Find research datasets worth reusing

opennotspecifiedJul 2022View details →

Fig. 1 in Fluctuating asymmetry and oxidative stress indicate environmental stress of Cane toads Rhinella marina

Fig. 1. Traits measured for fluctuating asymmetry assessment of the toad R. marina. Radius-Ulna (RU), Humerus (Hu), Femur (F), Tibia-Fibula (TF), Tarsal, Metatarsal, Phalange (TMPh 1,2,3,4,5), Abdomen (Ab), Head (He). We measured the distance from the right side (Rw) and left side (Lw).

Figure S1: Nutrient concentration of lettuce (Lactuca sativa 'Rex') plants grown at different total incident light levels in deep water culture hydroponics. Lines show multiple regression analysis results, indicating no significant interactions. Each data point represents one plant. N = nitrogen, P = phosphorus, K = potassium, Ca = calcium, Mg = magnesium, S = sulfur, B = boron, Cu = copper, Fe = iron, Mn = manganese, and Zn = zinc.

Open the record for dataset details and reuse information.

Detection of allophane by Zhurong rover indicative of limited water-alteration at Utopia Planitia of Mars

Tianwen-1 mission is China’s first Martian exploration mission, and the Zhurong rover has successfully landed in southern Utopia Planitia. The MarSCoDe onboard the Zhurong rover can obtain mineralogical information on the Martian surface materials by using SWIR spectra. However, the exact types of hydrated phases are not clear in the existing literature. Thus we combined the SWIR spectra and laboratory database of binary mixtures to further constrain mineral types and their contents. Our results reveal that allophane may be the source of hydration features in the SWIR spectra of the Tianwen-1 landing site. This is of great significance to the geological interpretation and process of the landing site.

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor (2001): BSF, minNDTI, and NOS

<h2>Data information</h2> This dataset provides annual aggregated predictors BSF, minNDTI, and NOS for the year 2001. They are calculated as follows: <ul> <li>minNDTI (minimum NDTI) is determined as the minimum value of the six NDTI values over a year (<a href="https://www.sciencedirect.com/science/article/pii/S0034425711003439?casa_token=5P1XeaRMuT4AAAAA:8VyzOkBJb_gbpap0uzjuT6YoYhylvqc_vY2-AOX2KCsZGrwQw1jySGd4Ox9JFz55TPQmF231IQ">Zheng et al., 2012</a>).</li> <li>BSF (bare soil fraction) is calculated by dividing the number of pixels classified as bare surface within a year's time series (identified by the criterion of NDVI values below 0.35) by the total number of pixels analyzed in that year (<a href="https://www.mdpi.com/2072-4292/11/18/2121">Castaldi et al., 2019</a>).</li> <li>NOS (number of seasons) indicates the frequency of cropping cycles within a year, calculated by counting NDVI peaks throughout the year (<a href="https://www.mdpi.com/2072-4292/6/3/2473">Li et al., 2014</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0034425720304685?casa_token=u4YFBsBotd4AAAAA:TgMiw5E7HNA9-oSF-oszmaRLzLPQJJmV-I13lvdkog1ibfEIZoPxJ3jA4F8Jcu5eXpzVBOyjew">Liu et al., 2020</a>). </li> </ul> <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor (2003): BSF, minNDTI, and NOS

<h2>Data information</h2> This dataset provides annual aggregated predictors BSF, minNDTI, and NOS for the year 2003. They are calculated as follows: <ul> <li>minNDTI (minimum NDTI) is determined as the minimum value of the six NDTI values over a year (<a href="https://www.sciencedirect.com/science/article/pii/S0034425711003439?casa_token=5P1XeaRMuT4AAAAA:8VyzOkBJb_gbpap0uzjuT6YoYhylvqc_vY2-AOX2KCsZGrwQw1jySGd4Ox9JFz55TPQmF231IQ">Zheng et al., 2012</a>).</li> <li>BSF (bare soil fraction) is calculated by dividing the number of pixels classified as bare surface within a year's time series (identified by the criterion of NDVI values below 0.35) by the total number of pixels analyzed in that year (<a href="https://www.mdpi.com/2072-4292/11/18/2121">Castaldi et al., 2019</a>).</li> <li>NOS (number of seasons) indicates the frequency of cropping cycles within a year, calculated by counting NDVI peaks throughout the year (<a href="https://www.mdpi.com/2072-4292/6/3/2473">Li et al., 2014</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0034425720304685?casa_token=u4YFBsBotd4AAAAA:TgMiw5E7HNA9-oSF-oszmaRLzLPQJJmV-I13lvdkog1ibfEIZoPxJ3jA4F8Jcu5eXpzVBOyjew">Liu et al., 2020</a>). </li> </ul> <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

opencc-zeroMar 2024View details →

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P75 (2015): Reflectances bands, NDVI and NDWI

