Find research datasets worth reusing

High-resolution hard X-ray tomography and histology of a rat jaw for stem cell-mediated distraction osteogenesis

<p>Histology and microtomography of a rat jaw after distraction. These datasets appear in "<em>Combining high-resolution hard X-ray tomography and histology for stem cell-mediated distraction osteogenesis</em>" Applied Sciences 12(12) (2022) 6268.</p> <p>Micromography (hdr/img files) has pixel size of 10.0228 µm. Histology (.tif file) has pixel size of 0.243094 µm.</p> <p>Slice to volume registration scripts can be found at https://github.com/grodgers1/SliceToVolume.</p>

High-resolution maps of material stock, population and employment in Austria from 1985 to 2018

<p>Global societal material stocks such as buildings and infrastructure accumulated rapidly within recent decades, along with population growth. Material stocks constitute the physical basis of most socio-economic activities and services, such as mobility, housing, health, or education. The dynamics of stock growth, and its relation to the population that demands those services, is an essential indicator for long-term societal resource use and patterns of emissions. The creation of societal material stock creates path dependencies for future resource use, with an important impact on how the transformation towards sustainable societies can succeed.</p> <p>This dataset features detailed maps of material stock and population, as well as the distribution of jobs, for Austria on a 30m grid. The data is based on recent maps of material stock and building volume (compare to Haberl et al. 2021, doi: 10.1021/acs.est.0c05642, data: https://zenodo.org/record/4522892), recent and historic census data, and a time series of Landsat TM, ETM+, and OLI Earth Observation data.</p> <p><strong>Temporal extent</strong></p> <p>The data contains annual maps from 1985 to 2018.</p> <p><strong>Data format and units</strong></p> <p>Per Austrian federal state, the data come in tiles of 30x30km. The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Please consider the generation of image pyramids before using *.vrt files.</p> <p>All image data has 34 bands, where band 1 is data for 1985, and band 34 is data for 2018.</p> <p>The dataset features</p> <ul> <li>population (Scaled by 100 to reduce data storage size. Divide by 100 to get people per cell)</li> <li>jobs (Scaled by 100 to reduce data storage size. Divide by 100 to get jobs per cell)</li> <li>mass (in tons) of … <ul> <li>total material stock <ul> <li>material stock in buildings <ul> <li>in commercial and industrial buildings</li> <li>in multi-family residential buildings</li> <li>in high-rise buildings</li> <li>in single-family residential buildings</li> <li>in lightweight buildings</li> </ul> </li> <li>material stock in road infrastructure</li> <li>material stock in rail infrastructure</li> <li>material stock in other infrastructure</li> </ul> </li> </ul> </li> </ul> <p><strong>Further information</strong></p> <p>For further information, please see the publication or contact Franz Schug (fschug@wisc.edu). Visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website </a>to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</p> <p><strong>Funding</strong></p> <p>This research was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).</p> <p> </p>

CN-N (version 1): a crop-specific, 1 km-resolution nitrogen rate data product in China over 2004–2016

<p>CN-N version 1: A dataset of crop-specific, 1 km resolution nitrogen rate for rice, wheat and  maize in China over 2004-2016 </p>

Global spatiotemporal continuous daily high-resolution total column carbon monoxide for TROPOMI

<p>A novel framework is developed to recover missing data in global TROPOMI TCCO product over land from Jun. 01 2018 to May. 31 2021 by fusing multisource data. Validation results show that the accuracy of recovered results is satisfactory and close to that of TROPOMI, with the R of 0.885 against NDACC and 0.918 against TCCON. Furthermore, the recovered results achieve a small (distinctly) better performance than those of MOPITT (CAMS). The spatial pattern of the recovered TCCO is consistent with that of the MOPITT TCCO and can specify much finer spatial details by comparison with CAMS.</p>

opencc-by-4.0Nov 2017View details →

Ind. Ch. 59 Grant of Mrgesvaravarman, Year 8, seal High resolution TIFF

<p>Ind. Ch. 59 Grant of Mrgesvaravarman, Year 8, seal. High resolution TIFF</p>

opencc-by-4.0Nov 2017View details →

Ind. Ch. 59 Grant of Mrgesvaravarman, Year 8 High resolution TIFF

<p>Ind. Ch. 59 Grant of Mṛgeśvaravarman, Year 8. High resolution TIFF</p>

opencc-by-4.0Dec 2017View details →

Flat-Field Super-Resolution Localization Microscopy with a Low-Cost Refractive Beam-Shaping Element

