Find research datasets worth reusing

opencc-by-4.0Jan 2022View details →

GPS time series of solid Earth deformation for the southern Antarctic Peninsula

This dataset contains vertical GPS time series observed from selected sites in the southern Antarctic Peninsula. The time series were processed using GAMIT-GLOBK software (Herring et al., 2016) in combination with a globally distributed network that includes all available data from the International GNSS Service (IGS), US POLENET-ANET and UKANET networks. The provided archive contains raw and corrected time series for the effect of elastic deformation-induced from the RACMO surface mass balance (SMB) model with a 5.5 km horizontal resolution (van Wessem et al. 2016). This data is used in the manuscript "GPS-observed elastic deformation due to surface mass balance variability in the Southern Antarctic Peninsula" to study how modelled elastic deformation due to SMB variation can explain vertical land motion observed derived from GPS signals.

opencc-by-4.0Feb 2022View details →

TimeSpec4LULC: A Smart-Global Dataset of Multi-Spectral Time Series of MODIS Terra-Aqua from 2000 to 2021 for Training Machine Learning models to perform LULC Mapping

TimeSpec4LULC is a smart open-source global dataset of multi-spectral time series for 29 Land Use and Land Cover (LULC) classes ready to train machine learning models. It was built based on the seven spectral bands of the MODIS sensors at 500 m resolution from 2000 to 2021 (262 observations in each time series). Then, was annotated using spatial-temporal agreement across the 15 global LULC products available in Google Earth Engine (GEE). TimeSpec4LULC contains two datasets: the original dataset distributed over 6,076,531 pixels, and the balanced subset of the original dataset distributed over 29000 pixels. The original dataset contains 30 folders, namely "Metadata", and 29 folders corresponding to the 29 LULC classes. The folder "Metadata" holds 29 different CSV files describing the metadata of the 29 LULC classes. The remaining 29 folders contain the time series data for the 29 LULC classes. Each folder holds 262 CSV files corresponding to the 262 months. Inside each CSV file, we provide the seven values of the spectral bands as well as the coordinates for all the LULC class-related pixels. The balanced subset of the original dataset contains the metadata and the time series data for 1000 pixels per class representative of the globe. It holds 29 different JSON files following the names of the 29 LULC classes. The features of the dataset are: - ".geo": the geometry and coordinates (longitude and latitude) of the pixel center. - "ADM0_Code": the GAUL country code. - "ADM1_Code": the GAUL first-level administrative unit code. - GHM_Index": the average of the global human modification index. - "Products_Agreement_Percentage": the agreement percentage over the 15 global LULC products available in GEE. - "Temporal_Availability_Percentage": the percentage of non-missing values in each band. - "Pixel_TS": the time series values of the seven spectral bands.

FIG. 3 in Taxonomy of Mexican and Central American Members of Solanum Series Conicibaccata (sect. Petota)

FIG. 3. Distribution of numbers of lateral and interjected leaflets in Mexican and Central American members of Solanum series Conicibaccata.

FIG. 5 in Taxonomy of Mexican and Central American Members of Solanum Series Conicibaccata (sect. Petota)

FIG. 5. Neighbor-joining tree (Jaccard's coefficient) based on 64 polymorphic RAPD fragments of the Mexican and Central American species of Solanum series Conicibaccata and comparison species. The numbers after the species correspond to those in Table 2.

FIG. 4 in Taxonomy of Mexican and Central American Members of Solanum Series Conicibaccata (sect. Petota)

FIG. 4. Variation in leaves of all four species of Mexican and Central American representatives of Solanum series Conicibaccata.