<h2>Data information</h2> This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2015. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI. <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Mesh used for Robust Discontinuity Indicators workflows

Mesh used in the paper "Robust Discontinuity Indicators for High-Order Reconstruction of Piecewise Smooth Functions." We have three types of spherical meshes: cubed-sphere (CS), quasi-uniform Voronoi (ICOD), and regionally refined (RRM) grids of the CS and ICOD types. They are NetCDF (.nc) meshes. Spherical meshes have different levels, from coarse to fine. All spherical meshes do not contain any function information. We also uploaded the general surfaces of the plane, cylinder, and reamer bit. They are VTK meshes. The grids in the general surfaces are the same, but they contain different function values and generated RDI information.

Fig. 4 in Comparative Phylogeography in the Taiwan-Luzon Volcanic Belt Indicates Fast Diversification History of Pachyrhynchus Weevils (Coleoptera: Curculionidae)

Fig. 4. Hierarchical Bayesian clustering analyses of three Pachyrhynchus weevils implemented in STRUCTURE under admixture model: (A) P. sarcitis, optimal K = 2 in subset i, ii, iv, v, vi and optimal K = 3 in subset iii; (B) P. nobilis, optimal K = 2 in each subset; (C) P. semperi, optimal K = 2 in subset i, and optimal K = 3 in subset ii. Barplots represent the probability that individual assigned to the genetic cluster.

Fig. 5 in Comparative Phylogeography in the Taiwan-Luzon Volcanic Belt Indicates Fast Diversification History of Pachyrhynchus Weevils (Coleoptera: Curculionidae)

Fig. 5. The best-fit demographic models of (A) Pachyrhynchus sarcitis, (B) P. nobilis and (C) P. semperi inferred from G-PhoCS.Topological assumption based on SNAPP population tree, and the following demographic parameters were estimated according to per-generation mutation rate μ = 2.9 × 10 − 9 and generation time g = 3. The width of the column represents the mean of effective population size (Ne), and the dashed line indicates divergence time (T: Thousand year). Migration rate (mtot) is not demonstrated because all value of mtot is not significant.

Fig. 6 in Comparative Phylogeography in the Taiwan-Luzon Volcanic Belt Indicates Fast Diversification History of Pachyrhynchus Weevils (Coleoptera: Curculionidae)

Fig. 6. Density plot of genealogical divergence index (gdi) of Pachyrhynchus sarcitis (A), P. nobilis (B), and P. semperi (C). The value of gdi> 0.7 supports as a distinct species, 0.2 <gdi <0.7 represents ambiguous species status and gdi <0.2 means populations belonged to the same species.The letters represent populations in the SNAPP trees (Fig. 3), and the detail information of gdi value of each island population is showed inTable 6.

Fig. 2 in Comparative Phylogeography in the Taiwan-Luzon Volcanic Belt Indicates Fast Diversification History of Pachyrhynchus Weevils (Coleoptera: Curculionidae)

Fig. 2. Concatenated SNPs trees of (A) Pachyrhynchus sarcitis, (B) P. nobilis, and (C) P. semperi constructed by GTR model in RAxML using concatenated SNPs data.The numbers on the branches indicate the bootstrap supports. Colorful boxes highlight populations from the same island group, and colorful bars represent each island population. Black dots denote the monophyletic lineage that are grouped by an island, and red dots denote the monophyletic lineage that are grouped by more than one island.

