Find research datasets worth reusing

Fig. 7 in Resolution of the Eremophila tietkensii (Scrophulariaceae) species complex based on congruence between morphological and molecular pattern analyses

Fig. 7. Non-metric multi-dimensional scaling (nMDS) ordination of Eremophila tietkensii and E. sp. Rudall River individuals with pedicels present overlain with groupings from cluster analysis. Numbers indicate geographic areas of individuals as shown in Fig. 2.

Fig. 3 in Resolution of the Eremophila tietkensii (Scrophulariaceae) species complex based on congruence between morphological and molecular pattern analyses

Fig. 3. Scanning electron microscopy (SEM) images of adaxial trichomes on leaves of Eremophila sp. Hamersley Range: (a) PERTH 03557464, (b) PERTH 09105972; E. tietkensii: (c) PERTH 03856275, (d) PERTH 03899918; E. sp. Rudall River: (e) PERTH 03878759, (f) PERTH 04201159; and E. sp. Calvert Range: (g) PERTH 03878570, (h) PERTH 07512821.

Fig. 2 in Resolution of the Eremophila tietkensii (Scrophulariaceae) species complex based on congruence between morphological and molecular pattern analyses

Fig. 2. Collection locations of all PERTH voucher specimens of Eremophila tietkensii, E. sp. Hamersley Range, E. sp. Rudall River and E. sp. Calvert Range in WA. Specimens were assigned to a priori groups numbered 1–7.

Fig. 9 in Resolution of the Eremophila tietkensii (Scrophulariaceae) species complex based on congruence between morphological and molecular pattern analyses

Fig. 9. Principal component analysis of detected SNPs of Eremophila tietkensii, E. sp. Hamersley Range, E. sp. Calvert Range, and E. sp. Rudall River. Inset shows positions of E. tietkensii and E. sp. Rudall River specimens.

Fig. 6 in Resolution of the Eremophila tietkensii (Scrophulariaceae) species complex based on congruence between morphological and molecular pattern analyses

Fig. 6. (a) Non-metric multi-dimensional scaling (nMDS) ordination of all individuals on the basis of selected traits (Table 1) overlain with groupings from cluster analysis (b); and (b) UPGMA classification of all individuals on the basis of selected traits (Table 1).

opencc-by-4.0Mar 2024View details →

Imaging surface structure and premelting of ice Ih with atomic resolution

Here lies the tabulated data and the MD trajectories used to create the Figures for the Nature manuscript 2023-08-14175 titled "Imaging surface structure and premelting of ice Ih with atomic resolution".

opencc-by-4.0Feb 2024View details →

High-resolution wall-to-wall time series predictions of seasonal maize area and yield for Rwanda over 2019-2023