FIG. 1 in Taxonomy of Mexican and Central American Members of Solanum Series Conicibaccata (sect. Petota)

FIG. 1. Alternative classifications of the Mexican and Central American species of Solanum series Conicibaccata.

opencc-by-4.0Feb 2022View details →

Virtual stations (TeroVIR ) and water level time series (TeroWAT) in West Africa and Arctic regions

The dataset contains a sample of locations across Siberia and Africa, for which water-level time series were automatically derived from Sentinel-3 altimeters (methodology described in Machefer et al. 20221) from year 2016 to year 2021, together with the in-situ station records and the area covered by the altimetry measurements. The purpose of this dataset is validation and exemplification of the methodology.  The methodology described produces comprehensive water level records at a global scale based on altimetry satellite data. The validation against in-situ data was assessed in numerous environments in West Africa and complex locations such as Arctic rivers partially covered with ice. This dataset offers a sample of the records at 3 locations in West Africa (Kemacina [Mali], Koulikouro [Mali], Lokoja [Niger]) and in the sub-arctic region (Yakutsk [Russia]). The data are organised by Level 1 of <a href="http://www.hydrosheds.org/">HydroBASINS</a>2 definition (ex: africa) in two folders, each containing: virtual stations (teroVIR) and insitu stations (insitu) as shapefiles with their associated metadata, the corresponding water level time series (teroWAT) in NetCDF, and the level 3 of HydroBASINS, corresponding to the largest river basins of each continent. Finally, a csv file (validation) presents the computed metrics assessing the accuracy of the processors. N.B.: time series with less than two common date points between insitu and teroWAT have not been assessed.  [1] Machefer, M., Perpinyà-Vallès M., Escorihuela M.J., Gustafsson D., Romero L. (2022): Challenges and evolution of water level monitoring towards a comprehensive, world-scale coverage with remote sensing. Earth System Science Data (Under Reviewing) [2] Lehner, B., Grill G. (2013): Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrological Processes, 27(15): 2171–2186. Data is available at www.hydrosheds.org.

dryad40/100

Characterizing North American Ipomoea grandifolia (Convolvulaceae), a member of Ipomoea series batatas

Species in the genus Ipomoea are often difficult to identify due to their similar morphologies and their ability to hybridize with one another. An undescribed North American Ipomoea morphotype in series Batatas, referred here as Ipomoea Carolina morphotype, was found to be morphologically, genetically, and reproductively isolated from other locally co-occurring Ipomoea species. A previous phylogenetic analysis that included a broader sampling of species in Ipomoea series Batatas suggested that Ipomoea Carolina morphotype may be Ipomoea grandifolia, a species described as found only in South America. To evaluate these findings, we tested intrinsic cross-compatibility between Ipomoea Carolina morphotype and I. grandifolia as well as with three other co-localizing North American Ipomoea species – Ipomoea cordatotriloba, Ipomoea lacunosa, and Ipomoea leucantha. We also examined genetic differentiation using single nucleotide polymorphisms from leaf transcriptomes from multiple individuals of all five species and several outgroup species. We find no cross-incompatibility and little genetic differentiation between Ipomoea Carolina morphotype and Ipomoea grandifolia, suggesting that Ipomoea Carolina morphotype is a representative of Ipomoea grandifolia. This finding raises additional questions about the origins of Ipomoea grandifolia in North America and how its disjunct distribution could play a role in the divergence of Ipomoea grandifolia in the future.

opencc-zeroMar 2022View details →

Dataset and R code: Effects of temperature and air pollution on emergency ambulance dispatches: a time series analysis in a medium-sized city in Germany

Dataset and R script to replicate results in the manuscript "Effects of temperature and air pollution on emergency ambulance dispatches: a time series analysis in a medium-sized city in Germany", currently under review.

Series y películas de TV y plataformas de streaming españolas lanzadas entre el año 2000 y 2022, valoradas por IMBD

Este dataset recoge las valoraciones en IMDB, a fecha de publicación (11/04/2022), de las películas y series españolas emitidas desde el año 2000.

