Find research datasets worth reusing

Dataset with the node discretisations employed for training advection models in "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"

Dataset with the node discretisations employed for training advection models in "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics" (https://doi.org/10.1063/5.0097679). The training code is available at https://github.com/mario-linov/graphs4cfd.

Long-read, chromosome-scale assembly of Vitis rotundifolia cv. Carlos and its unique resistance to Xylella fastidiosa subsp. fastidiosa.

We assembled and annotated a new, long-read genome assembly for ‘Carlos’, a cultivar of muscadine that exhibits tolerance, to build upon the existing genetic resources available for muscadine. We are awaiting release of the genome through NCBI, so we have made the assembly and annotations available here.

Data from: A framework to apply trait-based ecological restoration at large scales

R scripts and data to run the proposed framework at Coutinho, A. G., Carlucci, M. B., Cianciaruso, M. V. (2023) A framework to apply trait-based ecological restoration at large scales. Journal of Applied Ecology.

Large-scale Docking Datasets for Machine Learning

Large-scale virtual screening has become a valuable tool for early-phase drug discovery. Recent expansions of commercial chemical space have made it computationally intractable to evaluate all compounds in the libraries. Machine learning is one of the methods that aim to prioritize specific subsets of these vast libraries. In order to put these methods to the test, access to large-scale datasets is beneficial. To help the community benchmark their work, we share the docking scores of several ultralarge virtual screening campaigns. The datasets we provide contain canonical SMILES, compound identifiers, and docking scores. We docked two different chemical libraries against eight different biological targets with therapeutic relevance. The first dataset contained approximately 15.5 million molecules adhering to the "Rule-of-Four", whereas the second datasets consists of approximately 235 million "lead-like" molecules. The biological targets represent different classes of proteins and binding sites. More details on the datasets and our methods can be found on (<a href="http://github.com/carlssonlab/conformalpredictor">https://github.com/carlssonlab/conformalpredictor</a>) and our pre-print (<a href="https://doi.org/10.26434/chemrxiv-2023-w3x36">https://doi.org/10.26434/chemrxiv-2023-w3x36</a>). Please feel free to download and use these datasets for your own research purposes. We only ask that you cite our pre-print and datasets appropriately if you use it in your work. Thank you for your interest in our research!

Realistic afforestation scenarios in Great Britain at a 1 km scale to run with the land surface model JULES

Afforestation scenarios created in the work of Buechel et al. (XXXX) which cover Great Britain at a 1 km spatial resolution and attempt to represent potential realistic broadleaf afforestation. The datasets represent a 50% and 100% afforestation scenario. The netCDF files are designed so that may be run with the Joint UK Land Environment Simulator (JULES), a community land surface model. The dataset is structured similar to the CHESS-land dataset (Martinez-De La Torre, 2018) where each grid contains information on the fractional coverage of eight different land cover types: Broadleaf woodland, needleleaf woodland, grassland, shrubland, crops, bare soil, urban areas and inland water.    This dataset was created as part of the NERC doctoral training partnerships (grant number NE/L002612/1).   Martinez-de la Torre, A.., Blyth, E.M.. M. and Robinson, E.L.. L. (2018) ‘Water, carbon and energy fluxes simulation for Great Britain using the JULES Land Surface Model and the Climate Hydrology and Ecology research Support System meteorology dataset (1961-2015) [CHESS-land]’. NERC Environmental Information Data Centre. doi:10.5285/c76096d6-45d4-4a69-a310-4c67f8dcf096.

opencc-zeroMay 2023View details →

dryad40/100

Resource quantity and quality differentially control stream invertebrate biodiversity across spatial scales

Resource quantity controls biodiversity across spatial scales, however the importance of resource quality to cross-scale patterns in species richness has seldom been explored. We evaluated the relationship between stream basal resource quantity (periphyton chlorophyll-a) and invertebrate richness and compared this to the relationship of resource quality (periphyton stoichiometry) and richness at local and regional scales across 27 North American streams. At the local scale, invertebrate richness peaked at intermediate levels of chlorophyll-a, but had a shallow negative relationship with periphyton C:P and N:P. However, at the regional scale richness had a strong negative relationship with both chlorophyll-a and periphyton C:P and N:P. The divergent effects of periphyton chl-a and stoichiometry on invertebrate richness suggest that basal resource quantity limits diversity more than resource quality, consistent with patterns of eutrophication. Collectively, we demonstrate that resource quantity and quality play important, yet differing roles in shaping freshwater biodiversity across spatial scale.