This is the companion dataset to publication {TBD}. It contains 1) seasonal composites of predicted maize cover and yield at 10 m resolution in Rwanda for two annual agricultural seasons over five years, 2) scripts for the end-to-end machine learning pipeline that produces these data products, and 3) data or references needed as inputs to the pipeline.  <h2>1) Maize cover and yield seasonal composites</h2> The data are provided here as netCDF4 files with four dimensions for x, y, band, and season. They can also be accessed as Google Earth ImageCollections at:  <ul> <li>https://code.earthengine.google.com/?asset=projects/b2p-geospatial/assets/lulc_classifier_composite</li> <li>https://code.earthengine.google.com/?asset=projects/b2p-geospatial/assets/maize_yield_composite </li> </ul> <h3>Land cover and maize classification</h3> The land cover classification file is found at <code>data/composites/lulc_classifier_Rwanda_2019to2023.nc</code>. The land cover classification images contain 3 bands/variables: maizeProb, the raw predicted probability of the pixel being maize given by the gradient boosted tree model; majorityClass, the categorical land cover class with the highest predicted probability among any of the nine classes in the respective pixel; and optimalClass, the categorical land cover class adjusted to agree with national statistics for expected maize area. The land cover classes map to the raster values as follows:  <div> <div> <pre>{ 1: 'maize', 2: 'nonmaize_annual', 3: 'nonmaize_perennial', 4: 'scrub_shrub_land', 5: 'forest', 6: 'flooded_vegetation', 7: 'water', 8: 'structure', 9: 'bare' }</pre> </div> </div> The dataset includes 5 years (2019-2023) and 10 seasons - the available time period at time of publication. In Rwanda, maize is typically planted and harvested during two distinct agricultural seasons per year: Season A from September to February and Season B from March to June. Therefore the seasons in the data are: 2019_Season_A, 2019_Season_B, 2020_Season_A, 2020_Season_B, 2021_Season_A, 2021_Season_B, 2022_Season_A, 2022_Season_B, 2023_Season_A, 2023_Season_B. <h3>Maize yield</h3> The maize yield file is found at <code>data/composites/maize_yield_Rwanda_2019to2023.nc</code>. Each of the images in the yield composites has 3 bands/variables also: maizeYield, the model's output of continuous predicted yield (kg/ha) in each pixel regardless of land class; maizeYield_majorityClass, predicted maize yield masked to the majority class land classification; and maizeYieldAdj_optimalClass, where the raw predicted yields were masked to the optimal maize classification land cover layer and normalized to national statistics.  The dataset includes the same seasons as the classification product; see above for a description. <h2>2) End-to-end machine learning pipeline</h2> All earth observation imagery, analysis, and outputs unless otherwise stated were hosted in the Google Earth Engine (GEE) environment and developed with the Earth Engine Python API in Python v3.10. To set up a local conda environment use the <code>scripts/environment.yml</code> file. The user must have <a href="https://cloud.google.com/storage">Google Cloud Storage (GCS)</a> and <a href="https://cloud.google.com/earth-engine">Google Earth Engine (GEE)</a> accounts. The pipeline, at this scale, will incur some processing and storage fees, although Google offers a free trial to all new users and the total cost of the high-resolution wall-to-wall predictions is nominal (~$20 for one season).  The scripts needed to perform the pipeline are located in the <code>scripts</code> folder.  The files contained in the <code>scripts/helpers</code> directory will be called by various subsequent scripts and do not to be run interactively by the user.  Follow the script in the order described below. The user should pause after running each script and confirm that all outputs were created and loaded to GCS before continuing the pipeline; for some steps this may take hours to days depending on processing speed.  <h3>Google Cloud Storage and Earth Engine set-up</h3> Users should specify the names of the bucket and asset project that were chosen during set up of their GCS and GEE environments in the Objects section of <code>scripts/helpers/maize_pipeline_0_workspace.py</code>. <h3>Pipeline set-up</h3> In <code>scripts/pipeline_setup</code>, you will find the following scripts to perform data preparation of inputs into model building and prediction.  <ul> <li><code>maize_pipeline_1_clean_training_data.py</code> - Cleans and merges all available crop label and yield data for model training and validation</li> <li><code>maize_pipeline_2_dwnld_data_training.py</code> - Downloads satellite-derived and auxiliary features at training data points for model building</li> <li><code>maize_pipeline_3_dwnld_data_inference.py</code> - Downloads satellite-derived and auxiliary features at every 10 m pixel in Rwanda on a district-wise basis for prediction</li> </ul> <h3>Land cover and maize classification</h3> In <code>scripts/maize_classification</code>, you will find the following scripts to perform model building, prediction, and post-processing for the classificaton of land cover type and maize cover. <ul> <li><code>maize_classifier_1_feature_selection.py</code> - Selects features subset for land cover classification with mutual information score or variable importance</li> <li><code>maize_classifier_2_build_model.py</code> - Builds gradient boosted tree model for land cover classification from training data</li> <li><code>maize_classifier_3_prediction.py</code> - Applies model for land cover classification to every 10 m pixel in Rwanda by season and district</li> <li><code>maize_classifier_4_postprocess.py</code> - Mosaics district-wise predictions and normalizes maize cover predictions to national agricultural statistics</li> </ul> <h3>Maize yield</h3> In <code>scripts/maize_yield</code>, you will find the following scripts to perform modeling building, prediction, and post-processing for maize yield estimation.  <ul> <li><code>maize_yield_1_build_model.py</code> - Builds gradient boosted tree model and performs bias correction for maize yield estimation from training data</li> <li><code>maize_yield_2_prediction.py</code> - Applies model for maize yield estimation to every 10 m pixel in Rwanda by season and district</li> <li><code>maize_yield_3_postprocess.py</code> - Mosaics district-wise predictions and normalizes maize yield predictions to national agricultural statistics</li> </ul> If you are running the entire pipeline with refreshed training data and model building, run each of these scripts, in order. By default, the script will run all A and B seasons from 2019A to current. Otherwise, if you just wish to re-run or update seasonal predictions from the existing classification or yield model run <code>maize_pipeline_3_dwnld_data_inference.py</code> to download the seasonal feature data across Rwanda and <code>maize_classifier_3_prediction.py</code>and <code>maize_classifier_4_postprocess.py</code> for classification predictions or <code>maize_yield_2_prediction.py</code> and <code>maize_yield_3_postprocess.py</code> for yield predictions, making sure to specify which season(s) are of interest in each script. However to do this, you also need to have a copy of the previously built models in your GCS (provided at <code>data/models</code>).  <h2>3) Input data into machine learning pipeline</h2> A description of datasets that must be sourced outside of the GEE platform is provided below. When available, the primary data source is also included in the directory <code>data/baselayers</code>. All other data, including Sentinel-2 imagery, auxiliary data, and other existing global land cover classificaiton products are hosted on GEE and called by the scripts directly. All datasets last accessed on 12 March 2024. <h3>Administrative and geological boundaries</h3> <ul> <li>World Countries - Downloaded from <a href="https://datacatalog.worldbank.org/search/dataset/0038272/World-Bank-Official-Boundaries">The World Bank Official Boundaries</a> and included here at <code>data/baselayers/World_Countries</code>.</li> <li>Rwanda district boundaries - Downloaded from <a href="https://datacatalog.worldbank.org/search/dataset/0041453/Rwanda-Admin-Boundaries-and-Villages">The World Bank Rwanda Admin Boundaries And Villages</a> and included here at <code>data/baselayers/WB_NISR_2018</code>. This should be loaded into a FeatureCollection GEE asset named districts_fc for use in the pipeline. </li> <li>Rwanda agro-ecological zones - Downloaded from <a href="https://doi.org/10.1371/journal.pone.0149239">Nzeyimana, Hartemink & Geissen (2016)</a> and included here at <code>data/baselayers/MINAGRI_AEZ_1980</code>. This should be loaded into a FeatureCollection GEE asset named aez_rwanda for use in the pipeline. </li> </ul> <h3>Global land cover classification product</h3> <ul> <li>Microsoft/Impact Observatory LULC - Although the <a href="https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class">10m Annual Land Use Land Cover (9-class) V1</a> product contains data from 2017-2022, only the LULC map from the year 2021 was used, provided here at <code>data/baselayers/impactobs_lulc_rwa_2021.tif</code>. This should be loaded into an ImageCollection GEE asset named impact_obs_lulc for use in the pipeline.</li> </ul> (The others - Dynamic World and ESA's WorldCover - are hosted on GEE directly.) <h3>Land cover labels and maize yield crop cuttings</h3> <ul> <li>One Acre Fund - Contact authors to request access as this dataset is not hosted publicly. </li> <li>RTI International - The original source of this data (Radiant MLHub) has been discontinued, but users may be able to access it via <a href="https://beta.source.coop/repositories/rti/rwanda-crop-type/">Source Cooperative</a>. The data is also included here at <code>data/baselayers/rti_rwanda_crop_type_labels</code>. </li> <li>Crop Harvest - Downloaded from <a href="../records/7257688">Tseng et al. (2021, v13)</a> and included here at <code>data/baselayers/CropHarvest</code>. These data points were ultimately not used in the training data, but are provided here for others that may find this dataset useful in their context.</li> </ul> <h3>Rwanda national agricultural surveys</h3> <ul> <li>National Institute of Statisitcs Rwanda (NISR) - Downloaded from <a href="https://statistics.gov.rw/datasource/seasonal-agricultural-survey">NISR Seasonal Agricultural Survey</a> and existing seasons included here at <code>data/baselayers/NISR_Seasonal_Ag_Surveys</code>. For each subsequent season, the user will have to download the spreadsheet of survey results from the NISR webpage (linked) and add the respective season to the <code>get_nisr_data</code> function in the <code>helpers/maize_pipeline_0_helpers_postprocess.py</code> script to clean and read in the data for use in the pipeline. </li> </ul>

