Find research datasets worth reusing

opencc-by-4.0Feb 2024View details →

muBrain - a 3D volumetric reconstruction of the mid-fetal brain

<h2>File descriptions</h2> <h3>Volumes:</h3> <table> <tbody> <tr> <td>uBrain-volume.nii.gz</td> <td>microBrain template volume. A 3D reconstruction of the right hemisphere of a mid-fetal brain. Voxel size: 0.15mm.</td> </tr> <tr> <td>uBrain-atlas-labels.nii.gz</td> <td>microBrain brain tissue labels. Brain tissue labels (n=20) for the microBrain volume.</td> </tr> <tr> <td>brain-tissue-labels.txt</td> <td>LUT for brain tissue labels</td> </tr> </tbody> </table> <h3>Surfaces:</h3> <table> <tbody> <tr> <td>uBrain.R.outer.surf.gii</td> <td>outer (pial) cortical surface of the microBrain volume</td> </tr> <tr> <td>uBrain.R.inner.surf.gii</td> <td>inner (white) cortical surface of the microBrain volume</td> </tr> <tr> <td>uBrain.cortical-atlas.fetal36w-template.label.gii</td> <td>microBrain cortical atlas labels projected onto the 36w timepoint of the <a href="https://gin.g-node.org/kcl_cdb/dhcp_fetal_brain_surface_atlas">dHCP fetal surface template</a></td> </tr> <tr> <td>cortical-labels.txt</td> <td>LUT for cortical atlas labels.</td> </tr> </tbody> </table> <h3>Microarray data:</h3> <table> <tbody> <tr> <td>uBrain-processed-lmd-data.csv</td> <td>LMD microarray data from the <a href="https://www.brainspan.org/lcm/search/index.html">BrainSpan</a> atlas aligned to the microBrain cortical labels. </td> </tr> </tbody> </table>

opencc-by-4.0Jan 2023View details →

Data for: 3D in vitro modeling of the exocrine pancreatic unit using tomographic volumetric bioprinting

Abstract <div> <div> Pancreatic ductal adenocarcinoma (PDAC) is the most frequent type of pancreatic cancer, one of the leading causes of cancer-related deaths worldwide. The first lesions associated with PDAC occur within the functional units of exocrine pancreas. The crosstalk between PDAC cells and stromal cells plays a key role in tumor progression. Thus, in vitro, fully human models of the pancreatic cancer microenvironment are needed to foster the development of new, more effective therapies. However, it is challenging to make these models anatomically and functionally relevant. Here, we used tomographic volumetric bioprinting, a novel method to fabricate three-dimensional cell-laden constructs, to produce a portion of the complex convoluted exocrine pancreas in vitro. Human fibroblast-laden gelatin methacrylate-based pancreatic models were processed to reassemble the tubuloacinar structures of the exocrine pancreas and, then human pancreatic ductal epithelial (HPDE) cells overexpressing the KRAS oncogene (HPDE-KRAS) were seeded in the acinar lumen to reproduce the pathological exocrine pancreatic tissue. The growth and organization of HPDE cells within the structure was evaluated and the formation of a thin epithelium which covered the acini inner surfaces in a physiological way inside the 3D model was successfully demonstrated. Interestingly, immunofluorescence assays revealed a significantly higher expressions of alpha smooth muscle actin (α-SMA) vs. actin in the fibroblasts co-cultured with cancerous than with wild-type HPDE cells. Moreover, α-SMA expression increased with time, and it was found to be higher in fibroblasts that laid closer to HPDE cells than in those laying deeper into the model. Increased levels of interleukin (IL)-6 were also quantified in supernatants from co-cultures of stromal and HPDE-KRAS cells. These findings correlate with inflamed tumor-associated fibroblast behavior, thus being relevant biomarkers to monitor the early progression of the disease and to target drug efficacy.   </div> <div> To our knowledge, this is the first demonstration of a 3D bioprinted portion of pancreas that recapitulates its true 3-dimensional microanatomy, and which shows tumor triggered inflammation.   </div> </div>   Contents This repository contains the raw data, materials list, protocols, and code necessary to reproduce the work in the namesake preprint.

opencc-by-4.0Mar 2022View details →

Laboratory-measured and X-ray CT-derived volumetric composition of a permafrost core

