museum
FIGURE 4. Graph showing the cumulative weight gain on tape peels, which equate to cumulative loss of surface material.
FIGURE 2 in Towards sustainable treatments to preserve fossils from weathering, as part of the garden redevelopment project at the Natural History Museum
FIGURE 2. One of the ornithopod footprints from the Purbeck Group Durlston Formation, Stair Hole Member, with yellow outline showing approximate shape of impression.
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.