Find research datasets worth reusing

opencc-by-4.0Dec 2011View details →

Fig. 5. High-resolution X in Osteology Of Gobiderma Pulchrum (Monstersauria, Lepidosauria, Reptilia)

Fig. 5. High-resolution X-ray computed tomography scans of Gobiderma pulchrum (IGM 3/55) with matrix rendered invisible in A, right lateral, B, left lateral, C, dorsal, and D, ventral views.

Figure 5 in The skull of the rare Malaysian snake Anomochilus leonardi Smith, based on high-resolution X-ray computed tomography

Figure 5. Three-dimensional cutaway views along the frontal axis of Anomochilus leonardi (FRIM 0026) based on HRXCT data. A, approximately 0.97 mm depth; and B, approximately 1.34 mm depth. Scale bar = 1 mm. See key for abbreviations.

Figure 4 in The skull of the rare Malaysian snake Anomochilus leonardi Smith, based on high-resolution X-ray computed tomography

Figure 4. Three-dimensional cutaway views along the sagittal axis of Anomochilus leonardi (FRIM 0026) based on HRXCT data. A, approximately 0.69 mm depth; and B, approximately 1.45 mm depth. Scale bar = 1 mm. See key for abbreviations.

Figure 3 in The skull of the rare Malaysian snake Anomochilus leonardi Smith, based on high-resolution X-ray computed tomography

Figure 3. Three-dimensional cutaway views along the transverse axis of Anomochilus leonardi (FRIM 0026) based on HRXCT data. A, approximately 0.36 mm depth; B, approximately 1.29 mm depth; C, approximately 1.58 mm depth; D, approximately 1.97 mm depth; E, approximately 5.19 mm depth; F, approximately 5.81 mm depth; and G, approximately 5.94 mm depth. Scale bar = 1 mm. See key for abbreviations.

Figure 2 in The skull of the rare Malaysian snake Anomochilus leonardi Smith, based on high-resolution X-ray computed tomography

Figure 2. Three-dimensional reconstruction of the lower jaw of Anomochilus leonardi (FRIM 0026) based on HRXCT data. A, lateral view; B, medial view; C, dorsal view; and D, ventral view. Scale bar = 1 mm. See key for abbreviations.

China's historical 1km resolution eco-environmental quality data (CHEQ)

The China's High-resolution Eco-Environmental Quality (CHEQ) dataset provides 1-km historical eco-environmental quality (EEQ) during years 2001-2019. This dataset archived in TIFF format was jointly developed by Beijing Normal University, Southern University of Science and Technology and Peking University based on multi-source remote sensing data. The dataset covers the entire land of China with a yearly temporal resolution. The existing validation results show that the CHEQ dataset was highly consistent with the environmental index (EI) provided by the Ministry of Ecology and Environment of China. The CHEQ fills the research gap in the field of EEQ monitoring in China.

Code and Data for "Multiple re-reads of single proteins at single-amino-acid resolution using nanopores"

The primary structures containing data and analysis products are peptidereads_fig2.mat (for figure 2) and peptiderereads_fig3.mat (for figure 3). The main analysis scripts for these data structures are callvariants_fig2.m and reread_analysis_fig3.m respectively. Data for figures S6 (S6_reread_data.dat) and S8 (S8_hetero_data.dat), and the analysis script used to produce figure S6 (S6_reread_analysis.m) are also included. Other files are dependencies of these main scripts.   The fields in peptidereads_fig2 are as follows:     folder, eventnum, reducedStart, reducedEnd, suspicious, hasreread: notes for internal use variant: the true identity of the single-amino-acid substitution variant data: the ion current data for each read downsampled to 5 kHz. omit: whether the read was omitted from analysis due to length relativeDNAend: the index in the data where the DNA portion of the read ends. relativeLinkerEnd: the index in the data where the linker portion of the read ends. DNAlevels, Peplevels, Alllevels: extracted ion current levels for the DNA region, the peptide region, and everything. cal: the multiplicative and additive constants applied to calibrate the read caldata: the data with calibration constants applied cons0D, cons0W, cons0G, cons0DNA: initial guesses for consensuses based on hand curation of data. pepDcons0, pepWcons0, pepGcons0: the portion of the handmade consensus with the variant levels. pepDcons, pepWcons, pepGcons: the portion of the iterated consensus with the variant levels. inhandconsensus: whether the read was used in generation of the inital guess consensuses. inconsensus: whether the read was used in generation of either the initial guess or iterated consensuses. confidence: the relative likelihood of each variant assigned to the read incalls: whether the data was used in variant calling (i.e., not used in consensus generation) params: the analysis parameters used

Reproduction package to "Phonon-Trapping Enhanced Energy Resolution in Superconducting Single-Photon Detectors"

This is a reproduction package to the paper "Phonon-Trapping Enhanced Energy Resolution in Superconducting Single-Photon Detectors", which is published in <a href="https://doi.org/10.1103/PhysRevApplied.16.034051">Physical Review Applied</a>. It contains all data and code to reproduce the figures in this paper.