Fig. 3 in Comparative Phylogeography in the Taiwan-Luzon Volcanic Belt Indicates Fast Diversification History of Pachyrhynchus Weevils (Coleoptera: Curculionidae)

Fig. 3. Population trees of (A) Pachyrhynchus sarcitis, (B) P. nobilis, and (C) P. semperi inferred by Bayesian coalescent model in SNAPP, and probability distribution of all sampled tree was visualized by DensiTree. The number on the branch indicates the Bayesian posterior probability. Colorful boxes highlight the island populations from the same island group.The letters A to H were used in G-PhoCS and BPP analyses.

Fig. 1 in Comparative Phylogeography in the Taiwan-Luzon Volcanic Belt Indicates Fast Diversification History of Pachyrhynchus Weevils (Coleoptera: Curculionidae)

Fig. 1. Geographic distribution of Pachyrhynchus sarcitis, P. nobilis, and P. semperi in the Taiwan–Luzon volcanic belt.The three island groups are marked with different color series (green, blue, and brown), and each color on island corresponds to different populations in the analyses.

opencc-by-4.0Jun 2024View details →

DMI Klimaatlas v2024b - Fremskrivninger af det danske klima (Projections of climate indicators in Denmark)

See below for English version  --- DMI Klimaatlas v2024b - Fremskrivninger af det danske klima Klimaatlas leverer ét samlet datagrundlag for det fremtidige danske klima. Klimaatlas er udarbejdet på baggrund af DMI's egne data, internationale samarbejder og viden fra rapporter fra FN’s Klimapanel (IPCC). Finansieringen kommer fra Finansloven 2018 og 2022. Klimaindikatorer er udregnet og samlet for hele Danmark, kommuner, alle vandoplande (afvandingsområder) og kyststrækninger, i et højtopløsningsgitter (1x1 km). Denne opdatering indebærer: <ul> <li>Alle havniveau og stormflodsindikatorer er opdateret på baggrund af Kystdirektoratets nyeste opdatering af ”Højvandsstatistikkerne” fra Juli 2024 og rediveret 5 november 2024. </li> <li>Tilføjelse af to nye SSP-udledningsscenarier SSP1-1.9 og SSP3-7.0 for havniveau og stormflodsindikatorer.</li> <li>Ændring i indikatorerne: <ul> <li>Tilføjelse 1 indikator <ul> <li>Hyppighed af nuværende 100 årshændelse</li> </ul> </li> <li>Fjernelse 3 indikatorer <ul> <li>Samlet varighed af vandstandsvarslinger</li> <li>Hyppighed af vandstandsvarslinger</li> <li>10.000 årshændelse stormflod</li> </ul> </li> <li>Alle andre indikatorer fra tidligere udgaver findes også i dette datasæt.</li> </ul> </li> <li>Referenceåret for vandstand er flyttet fra 1990 til 1995, der giver en mindre afvigelse på tværs af alle hav målestationer på cirka 2 cm.</li> <li>Tilføjelse af to nye klimamodel datasæt for maksimum og minimum dagstemperatur. Alle relevante indikatorer er blevet genberegnet med den udvidet ensemble.</li> </ul> Datasættet indeholder de følgende elementer: <ul> <li>DMI_Klimaatlas_v2024b_Danmark_rapport.pdf – Klimaatlas rapport som giver et overblik over klimaforandring i Danmark.</li> <li>DMI_Klimaatlas_v2024b_Alle_indikator.xlsx – Microsoft Excel regneark med alle Klimaatlas indikatorer.</li> <li>DMI_Klimaatlas_v2024b_Excel_regnearker.zip - Microsoft Excel-regneark med indikatorer opdelt efter kommuner, vandopleande eller kystrækninger.</li> <li>DMI_Klimaatlas_v2024b_Kommune_rapporter.zip - PDF rapporter om klimaforandringer i alle 98 kommuner i Danmark.</li> <li>DMI_Klimaatlas_v2024b_NetCDF_indicators.zip – Indikatorer på en 1km gitter over Danmark i NetCDF format.</li> <li>DMI_Klimaatlas_v2024b_Udvidet_havniveau.xlsx – Udvidet havniveau datasæt til ekspertbrugere som har behov for en højere tidsopløsning, længere tidsdækning og eller andre scenarier for havniveaustigning.</li> <li>DMI_Report_24_12.pdf - " Methods used in Klimaatlas, the Danish Climate Atlas (v2024b) ", tekniske rapport som beskriver hvordan Klimaatlas data beregnes (på engelsk).</li> </ul> ---- DMI Klimaatlas v2024b - Projections of climate indicators in Denmark DMI’s Klimaatlas provides data for the future Danish climate. Klimaatlas is based on DMI's own data, international collaborations and knowledge from reports by the Intergovernmental Panel on Climate Change (IPCC). Funding comes from the Danish Finance Act 2018 and 2022. Climate indicators are calculated and compiled for all of Denmark, municipalities, all water basins (catchment areas) and coastlines, on a high-resolution grid (1x1 km). This update includes: <ul> <li>All sea level and storm-surge indicators have been updated in line with the latest version of the Danish Coastal Authority’s  “Højvandsstatistikkerne” from July 2024, revised 5th November 2024.</li> <li>Addition of two new SSP emissions scenarios, SSP1-1.9 and SSP3-7.0, for sea level and storm surge indicators.</li> <li>Changes in indicators: <ul> <li>Addition of 1 indicator <ul> <li>Frequency of current 100-year storm surge event</li> </ul> </li> <li>Removal of 3 indicators <ul> <li>Total duration of high-water warnings</li> <li>Frequency of high-water warnings</li> <li>Height of a 10 000 year storm surge event</li> </ul> </li> <li>All other indicators from previous versions can also be found in this dataset.</li> </ul> </li> <li>The reference year for sea level is moved from 1990 to 1995, giving a minor shift across all stations of around 2cm.</li> <li>Addition of two new climate models to the dataset for the maximum and minimum daily temperatures. All relevant indicators have been recalculated with the expanded ensemble.</li> </ul> The dataset consists of the following elements: <ul> <li>DMI_Klimaatlas_v2024b_Danmark_rapport.pdf – Klimaatlas report describing the effects of climate change in Denmark (in Danish)</li> <li>DMI_Klimaatlas_v2024b_Alle_indikator.xlsx – Microsoft Excel spreadsheet with all Klimaatlas indicators (in Danish).</li> <li>DMI_Klimaatlas_v2024b_ Excel_regnearker.zip - Microsoft Excel spreadsheet with indicators divided by municipalities ("Kommune"), coastal stretches ("Kyststrækninger") and drainages ("Vandoplande") (in Danish).</li> <li>DMI_Klimaatlas_v2024b_Kommune_rapporter.zip - PDF report summarising the findings of Klimaatlas for each of the 98 municipalities in Denmark (in Danish).</li> <li>DMI_Klimaatlas_v2024b_NetCDF_indicators.zip – Indicators on a 1km grid over Denmark in the NetCDF format (in Danish).</li> <li>DMI_Klimaatlas_v2024b_Udvidet_havniveau.xlsx – Extended sea level rise dataset for expert uses that have a need for a higher time resolution, longer time coverage or other climate scenarios for sea level rise (in Danish).</li> <li>DMI_Report_24_12.pdf - " Methods used in Klimaatlas, the Danish Climate Atlas (v2024b)", technical report describing the methods used in generating Klimaatlas data</li> </ul>