Traveling Waves: The Indonesian Tsunami Warning System - STS Webinar Series #1

Previously published on YouTube: https://www.youtube.com/watch?v=CWK1_Nxi__Y (Indonesian) Seri webinar ini diadakan oleh Pusat Riset Kewilayahan (PRW) BRIN untuk mendiskusikan tema-tema terkait Science, Technology, and Society Studies, salah satu program kajian yang dikembangkan di PRW-BRIN. Webinar ini membahas kelindan antara diskursus, aktor, mesin deteksi, serta praktik sains dan teknologi untuk menghadapi risiko dan ketidakpastian sistem pengetahuan peringatan dini tsunami di Indonesia. Dengan menggunakan pendekatan etnografis, diskusi ini akan berusaha menjawab pertanyaan terkait teknologi sistem peringatan dini tsunami Indonesia sebagai infrastruktur sosial dan jaringan konektivitas pakar Jerman - Indonesia dalam pengembangan sistem teknologi tersebut di Indonesia. (English) This webinar series was held by the BRIN Regional Research Center (PRW) to discuss themes related to Science, Technology, and Society Studies, one of the study programs developed at PRW-BRIN. This webinar discusses the interplay between discourse, actors, detection machines, as well as science and technology practices to deal with the risks and uncertainties of the tsunami early warning knowledge system in Indonesia. Using an ethnographic approach, this discussion will attempt to answer questions related to the technology of the Indonesian tsunami early warning system as a social infrastructure and connectivity network of German-Indonesian experts in developing the technology system in Indonesia.

European Stillbirth Rate Time Series Dataset

This dataset contains mostly annual time series of stillbirth rates from the mid-eighteenth century to the present for six countries including previously unpublished data for Denmark and for the Dutch region of Zeeland. There are several sheets within the Excel file, one of which explains the source of each series, potential stillbirth registration problems for each series and articles or book chapters with further information. This data was compiled by Eric Schneider from disparate sources and would not be possible without contributions from Anne Løkke (University of Copenhagen) and Frans van Poppel (NIDI).

opencc-by-4.0May 2022View details →

A tempοral Deep Convolutional Neural Network model on Sentinel-1 Image Time Series for pixel-wise Flood Classification (dataset)

This is a dataset which has been designed to be used for flood time series classification. Each time series is annotated as flood or no-flood and represents a pixel-wise time series derived from stack of Sentinel-1 IW GRD images that have been pre-processed according to <a href="http://doi.org/10.5281/zenodo.6510223">https://doi.org/10.5281/zenodo.6510223</a>.

opencc-by-4.0Jun 2022View details →

Assessing land surface phenology in Araucaria-Nothofagus forests in Chile with Landsat 8/Sentinel-2 time series - Data and Material

This dataset contains the Enhanced Vegetation Index (EVI) data used in our research work about land surface phenology of Andean Araucaria-Nothofagus forests as well as the phenology information derived from it. Study area: Conguillío National Park, Chile Study period: 2016-2020 Description of datasets: conguillio.sen2.lnd8.evi.2016.2020.nc - A raster dataset (NetCDF) of EVI values (resolution 10m). EVI was calculated from Level-2 Sentinel-2 and Landsat 8 data. To ensure harmonization, the Landsat 8 data was resampled and reprojected to Sentinel-2 properties prior to the index calculation. evi_gb_beck_white.tif - A raster dataset (GeoTiff) of phenological metrics per year (resolution 10m). Metrics were derived by fitting a double logistic function (see Beck et al., 2006) to the smoothed and interpolated EVI pixel time series. Subsequently, the main phenological variables SOS (start of season) and EOS (end of season) were extracted using a 50% threshold value. The dataset itself is a result of the R package "greenbrown" and the layers are named accordingly (see https://greenbrown.r-forge.r-project.org/phenology.php). It is available as GeoTIFF and as R rasterfile. Details about the methodology and results describing this dataset can be found in the following publication: Kosczor, E., Forkel, M., Hernández, J., Kinalczyk, D., Pirotti, F. & Kutchartt, E., 2022. Assessing land surface phenology in Araucaria-Nothofagus forests in Chile with Landsat 8/Sentinel-2 time series. Int. J. Appl. Earth Obs. Geoinf. 112, 102862. https://doi.org/10.1016/j.jag.2022.102862

opencc-by-4.0Sep 2021View details →

Data associated with the manuscript "Simple statistical models can be sufficient for testing hypotheses with population time series data"