opencc-zeroSep 2021View details →

dryad40/100

Data for: Tracking the temporal dynamics of insect defoliation by high-resolution radar satellite data

1. Quantifying tree defoliation by insects over large areas is a major challenge in forest management, but it is essential in ecosystem assessments of disturbance and resistance against herbivory. However, the trajectory from leaf-flush to insect defoliation to refoliation in broadleaf trees is highly variable. Its tracking requires high temporal- and spatial-resolution data, particularly in fragmented forests. 2. In a unique replicated field experiment manipulating gypsy moth Lymantria dispar densities in mixed-oak forests, we examined the utility of publicly accessible satellite-borne radar (Sentinel-1) to track the fine-scale temporal trajectory of defoliation. The ratio of backscatter intensity between two polarizations from radar data of the growing season constituted a canopy development index (CDI) and a normalized CDI (NCDI), which were validated by optical (Sentinel-2) and terrestrial laser scanning (TLS) data as well by intensive caterpillar sampling from canopy fogging. 3. The CDI and NCDI strongly correlated with optical and TLS data (Spearman's ρ=0.79 and 0.84, respectively). The ∆NCDIDefoliation (A-C) significantly explained caterpillar abundance (R2=0.52). The NCDI at critical time-steps and ΔNCDI related to defoliation and refoliation well discriminated between heavily and lightly defoliated forests. 4. We demonstrate that the high spatial and temporal resolution and the cloud independence of Sentinel-1 radar potentially enable spatially unrestricted measurements of the highly dynamic canopy herbivory. This can help monitor insect pests, improve the prediction of outbreaks, and facilitate the monitoring of forest disturbance, one of the high priority Essential Biodiversity Variables, in the near future.

Imaging Mass Cytometry Images (APP1) from: A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotyping at single-cell resolution.

Four µm-thick sections were cut from the APP1 FFPE block with a microtome and used for staining with a panel of 26 antibodies targeting the main immune, stromal and epithelial cell populations of the gastrointestinal tract (Supplementary Table 2). The optimal dilution of each antibody in the panel was identified by staining and ablating FFPE appendix sections. The resulting images were reviewed by a mucosal immunologist (J.S.) and the dilution giving the best signal to background ratio was selected for each antibody (Supplementary Table 2). To perform the staining for IMC, slides were dewaxed after a one-hour incubation at 60°C, rehydrated and heat-induced antigen retrieval was performed with a pressure cooker in Antigen Retrieval Reagent-Basic (R&D Systems). Slides were incubated in a 10% BSA (Sigma), 0.1% Tween (Sigma), and 2% Kiovig (Shire Pharmaceuticals) Superblock Blocking Buffer (Thermo Fisher) blocking solution at room temperature for two hours. Each antibody was added to a primary antibody mix at the selected concentration in blocking solution and incubated overnight at 4°C. After two washes in PBS and PBS-0.1% Tween, the slides were treated with the DNA intercalator Cell-ID™ Intercalator-Ir (Fluidigm) (containing the two iridium isotopes 191Ir and 193Ir) 1.25 mM in a PBS solution. After a 30-minute incubation, the slides were washed once in PBS and once in MilliQ water and air-dried. The stained slides were then loaded in the Hyperion Imaging System (Fluidigm) imaging module to obtain light-contrast high resolution images of approximately four mm2. These images were used to select the ROI in each slide. For APP1, a one mm2 ROI containing a lymphoid follicle in its whole depth alongside a portion of lamina propria and of epithelium was selected. ROIs were ablated at a o µm/pixel resolution and 200 Hz frequency.

opencc-zeroOct 2021View details →

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Data from: Age estimation using methylation-sensitive high-resolution melting (MS-HRM) in both healthy felines and those with chronic kidney disease

Age is an important ecological tool in wildlife conservation. However, it is difficult to estimate in most animals, including felines — most of whom are endangered. Here, we developed the first DNA methylation-based age-estimation technique — as an alternative to current age-estimation methods — for two feline species that share a relatively long genetic distance with each other: domestic cat (Felis catus; 79 blood samples) and an endangered Panthera, the snow leopard (Panthera uncia; 11 blood samples). We measured the methylation rates of two gene regions using methylation-sensitive high-resolution melting (MS-HRM). Domestic cat age was estimated with a mean absolute deviation (MAD) of 3.83 years. Health conditions influenced accuracy of the model. Specifically, the models built on cats with chronic kidney disease (CKD) had lower accuracy than those built on healthy cats. The snow leopard-specific model (i.e. the model that resets the model settings for snow leopards) had a better accuracy (MAD = 2.10 years) than that obtained on using the domestic cat model directly. This implies that our markers could be utilised across species, although changing the model settings when targeting different species could lead to better estimation accuracy. The snow leopard-specific model also successfully distinguished between sexually immature and mature individuals.