Supplementary Movie 1 from: A genetically encoded biosensor to monitor dynamic changes of c-di-GMP with high temporal resolution

This record contains Supplementary Movie 1 from: A genetically encoded biosensor to monitor dynamic changes of c-di-GMP with high temporal resolution Andreas Kaczmarczyk, Simon van Vliet, Roman Peter Jakob, Raphael Dias Teixeira, Inga Scheidat, Alberto Reinders, Alexander Klotz, Timm Maier, Urs Jenal Biozentrum, University of Basel, 4056 Basel, Switzerland Correspondence to: urs.jenal[at]unibas.ch, andreas.kaczmarczyk[at]unibas.ch

High-resolution global gridded population between 1870 and 2100

history_pop.zip contains historical gridded population data from 1870 to 2010 at a 10-year interval SSP1.zip contains future population projection data unter from 2020 to 2100 at a 10-year interval under SSP1 SSP2.zip contains future population projection data unter from 2020 to 2100 at a 10-year interval under SSP2 SSP3.zip contains future population projection data unter from 2020 to 2100 at a 10-year interval under SSP3 SSP4.zip contains future population projection data unter from 2020 to 2100 at a 10-year interval under SSP4 SSP5.zip contains future population projection data unter from 2020 to 2100 at a 10-year interval under SSP5

All-sky daily mean ambient air temperature datasets at 1-km resolution from 2003-2012 in China

<ul> <li>All-sky daily maximum, minimum, and mean ambient air temperature datasets at 1-km resolution over two decades (2003-2022) in China have been generated by the four-dimensional spatiotemporal deep forest (4D-STDF) model. The overall RMSE values for estimates are 1.49°C, 1.53°C, and 1.18°C.</li> <li>These datasets cover mainland China, featuring high spatial resolution (1km), long temporal sequences (2003-2022), and increased accuracy. They are presented in GeoTIFF format with WGS84 projection, with data measured in 0.1 degrees Celsius (°C).</li> <li>These datasets have divided for six parts to upload Zenodo, the links as follow: <ol> <li>Daily Tmax from the years 2003-2012: <a href="https://doi.org/10.5281/zenodo.10983219">https://doi.org/10.5281/zenodo.10983219</a>,</li> <li>Daily Tmax from the years 2013-2022: <a href="https://doi.org/10.5281/zenodo.10983207">https://doi.org/10.5281/zenodo.10983207</a>,</li> <li>Daily Tmin from the years 2003-2012: <a href="https://doi.org/10.5281/zenodo.10951765">https://doi.org/10.5281/zenodo.10951765</a>,</li> <li>Daily Tmin from the years 2013-2022: <a href="https://doi.org/10.5281/zenodo.10983199">https://doi.org/10.5281/zenodo.10983199</a>,</li> <li>Daily Tmean from the years 2003-2012: <a href="https://doi.org/10.5281/zenodo.10947354">https://doi.org/10.5281/zenodo.10947354</a>,</li> <li>Daily Tmean from the years 2013-2022: <a href="https://doi.org/10.5281/zenodo.10983177">https://doi.org/10.5281/zenodo.10983177</a></li> </ol> </li> <li>This dataset is daily Tmean from 2003-2012 (<a href="https://doi.org/10.5281/zenodo.10947354">https://doi.org/10.5281/zenodo.10947354</a>).</li> </ul>