Assessment of the acoustic adaptation hypothesis in frogs using large-scale citizen science data

This is the data required to reproduce the results of the manuscript "Assessment of the acoustic adaptation hypothesis in frogs using large-scale citizen science data", including measurements of tree canopy cover extracted from the Global Forest Cover Change dataset (Townshend 2016).   Reference Gillard, G. L. & Rowley, J. J. L. (2023). Assessment of the acoustic adaptation hypothesis in frogs using large-scale citizen science data. Journal of Zoology. [In publication].   Global Forest Cover Change Dataset Townshend J. 2016. Global Forest Cover Change (GFCC) Tree Cover Multi-Year Global 30 m V003 [Data set]. NASA EOSDIS Land Processes DAAC. Accessed June 22, 2022. doi:10.5067/MEaSUREs/GFCC/GFCC30TC.003.Townshend J. 2016. Global Forest Cover Change (GFCC) Tree Cover Multi-Year Global 30 m V003 [Data set]. NASA EOSDIS Land Processes DAAC. Accessed June 22, 2022. doi:10.5067/MEaSUREs/GFCC/GFCC30TC.003.

opencc-by-4.0Sep 2022View details →

Input data, species level results and code accompanying paper: Drivers of the changing abundance of European birds at two spatial scales

This repository contains the input data, species level results and code associated with the paper: Drivers of the changing abundance of European birds at two spatial scales.

Long-term spatial memory, across large spatial scales, in Heliconius butterflies

Data accompanying "Long-term spatial memory, across large spatial scales, in Heliconius butterflies", Current Biology 2023:   exp1.csv. Behavioural data from experiment 1. exp2.csv. Behavioural data from experiment 2. exp3.csv. Behavioural data from experiment 3. Exp1&2.csv. Behavioural data comparing experiment 1 and 2. Exp1byDay.csv. Behavioural data for experiment 1 split by day. Exp2byDay.csv. Behavioural data for experiment 2 split by day. Exp3byDay.csv. Behavioural data for experiment 3 split by day. exp1.R. R code for experiment 1 analysis. exp2.R. R code for experiment 2 analysis. exp3.R. R code for experiment 3 analysis. exp1vsExp2.R. R code for comparing experiment 1 and 2.

INCYDE: A large scale cyclone detection and intensity estimation dataset using satellite infrared imagery

INCYDE (INSAT-based Cyclone Detection and Intensity Estimation) is a cyclone detection and intensity estimation dataset. The cyclone images in the dataset are captured from INSAT 3D/3DR satellites over the Indian Ocean. The proposed INCYDE dataset contains over 100k cyclone images with augmentations taken from cyclones over the Indian Ocean from the year 2013 to 2021. The dataset pertains to two specific tasks: cyclone detection as an object detection task, and intensity estimation as a regression task. In addition to the dataset, this study introduces baseline models that were trained on the newly presented dataset

83 Block--63k or 50k scale Maps of South Asia

118 maps of the Survey of India covering block 83. In the time since this first batch of maps (see the list below) was posted additional maps of Block 83 have become available.  These additional maps are posted on a separate page, and can be reached by clicking the link(s) below.  Included in the final set of additional maps is an Index Map showing the total 63k/50k holdings in Block 83. <table> <tbody> <tr> <td><a href="../records/10460673">63k/50k Maps of South Asia--Block 83--additional 1--20240105</a></td> </tr> </tbody> </table>

78 Block--63k or 50k scale Maps of South Asia

230 maps of the Survey of India covering block 78. In the time since this first batch of maps (see the list below) was posted additional maps of Block 78 have become available.  These additional maps are posted on a separate page, and can be reached by clicking the link(s) below.  Included in the final set of additional maps is an Index Map showing the total 63k/50k holdings in Block 78. <table> <tbody> <tr> <td><a href="../records/10460636">63k/50k Maps of South Asia--Block 78--additional 1--20240105</a></td> </tr> </tbody> </table>

opencc-by-4.0May 2022View details →

Block 64--63k scale--Survey of India Maps

448 maps of the Survey of India covering block 64. In the time since this first batch of maps (see the list below) was posted additional maps of Block 64 have become available.  These additional maps are posted on a separate page, and can be reached by clicking the link(s) below.  Included in the final set of additional maps is an Index Map showing the total 63k/50k holdings in Block 64. <table> <tbody> <tr> <td><a href="../records/10463330">63k/50k Maps of South Asia--Block 64--additional 1--20240105</a></td> </tr> <tr> <td><a href="../records/10463338">63k/50k Maps of South Asia--Block 64--additional 2--20240105</a></td> </tr> </tbody> </table>