This dataset contains data on the volumetric composition of a permafrost core which has been drilled in a Yedoma upland in northeast Siberia (72.36613 N, 126.27272 E) in September 2017. This dataset supplements a research article to be submitted to the scientific journal The Cryosphere. It contains the following files: volumetric_contents_sampleRes_lab+CT.csv Contains the volumetric contents of total ice, organic, and mineral measured in the laboratory at AWI Potsdam at a coarse resolution. It further contains the volumetric contents of gas, excess ice, and two sediment phases (A,B) derived from a CT scan at UFZ Halle, downsampled to the resolution of the laboratory samples. volumetric_contents_highRes_CT.csv Contains the volumetric contents of gas, excess ice, and two sediment phases (A,B) derived from a CT scan at UFZ Halle at the original resolution of 50µm. regression analysis_paper.py This pyhton script uses the above listed input files to perform and evaluate a regression analysis of the CT data against the laboratory data. The regression result is the composition of the CT-derived sediment phases (A,B) in terms of pore ice, organic, and mineral. The script furthermore computes evaluation metrics of the lab-CT comparison, and computes volumetric contents of pore ice, total ice, organic, and mineral at the high resolution of the original CT data. volumetric_contents_sampleRes_all.csv This file can be reproduced by the files listed above and contains, in addition to the data contained in volumetric_contents_sampleRes_lab+CT.csv, the volumetric contents of pore ice, total ice, mineral, and organic as predicted by the regression model at the same (coarse) resolution as the laboratory samples. volumetric_contents_highRes_all.csv This file can be reproduced by the files listed above and contains, in addition to the data contained in volumetric_contents_highRes_CT.csv, the volumetric contents of pore ice, total ice, mineral, and organic as predicted by the regression model at the same (high) resolution as the original CT data. More details can be found in the article describing the study.

opencc-by-sa-4.0Apr 2019View details →

Soil water content (volumetric %) for 33kPa and 1500kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution

Soil water content (volumetric) in percent for 33 kPa and 1500 kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Training points are based on a global compilation of soil profiles (<a href="https://ncsslabdatamart.sc.egov.usda.gov/">USDA NCSS</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, <a href="https://data.isric.org/geonetwork/srv/eng/catalog.search#/metadata/a351682c-330a-4995-a5a1-57ad160e621c">ISRIC WISE</a>, <a href="http://egrpr.esoil.ru/">EGRPR</a>, <a href="https://esdac.jrc.ec.europa.eu/content/soil-profile-analytical-database-2">SPADE</a>, <a href="https://open.canada.ca/data/en/dataset/6457fad6-b6f5-47a3-9bd1-ad14aea4b9e0">CanNPDB</a>, <a href="https://data.nal.usda.gov/dataset/unsoda-20-unsaturated-soil-hydraulic-database-database-and-program-indirect-methods-estimating-unsaturated-hydraulic-properties">UNSODA</a>, <a href="https://doi.pangaea.de/10.1594/PANGAEA.885492">SWIG</a>, <a href="http://www.cprm.gov.br/en/Hydrology/Research-and-Innovation/HYBRAS-4208.html">HYBRAS</a> and <a href="http://dx.doi.org/10.4228/ZALF.2003.273">HydroS</a>). Data import steps are available <a href="https://gitlab.com/openlandmap/compiled-ess-point-data-sets/-/tree/master/themes/sol/SoilHydroDB">here</a>. Spatial prediction steps are described in detail <a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil/soil_water">here</a>. Note: these are actually measured and mapped soil content values; no Pedo-Transfer-Functions have been used (except to fill-in the missing NCSS bulk densities). Available water capacity in mm (derived as a difference between field capacity and wilting point multiplied by layer thickness) per layer is available <a href="https://doi.org/10.5281/zenodo.2629148">here</a>. Antarctica is not included. To access and visualize some of the maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a> If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: <ul> <li>sol = theme: soil,</li> <li>watercontent.33kPa = water content (volumetric percent) under field capacity (33 kPa suction),</li> <li>usda.4b1c = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.1 = version number: 0.1,</li> </ul>

opencc-by-nc-sa-4.0Dec 2018View details →

Coarse fragments % (volumetric) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution

Coarse fragments % (volumetric) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Based on machine learning predictions from global compilation of soil profiles and samples. Processing steps are described in detail <a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a>. Antarctica is not included. To access and visualize maps use:  <a href="http://www.openlandmap.org/">OpenLandMap.org</a> If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: <ul> <li>sol = theme: soil,</li> <li>coarsefrag.vfraction = variable: coarse fragments volumetric fraction,</li> <li>usda.3b1 = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>

opencc-by-4.0Oct 2024View details →

Sparse camera volumetric video applications. A comparison of visual fidelity, user experience, and adaptability: Subjective fidelity study data

Supplementary Material to the Paper: Sparse camera volumetric video applications. A comparison of visual fidelity, user experience, and adaptability This folder contains all collected data and scripts that were used to analyze the subjective fidelity study.

opencc-by-4.0Jun 2021View details →

A Bi-atrial Statistical Shape Model and 100 Volumetric Anatomical Models of the Atria