All-sky daily min ambient air temperature datasets at 1-km resolution from 2003-2012 in China

<ul> <li>All-sky daily maximum, minimum, and mean ambient air temperature datasets at 1-km resolution over two decades (2003-2022) in China have been generated by the four-dimensional spatiotemporal deep forest (4D-STDF) model. The overall RMSE values for estimates are 1.49°C, 1.53°C, and 1.18°C.</li> <li>These datasets cover mainland China, featuring high spatial resolution (1km), long temporal sequences (2003-2022), and increased accuracy. They are presented in GeoTIFF format with WGS84 projection, with data measured in 0.1 degrees Celsius (°C).</li> <li>These datasets have divided for six parts to upload Zenodo, the links as follow: <ol> <li>Daily Tmax from the years 2003-2012: <a href="https://doi.org/10.5281/zenodo.10983219">https://doi.org/10.5281/zenodo.10983219</a>,</li> <li>Daily Tmax from the years 2013-2022: <a href="https://doi.org/10.5281/zenodo.10983207">https://doi.org/10.5281/zenodo.10983207</a>,</li> <li>Daily Tmin from the years 2003-2012: <a href="https://doi.org/10.5281/zenodo.10951765">https://doi.org/10.5281/zenodo.10951765</a>,</li> <li>Daily Tmin from the years 2013-2022: <a href="https://doi.org/10.5281/zenodo.10983199">https://doi.org/10.5281/zenodo.10983199</a>,</li> <li>Daily Tmean from the years 2003-2012: <a href="https://doi.org/10.5281/zenodo.10947354">https://doi.org/10.5281/zenodo.10947354</a>,</li> <li>Daily Tmean from the years 2013-2022: <a href="https://doi.org/10.5281/zenodo.10983177">https://doi.org/10.5281/zenodo.10983177</a></li> </ol> </li> <li>This dataset is daily Tmin from 2003-2012 (<a href="https://doi.org/10.5281/zenodo.10951765">https://doi.org/10.5281/zenodo.10951765</a>).</li> </ul>

Comprehensive dataset from high resolution UAV land cover mapping of diverse natural environments in Serbia

This dataset consists of raw RGB and NIR images captured using the DJI Inspire 1 UAV equipped with interchangeable RGB and NDVI-modified cameras. Data were collected across 27 diverse study sites in Serbia, representing a variety of ecological and landscape features. The UAV flights followed pre-defined grid missions, capturing high-resolution imagery with a ground sampling distance (GSD) of 3–4 cm. The collected images were processed to produce georeferenced orthomosaics, which serve as the basis for detailed land cover classification and analysis.

Model input for "A high-resolution physical-biogeochemical model for marine resource applications in the Northern Indian Ocean (MOM6-COBALT-IND12)"

This dataset includes the model input files required to reproduce the simulations described in "A high-resolution physical-biogeochemical model for marine resource applications in the Northern Indian Ocean (MOM6-COBALT-IND12)". When using these files to run the model, input files should be organized in a folder named <code>INPUT/</code> located within the directory where the model is executed. Additionally, key configuration files, such as <code>data_table</code>, <code>diag_table</code>, <code>field_table</code>, and <code>input.nml</code>should be placed in the main working directory. To manage file size, only a subset of the data is provided, covering the first year of the simulation period. Note that ERA5 atmospheric forcing data is not included in this dataset but can be accessed directly from the <a href="https://doi.org/10.24381/cds.adbb2d47" target="_new" rel="noopener">Copernicus Climate DataStore</a>.