<p>Raw data for the article "Flat-Field Super-Resolution Localization Microscopy with a Low-Cost Refractive Beam-Shaping Element". Each set of three files is a set of dSTORM images, taken using either top-hat illumination or a Gaussian illumination. For Sample 0, the top-hat illumination was performed first. For Sample 1, the Gaussian illumination was performed first.</p>

opencc-by-4.0Dec 2017View details →

Ind. Ch. 60 Grant of the time of Ravivarman, seal High resolution TIFF

<p><a href="https://siddham.network/object/ob01055/">OB01055</a> Ind. Ch. 60 Grant of the time of Ravivarman, seal High resolution TIFF</p>

opencc-by-4.0Mar 2014View details →

Figure 6 in High-resolution stratigraphy of the Changhsingian (Late Permian) successions of NW Iran and the Transcaucasus based on lithological features, conodonts and ammonoids

Figure 6. Characteristic Changhsingian conodonts from the Julfa region (scale bars equal to 100 µm); all specimens stored in the collection of the Ferdowsi University, Mashhad. (A) Clarkina orientalis (Barskov and Koroleva, 1970); FUM#1J192.1; upper Julfa beds (Vedioceras beds), Ali Bashi 1 section. (B) Clarkina subcarinata Sweet, 1973; FUM#4J142.8; Zal Member (Ali Bashi Formation), Ali Bashi 4 section. (C) Clarkina changxingensis Wang and Wang, 1981; FUM#4J153.1; Zal Member (Ali Bashi Formation), Ali Bashi 4 section. (D) Clarkina bachmanni Kozur, 2004; FUM#AJ185.23; Paratirolites Limestone (Ali Bashi Formation), Aras Valley section. (E) Clarkina nodosa Kozur, 2004; FUM#G249.16; Paratirolites Limestone (Ali Bashi Formation), Ali Bashi M section. (F) Clarkina yini Mei, 1998; FUM#AJ192.4; Paratirolites Limestone (Ali Bashi Formation), Aras Valley section. (G) Clarkina abadehensis Kozur, 2004; FUM#1J248.9; Paratirolites Limestone (Ali Bashi Formation), Ali Bashi 1 section. (H) Clarkina hauschkei Kozur, 2004, FUM#1J249D.9; Paratirolites Limestone (Ali Bashi Formation), Ali Bashi 1 section. (I) Hindeodus eurypyge Nicoll, Metcalfe and Wang, 2002, FUM#1J255.7 (cusp broken); Zal Member (Ali Bashi Formation), Ali Bashi 1 section. (J) Hindeodus typicalis Sweet, 1970, FUM#G233.5; Paratirolites Limestone (Ali Bashi Formation), Ali Bashi M section. (K) Hindeodus typicalis Sweet, 1970, FUM#4J200.56; Paratirolites Limestone (Ali Bashi Formation), Ali Bashi 4 section. (L) Hindeodus julfensis Sweet, 1973, FUM#1J198.4; Zal Member (Ali Bashi Formation), Ali Bashi 4 section. (M) Hindeodus praeparvus Kozur, 1996, FUM#G274.6 (cusp broken); Aras Member (Elikah Formation), Ali Bashi M section. (N) Hindeodus changxingensis Wang, 1995, FUM#4J201.6 (cusp broken); Aras Member (Elikah Formation), Ali Bashi 4 section. (O) Merrillina ultima Kozur, 2004, FUM#AJ204.13; Aras Member (Elikah Formation), Aras Valley section. (P) Hindeodus parvus Kozur and Pjatakova, 1976, FUM#4J213.1; Elikah Formation; Ali Bashi 4 section.

opencc-by-4.0Mar 2014View details →

Figure 5 in High-resolution stratigraphy of the Changhsingian (Late Permian) successions of NW Iran and the Transcaucasus based on lithological features, conodonts and ammonoids