This dataset is part of the publication "A bi-atrial statistical shape model for large-scale in silico studies of human atria: Model development and application to ECG simulations" by Nagel et al. (<a href="https://doi.org/10.1016/j.media.2021.102210">https://doi.org/10.1016/j.media.2021.102210</a>). It includes a bi-atrial statistical shape model built based on 47 MR and CT images (Left atrium segmentation challenge (Tobon-Gomez, 2015), Left atrium fibrosis and scar segmentation challenge (Karim, 2013), Left atrial wall thickness challenge (Karim, 2018)). ScalismoLab (https://scalismo.org) was used for parts of the model generation. Further Details are explained in the paper. The SSM is available as an h5 file including information about the mean shape's vertex locations and their triangulation as well as the eigenvectors and -values.  100 random instances derived from the model are available. Each zip file contains the volumetric bi-atrial geometry as vtk file, which was augmented in a post-processing step with a homogeneous wall thickness, fiber orientation, intra-atrial bridges and material tags so that they are ready to use for electrophysiological simulations of atrial signals. Furthermore, the scalar field resulting from computing the gradient of the Laplace equation with the boundary conditions described by Piersanti et al. (Modeling cardiac muscle fibers in ventricular and atrial electrophysiology simulations, Computer Methods in Applied Mechanics and Engineering, 2020, <a href="https://doi.org/10.1016/j.cma.2020.113468">https://doi.org/10.1016/j.cma.2020.113468</a>) are available on the left and the right atrial instances.  Furthermore, 95 geometries with uniformly distributed left atrial volumes are available in LAE_geometries.zip.

opencc-by-4.0Aug 2022View details →

Volumetric imaging of cellular dynamics with deep learning enhanced bioluminescence microscopy

The low photon emission of known luciferases, currently limit their widespread use as contrast agents in live cell microscopy because they demand long exposure times that are prohibitive for imaging fast biological dynamics. To increase the versatility of bioluminescence microscopy as an alternative for fluorescence microscopy, we present an improved low-light microscope in combination with deep learning methods to image extremely photon-starved samples enabling subsecond exposures for timelapse and volumetric imaging. Here, we leverage a versatile training data set for deep learning based bioluminescence microscopy including paired images of noisy and ground thruth fluorescence data of body wall muscle labeled Caenorhabditis elegans animals. These data include light-field images and their ground truth reconstructions for training a CNN for fast light fiel deconvolution.

openCC (other)Jun 2025View details →

UCSB SONGS Mitigation Monitoring: Wetland Survey - Tidal Volumetric Flow Rate

These data describe annual estimates of tidal volumetric flow rate collected as part of the SONGS San Dieguito Wetland Restoration mitigation monitoring program designed to evaluate compliance of the restoration project with conditions of the SONGS permit. Monitoring began in 2012 in the San Dieguito Wetlands in San Diego County, CA. The tidal volumetric flow rate into the wetland between low and high tide was sampled 24 times annually along a cross section transect of the main channel located 0.9 km from the inlet.

openCC (other)Nov 2019View details →

Soil volumetric water content calculated from neutron hydroprobe data along the LTER-I transects (control and fertilized) at the Jornada Basin LTER, 1986-ongoing

This data package contains volumetric water content (VWC) measurements calculated from soil neutron hydroprobe data collected on the permanent LTER-I transects located at Chihuahuan Desert Rangeland Research Center (CDRRC) in the Jornada Basin of southern New Mexico, USA. The control and treatment transects are parallel to each other and are 2.7 km in length extending from the middle of the College Playa to the foot of Mt. Summerford. The treatment transect was treated annually with ammonium nitrate fertilizer (NH4NO3 at 10g N/m2/yr) until 1987. Measurement stations are located at 30 meter intervals along each transect, and there are neutron probe access tubes located every station on the control transect (n=89) and at every fifth station at the treatment transect (n=19). Measurements were taken at 5 depths using a neutron probe (CPN Model 503DR Hydroprobe) and were then converted to VWC at 30 cm, 60 cm, 90 cm, 110 cm, and 130 cm depths. Neutron probe VWC readings taken in 3 non-weighing mini-lysimeters along each transect are also included. This dataset consists of the calculated water content (cm3 water/cm3 soil) obtained by applying site-specific calibration equations to data derived from the thermalized neutron counts found in EDI packageID knb-lter-jrn.210001001. Measurements were taken at 2 week intervals from April 1982 to 1987 and monthly thereafter. Data collection for this study is ongoing.

openCC (other)Oct 2019View details →

Soil volumetric water content calculated from neutron hydroprobe data at 15 NPP study sites at the Jornada Basin LTER, 1989-2011 (Deprecated)