This is a revised version of the archive of R code and data used in the manuscript, Simple statistical models can be sufficient for testing hypotheses with population time series data. The data are in three files. etodata1.csv and etodata2.csv contain two versions of the same data for shoal-dwelling fishes in the Etowah River and associated environmental covariates. knz_dat contains data for small mammals collected in the Konza Prairie Biological Station and associated environmental covariates. The R code consists of four primary files that call nine auxiliary files. CaseStudy1-main_code and CaseStudy2-main_code are the primary files for running the two case studies. Simulations1 and Simulations2 are the files for running the two batteries of simulations. We thank the Konza Prairie Biological Station and Konza Prairie Long-Term Ecological Research Program supported by the National Science Foundation (DEB-1440484) for collecting and providing access to mammal community data. More details are in the manuscript and supporting information.

opencc-by-4.0Jul 2022View details →

Outputs of the Jupyter Notebook - Concatenating a gridded rainfall reanalysis dataset into a time series

The dataset contains the outputs of the notebook "Concatenating a gridded rainfall reanalysis dataset into a time series" published in The Environmental Data Science Book. Contributions Notebook <ul> <li> Timothy Lam (author), University of Exeter, <a href="https://github.com/timo0thy">@timo0thy</a> </li> <li> Marlene Kretschmer (author), University of Reading, <a href="https://github.com/MarleneKretschmer">@MarleneKretschmer</a> </li> <li> Samantha Adams (author), Met Office Informatics Lab, <a href="https://github.com/svadams">@svadams</a> </li> <li> Rachel Prudden (author), Met Office Informatics Lab, <a href="https://github.com/RPrudden">@RPrudden</a> </li> <li> Elena Saggioro (author), University of Reading, <a href="https://github.com/ESaggioro">@ESaggioro</a> </li> <li> Nick Homer (reviewer), University of Edinburgh, <a href="https://github.com/NHomer">@NHomer</a> </li> <li> Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a> </li> </ul> Dataset originator/creator <ul> <li> NOAA National Center for Environmental Prediction (creator) </li> </ul> Dataset authors <ul> <li> Eugenia Kalnay, Director, NCEP Environmental Modeling Center </li> </ul> Dataset documentation <ul> <li> E. Kalnay, M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S. Saha, G. White, J. Woollen, Y. Zhu, M. Chelliah, W. Ebisuzaki, W. Higgins, J. Janowiak, K. C. Mo, C. Ropelewski, J. Wang, A. Leetmaa, R. Reynolds, Roy Jenne, and Dennis Joseph. The ncep/ncar 40-year reanalysis project. Bulletin of the American Meteorological Society, 77(3):437 – 472, 1996. URL: <a href="https://journals.ametsoc.org/view/journals/bams/77/3/1520-0477_1996_077_0437_tnyrp_2_0_co_2.xml">https://journals.ametsoc.org/view/journals/bams/77/3/1520-0477_1996_077_0437_tnyrp_2_0_co_2.xml</a>, <a href="https://doi.org/10.1175/1520-0477(1996)077%3C0437:TNYRP%3E2.0.CO;2">doi:10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2</a>. </li> </ul> Pipeline documentation <ul> <li> Marlene Kretschmer, Samantha V. Adams, Alberto Arribas, Rachel Prudden, Niall Robinson, Elena Saggioro, and Theodore G. Shepherd. Quantifying causal pathways of teleconnections. Bulletin of the American Meteorological Society, 102(12):E2247 – E2263, 2021. URL: <a href="https://journals.ametsoc.org/view/journals/bams/102/12/BAMS-D-20-0117.1.xml">https://journals.ametsoc.org/view/journals/bams/102/12/BAMS-D-20-0117.1.xml</a>, <a href="https://doi.org/10.1175/BAMS-D-20-0117.1">doi:10.1175/BAMS-D-20-0117.1</a>. </li> </ul>

opencc-by-4.0Jul 2022View details →

Code and Data associated with "Discovery of positive and purifying selection in metagenomic time series of hypermutator microbial populations"