opencc-by-4.0Oct 2021View details →

High resolution Cropland Extent for Karamoja Uganda 2010/11

This dataset is a shapefile of Cropland extent for Karamoja derived for 2010/2011 from WorldView 1&2 panchromatic and multispectral data at 0.5 m and 1.8 m resolution respectively. The satellite images were acquired between 2010 and 2011 and obtained from The National Geospatial-Intelligence Agency (NGA) through an agreement with NASA under the NextView License (For information about the NextView License, please visit: http://cad4nasa.gsfc. nasa.gov/.). Description of the method to derive the map are included in the reference below. Nakalembe, C., Dempewolf, J., & Justice, C. (2017). Agricultural land use change in Karamoja Region , Uganda. Land Use Policy, 62, 2–12. https://doi.org/10.1016/j.landusepol.2016.11.029

opencc-by-4.0Mar 2022View details →

Global daily high-resolution surface carbon monoxide concentrations (2019-2020)

A brand-new framework is developed to estimate daily globally distributed surface carbon monoxide (CO) concentrations (2019-2020) at a high spatial resolution (0.05º) through data-driven fusion. Evaluation results (historical) show that the proposed framework presents a desired estimation accuracy over the globe, with the Rs/RMSEs of 0.73/0.273 ppm and 0.77/0.215 ppm at daily and monthly scales, respectively. Compared to GEOS Composition Forecasting replay  product, the proposed framework also performs distinctly better, of which the R increases by 0.3 and RMSE decreases by 0.185 ppm.

opencc-by-4.0Oct 2021View details →

MED-GOLD Indicators for the Wine pilot over Douro Valley based on High Resolution Climate projections

Indicators of interest for the Wine sector over the Douro Valley using high resolution climate projections. <ol> <li>GDD (Growing Degree Days) - summation of daily differences between daily temperature averages and 10 for the period April-October</li> <li>GST (Growing Season Temperature) - average of daily average temperatures for the period April-October</li> <li>SprR (Spring Rain) - Precipitation accumulated between 21st April-to 21st June,</li> <li>HarvR (Harvest Rain)-Precipitation accumulated between 21 August and 21 October</li> <li>SU35 -number of days with temperature higher than 35°C for the period April-October,</li> <li>WSDI (Warm Spell Duration Index) -days with at least 6 consecutive days when the daily temperature maximum exceeds its 90th percentile for the period April-October.</li> </ol> The results are based on an sub-ensemble of five RCMs from the EURO-CORDEX modelling experiment which have been statistically downscaled to 1km x1km horizontal resolution using the PTHRES gridded dataset as the reference dataset. More details can be found in Raül Marcos-Matamoros, (2018). Report on the methodology followed to implement the wine pilot services. Zenodo. https://doi.org/10.5281/zenodo.4543337 Datasets computed by National Observatory of Athens, in collaboration with SOGRAPE VINHOS S.A. in the framework of the European MED-GOLD project, funded from the European Union's Horizon 2020 Research and Innovation programme under Grant agreement No.776467

opencc-by-4.0Oct 2021View details →

Low-resolution infrared thermal dataset and potential privacy-preserving applications

A low-resolution infrared thermal dataset of people and thermal objects, such as a working laptop, in indoor environments. The dataset was collected by a far infrared thermal camera (32 by 24 pixels), which can capture the position and shape information of thermal objects without privacy issues that enable trustworthy computer vision applications. The dataset consists of 1770 thermal images with high-quality annotation collected from an indoor room with around 15 degrees.

opencc-by-4.0Jul 2021View details →

Bacteriophage T5 Structure at Atomic Resolution

Bacteriophage T5 structure at atomic resolution. This structure has been constructed in UCSF Chimera software putting together all the structures that compose bacteriophage T5 using cryoEM reconstructions and pdb structures updated to October 2025. By Dr. Victor Padilla-Sanchez, PhD from Washington Metropolitan University. Email: drvictorpadilla@aol.com

opencc-by-4.0Jul 2021View details →

Bacteriophage T7 Structure at Atomic Resolution.

Bacteriophage T7 structure at atomic resolution. This structure has been constructed in UCSF Chimera software putting together all the structures that compose bacteriophage T7 using cryoEM reconstructions and pdb structures. By Dr. Victor Padilla-Sanchez, PhD from Washington Metropolitan University. Email: drvictorpadilla@aol.com

opengpl-3.0-or-laterJan 2023View details →

A river on fiber: high resolution fluvial monitoring with distributed acoustic sensing – Data, Matlab Scripts and App

Matlab software and data associated with Roth et al. (submitted to Seismica, 2025).

opencc-by-4.0Jul 2018View details →

Sensitivity of deep ocean biases to horizontal resolution in prototype CMIP6 simulations: video supplements

These are video supplements cited in the manuscript "Sensitivity of deep ocean biases to horizontal resolution in prototype CMIP6 simulations" that has been submitted to Geoscientific Model Development (GMD; MS No.: gmd-2018-192). The animations (with a 10yr running window) show the development of temperature biases in the deep ocean over a period of 100 years in two pre-industrial configurations with the AWI Climate Model: AWI-CM-LR and AWI-CM-HR. Meridional biases along the 30.5°W transect through the Atlantic Ocean (S3 and S4; animated version of Fig.8 for LR and HR) and maps of along-isopycnal biases (sigma_1=31.8) are shown (S1 and S2). Time axes have been added compared to version 1.