Figure 5. The correlation of the conodont schemes by Kozur (2005, 2007), Shen and Mei (2010) and own results with the ammonoid stratigraphy by Shevyrev (1965) and own results.

opencc-by-4.0Mar 2014View details →

Figure 7 in High-resolution stratigraphy of the Changhsingian (Late Permian) successions of NW Iran and the Transcaucasus based on lithological features, conodonts and ammonoids

Figure 7. Characteristic Changhsingian ammonoids from the Julfa region (scale bars equal to 5 mm); all specimens stored in the collection of the Museum für Naturkunde, Berlin. (A) Phisonites triangulus Shevyrev, 1965 from the Aras Valley section, specimen MB.C.22703; × 1.0. (B) Iranites transcaucasius (Shevyrev, 1965) from the Aras Valley section, specimen MB.C.22704; × 1.0. (C) Dzhulfites nodosus Shevyrev, 1965 from the Aras Valley section, specimen MB.C.22705; × 1.0. (D) Shevyrevites nodosus Shevyrev, 1965 from the Aras Valley section, specimen MB.C.22706; × 1.0. (E) Paratirolites trapezoidalis Shevyrev, 1965 from the Ali Bashi 4 section, specimen MB.C.22707; × 0.75. (F) Stoyanowites dieneri (Stoyanow, 1910) from the Aras Valley section, specimen MB.C.22708; × 1.0. (G) Paratirolites vediensis Shevyrev, 1965 from the Ali Bashi N section, specimen MB.C.22709; × 0.75. (H) Abichites stoyanowi (Kiparisova, 1947) from the Ali Bashi N section, specimen MB.C.22710; × 1.25. (I) Arasella minuta (Zakharov, 1983) from the Ali Bashi N section, specimen MB.C.22711; × 1.25.

High-Resolution Vector-borne Disease Infection Risk Mapping with Area-to-Point Kriging and Species Distribution Modeling - Datasets

<p>Datasets and notebooks used in the publication High-Resolution Vector-borne Disease Infection Risk Mapping with Area-to-Point Kriging and Species Distribution Modeling</p>

Data and code for high-resolution climate-resilient corridor mapping in southwestern Costa Rica (beta)

<p>High-resolution mapping and validation of potential climate-resilient corridors in southwestern Costa Rica. A fully documented, complete version of this repository will be archived with a DOI upon manuscript publication.</p>

Historical time-series reconstruction benchmark dataset of Landsat bi-monthly aggregates from GLAD ARD-2 at 30-m resolution with stratified sampling based on ESA CCI