This data package contains soil water content data calculated from monthly neutron hydroprobe count measurements made at 15 net primary production (NPP) study locations on Jornada Experimental Range (JER) and Chihuahuan Desert Rangeland Research Center (CDRRC) lands. Once a month, neutron probe measurements are made at 10 depths (where possible) at each of 10 access tubes at each of the 15 NPP sites using a neutron probe (CPN Model 503DR Hydroprobe). The raw dataset, also on EDI (knb-lter-jrn.210013001), consists of the count of thermalized neutrons at 30 cm depth intervals to a maximum depth of 300 cm. In this data package, the raw neutron counts have been converted to volumetric water content (VWC) to a maximum depth of 270 cm using calibration equations (deepest probe depths are excluded from VWC calculations). The NPP sites these measurements are made at represent the 5 dominant vegetation types of the Jornada Basin, which consist of 3 shrub (creosotebush, mesquite dune, and tarbush) and 2 grass (upland grassland and playa) types. Three NPP sites are located in each of the types. This data collection is ongoing with new data collected monthly. NOTE: This data package is deprecated and will not be updated in the future. These VWC values were calculated using a now-outdated calibration method. Values of VWC calculated with the improved and fully documented calibration method are available in another EDI data package (knb-lter-jrn.210013003).

openCC (other)Jun 2022View details →

Pulse-Press Project (P3): Continuous soil temperature and volumetric water content (VWC) measurements, McMurdo Dry Valleys, Antarctica (2012-2021, ongoing)

Climate warming in polar regions is associated with thawing of permafrost, resulting in significant changes in soil hydrology, biogeochemical cycling, and in the activity and composition of soil communities. While ongoing directional climate warming presses can elicit such responses over decadal time scales, their manifestation typically occurs as discrete thawing pulses. Indeed, in the McMurdo Dry Valleys of Antarctica, abrupt changes in community structure and biogeochemical cycling in terrestrial and aquatic ecosystems following a summer warming event (Jan. 2002) exceeded the influences of a decadal cooling trend in both magnitude and rate of response. Thus, we anticipate that climate-mediated permafrost changes and their associated impacts on soil communities and biogeochemical cycles may occur over seasonal time scales. The Pulse-Press Project (P3) experiment was established in 2012 as part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) program to investigate impacts of seasonal wetting on ecosystem structure and functioning by simulating different frequencies of permafrost thawing events in Antarctic permafrost soils. Since the top horizons of most Antarctic soils are dry permafrost (i.e., there is insufficient water content to generate ice-cement), with ice-cement or massive ice typically below 30 cm, permafrost thawing events are likely to result in subsurface movements of water that may manifest as groundwater seeps down gradient. The P3 experiment consists of three permanent plots situated on the south-facing hillslope above Many Glaciers Pond in Taylor Valley. Each plot is 15 m by 7.5 m with a trench on the upslope end that is used for experimental wetting events. The Press plot receives water every austral summer, the Pulse plot receives water every other austral summer, and the Control plot never receives water, serving as the ambient treament. Each plot is instrumented with a network of soil moisture and temperature sensors, positioned

Continuous soil temperature, specific conductance, and volumetric water content measurements from the F6 Active Layer Monitoring Station (ALMS01), McMurdo Dry Valleys, Antarctica (2014-2021, ongoing)

As part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) project, five Active Layer Monitoring Stations (ALMSs) were established throughout Taylor Valley, Antarctica to support new research foci around the thermal-moisture dynamics of soils that may control habitat conditions and faunal responses to seasonal and annual freezing cycles in this ecosystem. Two ALMSs were established adjacent to streams (Green Creek, Von Guerard Stream), with sensors installed through the active layer from the thalweg out to the shoreline and dry soil beyond. Two ALMSs were similarly established adjacent to water tracks (Wormherder Creek, Water Track B) that are zero-order drainages of snow and ice melt that rarely have surface flow. The remaining station was established in dry soil (F6) to serve as an ambient control. ALMSs measure soil temperature, soil moisture (as volumetric water content; VWC), and specific conductance (as electrical conductivity; EC) through the active layer (soil surface down to the frost table) at several locations from the water’s edge to dry soils. This data package contains measurements from the Active Layer Monitoring Station at F6 (ALMS01), located on the south shore of Lake Fryxell.

Continuous soil temperature, specific conductance, and volumetric water content measurements from the Wormherder Creek Active Layer Monitoring Station (ALMS02), McMurdo Dry Valleys, Antarctica (2014-2021, ongoing)

Continuous soil temperature, specific conductance, and volumetric water content measurements from the Von Guerard Stream Active Layer Monitoring Station (ALMS03), McMurdo Dry Valleys, Antarctica (2014-2021, ongoing)

Continuous soil temperature, specific conductance, and volumetric water content measurements from the Green Creek Active Layer Monitoring Station (ALMS04), McMurdo Dry Valleys, Antarctica (2014-2021, ongoing)