opencc-by-4.0May 2022View details →

Block 63--63k scale--Survey of India Maps

416 maps of the Survey of India covering block 63. In the time since this first batch of maps (see the list below) was posted additional maps of Block 63 have become available.  These additional maps are posted on a separate page, and can be reached by clicking the link(s) below.  Included in the final set of additional maps is an Index Map showing the total 63k/50k holdings in Block 63. <table> <tbody> <tr> <td><a href="../records/10460507">63k/50k Maps of South Asia--Block 63--additional 1--20240105</a></td> </tr> <tr> <td><a href="10460513">63k/50k Maps of South Asia--Block 63--additional 2--20240105</a></td> </tr> </tbody> </table>

PubGraph: A Large-Scale Scientific Knowledge Graph

We present PubGraph, a new resource for studying scientific progress that takes the form of a large-scale knowledge graph (KG) with more than 385M entities, 13B main edges, and 1.5B qualifier edges. PubGraph is comprehensive and unifies data from various sources, including Wikidata, OpenAlex, and Semantic Scholar, using the Wikidata ontology. Beyond the metadata available from these sources, PubGraph includes outputs from auxiliary community detection algorithms and large language models. To further support studies on reasoning over scientific networks, we create several large-scale benchmarks extracted from PubGraph for the core task of knowledge graph completion (KGC). These benchmarks present many challenges for knowledge graph embedding models, including an adversarial community-based KGC evaluation setting, zero-shot inductive learning, and large-scale learning. All of the aforementioned resources are accessible at <a href="https://purl.archive.org/pubgraph">https://purl.archive.org/pubgraph</a> and released under the CC-BY-SA license. We plan to update PubGraph quarterly to accommodate the release of new publications.

Increased precipitation over land due to climate feedback of large-scale bioenergy cultivation

Biophysical effects of different bioenergy crop cultivation scenarios on global water cycles simulated by coupled IPSL-CM model, with ORCHIDEE-MICT-BIOENERGY as the land component and LMDz as the atmosphere component. The spatial resolution of the coupled model was 1.26° latitude × 2.5° longitude (i.e., 143*144 grid cells globally). Datasets includes: 1. Source data and plotting code for Figure 1, the global precipitation changes induced by bioenergy cultivation. 2. Source data and plotting code for Figure 2, the diagnostic precipitation changes for global land area, in and outside of the bioenergy cultivation area. 3. Source data plotting code for Figure 3, the changes in water balance for global land area, in and outside of bioenergy cultivation area, monsoon regions and four different humidity zones.

Emotes-2-Vec: A Large Scale Embedding of Twitch Chat Data

These are the data and resources used for a Twitch Emote recommendation system using a Word2Vec model. The nature and exploration of the data is described in Emotes-2-Vec: A Large Scale Embedding of Twitch Chat Data. To protect the privacy of the users whose messages were scraped to build this corpus, names and timestamps have been removed and only the message bodies are included. However, a tutorial for this project is included on the project GitHub: https://github.com/KoroshM/Emote-Recommender. embeddings.tsv and labeled_metadata.tsv may be used in TensorFlow's embedding projector to visualize the embedding space. Note: Model files are the following: embeddings.tsv labeled_metadata.tsv model model.model** model.wv.vectors.npy **Located here: https://drive.google.com/drive/folders/1RZC4JA4CpAcwoo6dOwq_jobTd6dNi_n2?usp=sharing

Data for "Beyond Magic Numbers: Atomic Scale Equilibrium Nanoparticle Shapes for Any Size"

This record contains <a href="https://wiki.fysik.dtu.dk/ase/ase/db/db.html">ASE databases</a> in sqlite format that contain the minimum energy structures of nanoparticles of Ag, Au, Cu, and Pd as determined in the paper "Beyond Magic Numbers: Atomic Scale Equilibrium Nanoparticle Shapes for Any Size", Rahm and Erhart, Nano Letters 17, 5775 (2017); <a href="http://doi.org/10.1021/acs.nanolett.7b02761">doi: 10.1021/acs.nanolett.7b02761</a> The <code>access-database.py</code> script included in this record demonstrates how to access the databases. It requires the <a href="https://wiki.fysik.dtu.dk/ase/">ASE package</a> to be installed.