<h2>Description</h2> <p>Historical time-series reconstruction benchmark dataset presented here is designed for evaluating and comparing the performance of time series reconstruction methods in the context of land cover change detection. The dataset is based on the European Space Agency Climate Change Initiative (ESA CCI) land cover dataset, which has been aggregated into 18 classes to facilitate analysis. The dataset includes information on land cover dynamics from 2000 to 2020, focusing on identifying and characterizing changes in land cover over time.</p> <h3><strong>Data Collection and Processing:</strong></h3> <p>The dataset is derived from the ESA CCI land cover dataset, which provides information on land cover classes at a global scale. The original dataset, containing 37 land cover classes, was aggregated into 18 classes based on similarity. Pixels with stable land cover over the study period and pixels with one or multiple land cover changes were identified and grouped into strata for sampling purposes.</p> <p>Sampling points were selected using a stratified sampling design, ensuring representation across different land cover classes and change scenarios. Approximately 2600 points were selected from each stratum, resulting in a total of 51,978 sampling points. The selected points were uniformly distributed along the strata, with spatial variations accounted for.</p> <p>Bimonthly time series data were extracted for each sampling point from 1997 to 2022, capturing temporal dynamics in land cover. Artificial gaps were introduced into the time series data to simulate real-world data loss, allowing for the evaluation of time series reconstruction methods under varying gap densities.</p> <p>The time series values were extracted from Landsat GLAD imagery using the specified spectral bands, including blue, green, red, NIR, SWIR1, SWIR2, and thermal bands. Additionally, a clear quality band was also extracted.</p> <h3>Data Details</h3> <ul> <li><strong>Time Period:</strong> 1997-01-01 to 2022-12-31</li> <li><strong>Type of Data: </strong>R data frame / Geopackage points.</li> <li><strong>Collection/Derivation:</strong> Derived from Landsat ARD v2, processed with Scikit-map.</li> <li><strong>Coordinate Reference System:</strong> EPSG:4326</li> <li><strong>Bounding Box:</strong> All the globe</li> <li><strong>File Format:</strong> RDS</li> </ul> <p> </p> <h3><strong>Reclassified Classes of ESA CCI Land Cover Dataset</strong></h3> <table> <tbody> <tr> <td> <div> <div> <p><strong>Aggregated Class Code</strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Aggregated Class Label</strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Original ESA CCI Classes</strong></p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>10</p> </div> </div> </td> <td> <div> <div> <p>Cropland rainfed</p> </div> </div> </td> <td> <div> <div> <p>10, 11, 12</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>30</p> </div> </div> </td> <td> <div> <div> <p>Mosaic cropland | natural vegetation</p> </div> </div> </td> <td> <div> <div> <p>30, 40</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>50</p> </div> </div> </td> <td> <div> <div> <p>Tree cover broadleaved evergreen</p> </div> </div> </td> <td> <div> <div> <p>50</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>60</p> </div> </div> </td> <td> <div> <div> <p>Tree cover broadleaved deciduous</p> </div> </div> </td> <td> <div> <div> <p>60, 61, 62</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>70</p> </div> </div> </td> <td> <div> <div> <p>Tree cover needleleaved evergreen</p> </div> </div> </td> <td> <div> <div> <p>70, 71, 72</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>80</p> </div> </div> </td> <td> <div> <div> <p>Tree cover needleleaved deciduous</p> </div> </div> </td> <td> <div> <div> <p>80, 81, 82</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>90</p> </div> </div> </td> <td> <div> <div> <p>Tree cover mixed leaf type</p> </div> </div> </td> <td> <div> <div> <p>90</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>100</p> </div> </div> </td> <td> <div> <div> <p>Mosaic tree and shrub | herbaceous cover</p> </div> </div> </td> <td> <div> <div> <p>100, 110</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>120</p> </div> </div> </td> <td> <div> <div> <p>Shrubland</p> </div> </div> </td> <td> <div> <div> <p>120, 121, 122</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>150</p> </div> </div> </td> <td> <div> <div> <p>Sparse vegetation</p> </div> </div> </td> <td> <div> <div> <p>150, 151, 152, 153</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>160</p> </div> </div> </td> <td> <div> <div> <p>Tree cover flooded</p> </div> </div> </td> <td> <div> <div> <p>160, 170</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>180</p> </div> </div> </td> <td> <div> <div> <p>Shrub or herbaceous cover flooded</p> </div> </div> </td> <td> <div> <div> <p>180</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>200</p> </div> </div> </td> <td> <div> <div> <p>Bare areas</p> </div> </div> </td> <td> <div> <div> <p>200, 201, 202</p> </div> </div> </td> </tr> </tbody> </table> <p>In the table, each row represents a reclassified land cover class, identified by a unique code. The 'Original ESA CCI Classes' column lists the specific land cover classes from the European Space Agency Climate Change Initiative dataset that are grouped together to form each broader category. Note that land cover classes not listed in this table were retained in their original value and were not reclassified.</p> <h3><strong>File Format</strong></h3> <p>The dataset comprises observations spanning from January 1997 to November 2022, capturing data for 51,978 samples.</p> <ul> <li>blue.rds: Time series data for the blue spectral band.</li> <li>green.rds: Time series data for the green spectral band.</li> <li>red.rds: Time series data for the red spectral band.</li> <li>nir.rds: Time series data for the near-infrared (NIR) spectral band.</li> <li>swir1.rds: Time series data for the shortwave infrared 1 (SWIR1) spectral band.</li> <li>swir2.rds: Time series data for the shortwave infrared 2 (SWIR2) spectral band.</li> <li>thermal.rds: Time series data for the thermal infrared band.</li> <li>clear.rds: Time series data for the clear quality band, used for masking out cloudy observations.</li> </ul> <p>How open the files in R:</p> <p><code>blue <- readRDS("blue.rds")</code></p> <p>To open the files in Python, you need to the <code>pyreadr</code> library:</p> <p><code>import pyreadr</code><br><code>blue = pyreadr.read_r('blue.rds')</code></p> <p> </p>

opencc-by-4.0Apr 2024View details →

Correlative microscopy of rat cultured hippocampal pyramidal cell from 40x confocal imaging to super-resolution 93x 3D STED of dendritic spines