Code and data sufficient to reproduce analyses in "Discovery of positive and purifying selection in metagenomic time series of hypermutator microbial populations".

opencc-zeroJun 2022View details →

Ehrhart series coefficients and quasi-period for random rational polytopes

Ehrhart series coefficients and quasi-period for random rational polytopes A dataset of Ehrhart data for 84000 randomly generated rational polytopes, in dimensions 2 to 4, with quasi-periods 2 to 15. The polytopes used to generate this data were produced by the following algorithm: <ol> <li>Fix \(d\) a positive integer in \(\{2,3,4\}\).</li> <li>Choose \(r\in\{2,\ldots,15\}\) uniformly at random.</li> <li>Choose \(d + k\) lattice points \(\{v_1,\ldots,v_{d+k}\}\) uniformly at random in a box \([-5r,5r]^d\), where \(k\) is chosen uniformly at random in \(\{1,\ldots,5\}\).</li> <li>Set \(P := \mathrm{conv}\{v_1,\ldots,v_{d+k}\}\). If \(\mathrm{dim}(P)\) is not equal to \(d\) then return to step 3.</li> <li>Choose a lattice point \(v\in P \cap \mathbb{Z}^d\) uniformly at random and replace \(P\) with the translation \(P-v\).</li> <li>Replace \(P\) with the dilation \(P/r\).</li> </ol> The final dataset was produced by first removing duplicate records, and then downsampling to a subset with 2000 datapoints for each pair \((d,q)\), where \(d\) is the dimension of \(P\) and \(q\) is the quasi-period of \(P\), with \(d\in\{2,3,4\}\) and \(q\in\{2,\ldots,15\}\). For details, see the paper:  Machine Learning the Dimension of a Polytope, Tom Coates, Johannes Hofscheier, and Alexander M. Kasprzyk, 2022. If you make use of this data, please cite the above paper and the DOI for this data:  doi:10.5281/zenodo.6614829 quasiperiod.txt.gz The file "quasiperiod.txt.gz" is a gzip-compressed plain text file containing key:value records with keys and values as described below, where each record is separated by a blank line. There are 84000 records in the file. Example record ULID: 01G57JBYP2ZW825E0NT4Q9JQNQ Dimension: 2 Quasiperiod: 2 Volume: 97 EhrhartDelta: [1,50,195,289,192,49] Ehrhart: [1,50,198,...] LogEhrhart: [0.000000000000000000000000000000,3.91202300542814605861875078791,5.28826703069453523626966617327,...] (The values for Ehrhart and LogEhrhart in the example have been truncated.) For each polytope \(P\) of dimension \(d\) and quasi-period \(q\) we record the following keys and values in the dataset: ULID: A randomly generated string identifier for this record. Dimension: A positive integer. The dimension \(2 \leq d \leq 4\) of the polytope \(P\). Quasiperiod: A positive integer. The quasi-period \(2 \leq q \leq 15\) of the polytope \(P\). Volume: A positive rational number. The lattice-normalised volume \(\mathrm{Vol}(P)\) of the polytope \(P\). EhrhartDelta: A sequence \([1,a_1,a_2,\ldots,a_N]\) of integers of length \(N + 1\), where \(N := q(d + 1) - 1\). This is the Ehrhart \(\delta\)-vector (or \(h^*\)-vector) of \(P\). The Ehrhart series \(\mathrm{Ehr}(P)\) of \(P\) is given by the power-series expansion of \((1 + a_1t + a_2t^2 + \ldots + a_Nt^N) / (1 - t^q)^{d+1}\). Ehrhart: A sequence \([1,c_1,c_2,\ldots,c_{1100}]\) of positive integers. The value \(c_i\) is equal to the number of lattice points in the \(i\)-th dilation of \(P\), that is, \(c_i = \#(iP \cap \mathbb{Z}^d)\). Equivalently, \(c_i\) is the coefficient of \(t^i\) in \(\mathrm{Ehr}(P) = 1 + c_1t + c_2t^2 + \ldots = (1 + a_1t + a_2t^2 + \ldots + a_Nt^N) / (1 - t^q)^{d+1}\). LogEhrhart: A sequence \([0,y_1,y_2,\ldots,y_{1100}]\) of non-negative floating point numbers. Here \(y_i := \log c_i\).