opencc-by-4.0Nov 2024View details →

Datasets and relevant code in the Manuscript "Estimation of fire counts and fire radiative power using satellite optical and microwave vegetation indices with random forest method"

1. multiyears_season_fire_ndvi_fwi_edvi_0.25.mat Multiyear averages of ln (FC), ln (FRP), DMC, ISI, EDVI10-18, EDVI18-36, and NDVI over East Asia in 2003–2010 2. RF_edvi_data.mat Estimated FC and FRP based on RF model with EDVIs and NDVI  3.RF_fwis_data.mat Estimated FC and FRP based on RF model without EDVIs and NDVI  4. temporal_variations.mat East Asia Regional Time Series Dataset 5.rf_train_cv_forest_review.py Random forest model python code

SHOWCASE Task 2.7 Biodiversity Indicators maps for the Hungarian case study area (Kiskunság)

The data set for the SHOWCASE Task 2.7 Hungarioan case study area of  Kiskunság encompasses 48 raster maps at 10 m resolution (Reference system EPSG:3035) for the following 0-1 normalised biodiversity indicators: <ol> <li>Wild bees abundance  (WBA)</li> <li>Wild bees species richness (WBR)</li> <li>Spider abundance (SpA)</li> <li>Spider species richness (SpR)</li> <li>Vascular plants species richness (PlaR)</li> <li>Composite Biodiversity Indicator (BioDiv: 0-1 normalised sum of the indicators 1-5)</li> </ol> Each indicator was predicted and mapped for two seasons (r1 and r2) in two years (2022 and 2023) under two management scenarios (control "treatmentiszero", and intervention "treatmentisone"). Target land use: arable land.