Seminar - Dr Thomas Beaney - Simplifying complexity: generating multi-resolution clusters of chronic diseases in 10 million people in England

Recording of the presentation given on Wednesday 23rd October by Dr Thomas Beaney “Simplifying complexity: generating multi-resolution clusters of chronic diseases in 10 million people in England” In this talk, Tom discusses an approach for generating clusters of chronic diseases in people with Multiple Long-Term Conditions (MLTC), which combines natural language processing and network-based clustering. He also discusses the challenges of evaluating and using clusters in practice to influencepatient care. You can view the publication of his work here: https://www.nature.com/articles/s43856-024-00529-4

30 m resolution Qinghai-Tibet Plateau quarterly leaf area index maps of 2023

Quarterly LAI maps of the Qinghai-Tibet Plateau are stored in folders divided into 5 × 5-degree blocks. The data files are in TIFF format, using the WGS84 coordinate system, with a uniform resolution of 30 meters. The naming convention for the files is: “LAI_Quarter_Longitude_Latitude”, e.g., LAI_1202_95_30.tif indicates the 2023 Q1 maximum LAI composite image for a block with the lower-left corner at 95°E longitude and 30°N latitude. Data usage instructions: The files can be opened with software such as ArcGIS, ENVI, and QGIS. Pixel valid values range from 0 to 8, while the background fill value is NaN. All block data has been compressed using LZW (Lempel-Ziv-Welch) lossless compression.

opencc-by-4.0Jul 2024View details →

Simulation of Southern Ocean cloud processes using the high-resolution regional UK Met Office Unified Model with interactive aerosols

Code and model data used to create the figures for Price et al., 2024, JGR (in prep.).

MD Source Data for "Subunit-specific conductance of single HCN pacemaker channels at femtosiemens resolution"

This dataset is the MD source data for the research paper ''Subunit-specific conductance of single HCN pacemaker channels at femtosiemens resolution''. It includes the following parts: <ol> <li>Figures.zip. This file provides high resolution MD related figures, including main figures and supplementary figures.</li> <li>Figure_source_data.zip. This is the source data for the figures in the paper.</li> <li>Homology_Modelling_result.zip. This file provides the input (rHCN4, PDB ID: 7NP3) and output files (mHCN1-4, hHCN4) of homology modelling.</li> <li>MD_input.zip. This is the MD setup input data. It includes starting structure, topology file, .mdp file and force field file. Due to the limitation of storage space (50 GB), it is a pity we couldn't upload all trajectories files (~180 GB). But these files are enough for rerunning the MD simulations.</li> <li>Movie 1. K+ permeation in mHCN2 channel during a 250 ns trajectory.</li> </ol>

opencc-by-4.0Jul 2024View details →

Characteristics and Representation of Subgrid Convective Flux in Tropical Cyclone Convection System at Convection-Permitting Resolution

Data and analysis scripts for figures of Journal article (Characteristics and Representation of Subgrid Convective Flux in Tropical Cyclone Convection System at Convection-Permitting Resolution)

opencc-by-4.0Oct 2024View details →

Single-nucleotide-resolution genomic maps of O6-methylguanine from the glioblastoma drug temozolomide

Temozolomide kills cancer cells by forming O6-methylguanine (O6-MeG), which leads to cell cycle arrest and apoptosis. However, O6-MeG repair by O6-methylguanine-DNA methyltransferase (MGMT) contributes to drug resistance. Characterizing genomic profiles of O6-MeG could elucidate how O6-MeG accumulation is influenced by repair, but there are no methods to map genomic locations of O6-MeG. Here, we developed an immunoprecipitation- and polymerase-stalling-based method, termed O6-MeG-seq, to locate O6-MeG across the whole genome at single-nucleotide resolution. We analyzed O6-MeG formation and repair with regards to sequence contexts and functional genomic regions in a glioblastoma-derived cell line and evaluated the impact of MGMT expression. O6-MeG signatures were highly similar to mutational signatures from patients previously treated with temozolomide. Furthermore, MGMT did not preferentially repair O6-MeG with respect to sequence context, chromatin state or gene expression level, however, may protect oncogenes from mutations. Finally, we found an MGMT-independent strand bias in O6-MeG accumulation in highly expressed genes. These data provide high resolution insight on how O6-MeG formation and repair are impacted by genome structure and nucleotide sequence. Further, O6-MeG-seq is expected to enable future studies of DNA modification signatures as diagnostic markers for addressing drug resistance and preventing secondary cancers.