opencc-zeroJun 2022View details →

Ehrhart series coefficients for random lattice polytopes

Ehrhart series coefficients for random lattice polytopes A dataset of Ehrhart data for 2918 randomly generated lattice polytopes, in dimensions 2 to 8. The polytopes used to generate this data were produced by the following algorithm: <ol> <li>Fix \(d\) a positive integer in \(\{2,\ldots,8\}\).</li> <li>Choose \(d + k\) lattice points \(\{v_1,\ldots,v_{d+k}\}\) uniformly at random in a box \([-5,5]^d\), where \(k\) is chosen uniformly at random in \(\{1,\ldots,5\}\).</li> <li>Set \(P := \mathrm{conv}\{v_1,\ldots,v_{d+k}\}\). If \(\mathrm{dim}(P)\neq d\) then return to step 2.</li> </ol> The final dataset has duplicate records removed. The data is distributed by dimension \(d\) as follows: <table> <tbody> <tr> <th scope="row">d</th> <td>2</td> <td>3</td> <td>4</td> <td>5</td> <td>6</td> <td>7</td> <td>8</td> </tr> <tr> <th scope="row">#</th> <td>431</td> <td>787</td> <td>812</td> <td>399</td> <td>181</td> <td>195</td> <td>113</td> </tr> </tbody> </table> For details, see the paper:  Machine Learning the Dimension of a Polytope, Tom Coates, Johannes Hofscheier, and Alexander M. Kasprzyk, 2022. If you make use of this data, please cite the above paper and the DOI for this data:  doi:10.5281/zenodo.6614821 dimension.txt.gz The file "dimension.txt.gz" is a gzip-compressed plain text file containing key:value records with keys and values as described below, where each record is separated by a blank line. There are 2918 records in the file. Example record ULID: 1FTU9VGPXXU82CTDGD6WYMBF9 Dimension: 3 Volume: 342 EhrhartDelta: [1,70,223,48] Ehrhart: [1,74,513,...] LogEhrhart: [0.000000000000000000000000000000,4.30406509320416975378532779249,6.24027584517076953419476314266,...] (The values for Ehrhart and LogEhrhart in the example have been truncated.) For each polytope \(P\) of dimension \(d\) we record the following keys and values in the dataset: ULID: A randomly generated string identifier for this record. Dimension: A positive integer. The dimension \(2 \leq d \leq 8\) of the polytope \(P\). Volume: A positive integer. The lattice-normalised volume \(\mathrm{Vol}(P)\) of the polytope \(P\). EhrhartDelta: A sequence \([1,a_1,a_2,\ldots,a_d]\) of integers of length \(d + 1\). This is the Ehrhart \(\delta\)-vector (or \(h^*\)-vector) of \(P\). The Ehrhart series \(\mathrm{Ehr}(P)\) of \(P\) is given by the power-series expansion of \((1 + a_1t + a_2t^2 + \ldots + a_dt^d) / (1 - t)^{d+1}\). In particular, \(\mathrm{Vol}(P) = 1 + a_1 + a_2 + \ldots + a_d\). Ehrhart: A sequence \([1,c_1,c_2,\ldots,c_{1100}]\) of positive integers. The value \(c_i\) is equal to the number of lattice points in the \(i\)-th dilation of \(P\), that is, \(c_i = \#(iP \cap \mathbb{Z}^d)\). Equivalently, \(c_i\) is the coefficient of \(t^i\) in \(\mathrm{Ehr}(P) = 1 + c_1t + c_2t^2 + \ldots = (1 + a_1t + a_2t^2 + \ldots + a_dt^d) / (1 - t)^{d+1}\). LogEhrhart: A sequence \([0,y_1,y_2,\ldots,y_{1100}]\) of non-negative floating point numbers. Here \(y_i := \log c_i\)