<p>This dataset contain multi-scale image of rat hippocampal pyramidal cell related to our paper "<em>From tissues to segmentation: a modular framework for multi-scale neuron isolation</em>" by Cauzzo et al. <strong>Nature Comm (2024).</strong></p>

Global soil moisture simulated by SoilClim and mHM models at 0.5° resolution for the 1980–2022 period

<p>This deposit contains two .zip archives (SoilClim_AWR_2m_1980_2022.zip and mHM_SM_2m_1980_2022.zip), each containing 1570 GeoTIFF files. </p> <p>The file SoilClim_AWR_2m_1980_2022.zip contains 10-day simulations of relative available water (AWR), where 100% represents the full field capacity and 0% represents the wilting point, produced the SoilClim water balance model for the 2.0 m soil depth, covering a global nonglaciated land with a 0.5° resolution, excluding latitudes above 72° N and all of Antarctica, for the 1980–2022 period.</p> <p>The file mHM_SM_2m_1980_2022.zip contains 10-day simulations of soil moisture (SM), produced the mesoscale Hydrologic Model (mHM) for the 2.0 m soil depth, covering a global nonglaciated land with a 0.5° resolution, excluding latitudes above 72° N and all of Antarctica, for the 1980–2022 period.</p>

Figure 2 in Detection of the Trp-2027-Cys Mutation in Fluazifop-P-butyl-resistant Itchgrass (RottboelliO cochinchinensis) using High-Resolution Melting Analysis (HRMA)

Figure 2. Agarose gel (1,8%) showing polymerase chain reaction products (89 bp) of the chloroplastic acetyl-coenzyme A carboxylase gene carboxyl-transferase domain targeted by HRMA primers RottF and RottR.

Figure 5 in Detection of the Trp-2027-Cys Mutation in Fluazifop-P-butyl-resistant Itchgrass (RottboelliO cochinchinensis) using High-Resolution Melting Analysis (HRMA)

Figure 5. High-resolution melting analysis (HRMA) for detection of the Trp-2027-Cys mutation in Rottboellia cochinchinensis carboxyl-transferase domain of the acetylcoenzyme A carboxylase gene conferring resistance to fluazifop-P-butyl. (B) Normalized plot and (C) difference plot using susceptible (wild type) as the reference genotype.

Figure 4 in Detection of the Trp-2027-Cys Mutation in Fluazifop-P-butyl-resistant Itchgrass (RottboelliO cochinchinensis) using High-Resolution Melting Analysis (HRMA)

Figure 4. High-resolution melting analysis (HRMA) for detection of mutation Trp-2027-Cys in Rottboellia cochinchinensis carboxyl-transferase domain of the acetyl-coenzyme A carboxylase gene conferring resistance to fluazifop-P-butyl. Three genotypes are included: wild type (homozygous TGG, susceptible), mutant homozygous (TGC, resistant), and artificial mutant heterozygous (TGG/TGC, possibly resistant). (A) Representative profiles of the melting curves (derivative melt curves), (B) normalized plot, and (C) difference plot using susceptible (wild type) as the reference genotype.

Figure 1 in Detection of the Trp-2027-Cys Mutation in Fluazifop-P-butyl-resistant Itchgrass (RottboelliO cochinchinensis) using High-Resolution Melting Analysis (HRMA)

Figure 1. Sequence alignment showing the single-nucleotide change (G/C) within the chloroplastic acetyl-coenzyme A carboxylase gene carboxyl-transferase domain fragments from Rottboellia cochinchinensis susceptible (G) and resistant (C) biotypes. The sequences of the HRMA primers are colored in red. Position numbers (Alopecurus myosuroides full ACCase sequence, GenBank AJ310767, numbering) are given above the nucleotide sequences. Conserved nucleotides are indicated by dots.