SHOWCASE Task 2.7 Biodiversity Indicator maps for the Portuguese case study area (Evora, Portel, Cuba, Vidigueira and Beja)

The data set for the SHOWCASE Task 2.7 Portuguese case study area of  the Municipalities of Evora, Portel, Cuba, Vidigueira and Beja encompasses 48 raster maps (GEOTIF format) at 10 m resolution (Reference system EPSG:3035) for the following 0-1 normalised biodiversity indicators: <ol> <li>Wild bees abundance  (WBA)</li> <li>Wild bees species richness (WBR)</li> <li>Spider abundance (SpA)</li> <li>Spider species richness (SpR)</li> <li>Vascular plants species richness (PlaR)</li> <li>Composite Biodiversity Indicator (BioDiv: 0-1 normalised sum of the indicators 1-5)</li> </ol> Each indicator was predicted and mapped for two seasons (r1 and r2) in two years (2022 and 2023) under two management scenarios (control "treatmentiszero", and intervention "treatmentisone"). Target land use: permanent crops (olive orchards).

SHOWCASE Task 2.7 Biodiversity Indicator maps for the Spanish case study area (Guadalquivida)

The data set for the SHOWCASE Task 2.7 Spanish case study area of Guadalquivida encompasses 48 raster maps (GEOTIF format) at 10 m resolution (Reference system EPSG:3035) for the following 0-1 normalised biodiversity indicators: <ol> <li>Wild bees abundance  (WBA)</li> <li>Wild bees species richness (WBR)</li> <li>Spider abundance (SpA)</li> <li>Spider species richness (SpR)</li> <li>Vascular plants species richness (PlaR)</li> <li>Composite Biodiversity Indicator (BioDiv: 0-1 normalised sum of the indicators 1-5)</li> </ol> Each indicator was predicted and mapped for two seasons (r1 and r2) in two years (2022 and 2023) under two management scenarios (control "treatmentiszero", and intervention "treatmentisone"). Target land use: permane crops (stone fruit orchards)

SHOWCASE Task 2.7 Biodiversity Indicator maps for the Swiss case study area (Solothurn)

The data set for the SHOWCASE Task 2.7 Swiss case study area of Solothurn encompasses 48 raster maps (GEOTIF format) at 10 m resolution (Reference system EPSG:3035) for the following 0-1 normalised biodiversity indicators: <ol> <li>Wild bees abundance  (WBA)</li> <li>Wild bees species richness (WBR)</li> <li>Spider abundance (SpA)</li> <li>Spider species richness (SpR)</li> <li>Vascular plants species richness (PlaR)</li> <li>Composite Biodiversity Indicator (BioDiv: 0-1 normalised sum of the indicators 1-5)</li> </ol> Each indicator was predicted and mapped for two seasons (r1 and r2) in two years (2022 and 2023) under two management scenarios (control "treatmentiszero", and intervention "treatmentisone"). Target land use: arable land.

SHOWCASE Task 2.7 Biodiversity Indicator maps for the Dutch case study area (Zuid-Limburg)

The data set  for the SHOWCASE Task 2.7 Dutch case study area of Zuid Limburg encompasses 48 raster maps (GEOTIF format) at 10 m resolution (Reference system EPSG:3035) for the following 0-1 normalised biodiversity indicators: <ol> <li>Wild bees abundance  (WBA)</li> <li>Wild bees species richness (WBR)</li> <li>Spider abundance (SpA)</li> <li>Spider species richness (SpR)</li> <li>Vascular plants species richness (PlaR)</li> <li>Composite Biodiversity Indicator (BioDiv: 0-1 normalised sum of the indicators 1-5)</li> </ol> Each indicator was predicted and mapped for two seasons (r1 and r2) in two years (2022 and 2023) under two management scenarios (control "treatmentiszero", and intervention "treatmentisone"). Target land use: arable land