opencc-by-4.0Jan 2023View details →

Upscaling soil organic carbon measurements at the continental scale using multivariate clustering analysis and machine learning

Data Description: To improve SOC estimation in the United States, we upscaled site-based SOC measurements to the continental scale using multivariate geographic clustering (MGC) approach coupled with machine learning models. First, we used the MGC approach to segment the United States at 30 arc second resolution based on principal component information from environmental covariates (gNATSGO soil properties, WorldClim bioclimatic variables, MODIS biological variables, and physiographic variables) to 20 SOC regions. We then trained separate random forest model ensembles for each of the SOC regions identified using environmental covariates and soil profile measurements from the International Soil Carbon Network (ISCN) and an Alaska soil profile data. We estimated United States SOC for 0-30 cm and 0-100 cm depths were 52.6 + 3.2 and 108.3 + 8.2 Pg C, respectively. Files in collection (32): Collection contains 22 soil properties geospatial rasters, 4 soil SOC geospatial rasters, 2 ISCN site SOC observations csv files, and 4 R scripts gNATSGO TIF files: ├── available_water_storage_30arc_30cm_us.tif                   [30 cm depth soil available water storage] ├── available_water_storage_30arc_100cm_us.tif                 [100 cm depth soil available water storage] ├── caco3_30arc_30cm_us.tif                                                 [30 cm depth soil CaCO3 content] ├── caco3_30arc_100cm_us.tif                                               [100 cm depth soil CaCO3 content] ├── cec_30arc_30cm_us.tif                                                     [30 cm depth soil cation exchange capacity] ├── cec_30arc_100cm_us.tif                                                   [100 cm depth soil cation exchange capacity] ├── clay_30arc_30cm_us.tif                                                     [30 cm depth soil clay content] ├── clay_30arc_100cm_us.tif                                                   [100 cm depth soil clay content] ├── depthWT_30arc_us.tif                                                        [depth to water table] ├── kfactor_30arc_30cm_us.tif                                                 [30 cm depth soil erosion factor] ├── kfactor_30arc_100cm_us.tif                                               [100 cm depth soil erosion factor] ├── ph_30arc_100cm_us.tif                                                      [100 cm depth soil pH] ├── ph_30arc_100cm_us.tif                                                      [30 cm depth soil pH] ├── pondingFre_30arc_us.tif                                                     [ponding frequency] ├── sand_30arc_30cm_us.tif                                                    [30 cm depth soil sand content] ├── sand_30arc_100cm_us.tif                                                  [100 cm depth soil sand content] ├── silt_30arc_30cm_us.tif                                                        [30 cm depth soil silt content] ├── silt_30arc_100cm_us.tif                                                      [100 cm depth soil silt content] ├── water_content_30arc_30cm_us.tif                                      [30 cm depth soil water content] └── water_content_30arc_100cm_us.tif                                   [100 cm depth soil water content] SOC TIF files: ├──30cm SOC mean.tif                             [30 cm depth soil SOC] ├──100cm SOC mean.tif                           [100 cm depth soil SOC] ├──30cm SOC CV.tif                                 [30 cm depth soil SOC coefficient of variation] └──100cm SOC CV.tif                              [100 cm depth soil SOC coefficient of variation] site observations csv files: ISCN_rmNRCS_addNCSS_30cm.csv       30cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data ISCN_rmNRCS_addNCSS_100cm.csv       100cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data Data format: Geospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution. Geospatial projection:  <pre><code>GEOGCS["GCS_WGS_1984", DATUM["D_WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["Degree",0.017453292519943295]] (base) [jbk@theseus ltar_regionalization]$ g.proj -w GEOGCS["wgs84", DATUM["WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["degree",0.0174532925199433]] </code></pre>

Figure S2 in Large-scale snake genome analyses provide insights into vertebrate development

Figure S2. Snake genome features, related to Figure 2 (A) Evolution of chromosomes in snakes. In total, 23 proto-chromosomes of Serpentes were reconstructed using four lizards as outgroup. (B) Circos plots showing conserved synteny between the hypothesized Serpentes ancestor: Serpentes (red) and Hong Kong dwarf snake (Csep-blue). (C) Snake body lengths were significantly negatively correlated with the genome evolutionary rate (correlation coefficient = 0.50, p value = 0.011). (D) Length distribution of snake ancestor gain and lost genome segments. Length distribution of snake ancestor unique genome segments (left). Length distribution of snake ancestor lost genome segments (right).