Find research datasets worth reusing

opencc-by-4.0Aug 2023View details →

Minimal data set for "Cohort profile: The ENTWINE iCohort Study, a multinational longitudinal web-based study of informal care"

Title: Minimal Data Set for the Reproduction of Findings in "Elayan et al., Cohort Profile: The ENTWINE iCohort Study, a Multinational Longitudinal Web-Based Study of Informal Care".   Study Summary: The data sets provided herein are derived from the ENTWINE iCohort Study, a multinational web-based cohort study employing an intensive longitudinal design. The study integrates a two-wave panel survey (baseline and 6-month follow-up) with optional weekly diary assessments. The cohort comprises caregivers and care recipients from nine countries: the United Kingdom, the Netherlands, Italy, Sweden, Israel, Germany, Greece, Poland, and Ireland. The study aimed to examine the influence of personal, psychological, social, economic, and geographic factors on caregiving experiences. Participants were eligible if they met the following criteria: 1) residency in a participating country; 2) capability to respond to surveys in English, Swedish, German, Dutch, Italian, Greek, Hebrew, or Polish; 3) access to the internet and ability to use it; 4) at least 18 years of age; 5) self-declared cognitive and physical capacity to complete the surveys; 6) either providing care to an adult (aged ≥ 18 years) with a chronic health condition, disability, or other care need, or receiving care from an adult due to similar conditions. The detailed methodology and results of the study can be found in the associated manuscript. For the complete survey questionnaires, please refer to: Morrison V, Zarzycki M, Vilchinsky N, Sanderman R, Lamura G, Fisher O, et al. A Multinational Longitudinal Study Incorporating Intensive Methods to Examine Caregiver Experiences in the Context of Chronic Health Conditions: Protocol of the ENTWINE-iCohort. Int J Environ Res Public Health. 2022;19. doi: <a href="https://doi.org/10.3390/ijerph19020821">10.3390/ijerph19020821</a>   Data files: The repository contains the following data files: <ol> <li>"cg_minimal_dataset" (available in dta, sav, rds, and xlsx formats): This is a minimal data set containing de-identified and processed data derived from the ENTWINE iCohort Caregiver Baseline Survey. The variables present in this data set are detailed in the associated codebook, "cg_minimal_dataset_codebook".</li> <li>"cr_minimal_dataset" (available in dta, sav, rds, and xlsx formats): This is a minimal data set containing de-identified and processed data derived from the ENTWINE iCohort Care Recipient Baseline Survey. The variables present in this data set are detailed in the associated codebook, "cr_minimal_dataset_codebook".</li> </ol>

opencc-by-4.0Aug 2023View details →

TEAMx-PC22 (TEAMx pre-campaing 2022) - ACINN temperature and humidity logger data set from Nafingalm

ABSTRACT This data set was collected with a network of Onset HOBO temperature and humidity data loggers of <a href="http://acinn.uibk.ac.at/">ACINN</a> at the Nafingalm, Austria, in summer 2022 in the framework of the TEAMx pre-campaign 2022 (TEAMx-PC22). The aim of TEAMx-PC22 was to test new instruments, new instrument configurations and new measurement sites to support the planning of the main TEAMx observational campaign (TOC) in 2024/2025. More details about TEAMx can be found at <a href="http://www.teamx-programme.org">http://www.teamx-programme.org</a> as well as in Serafin et al. (2020) and in Rotach et al. (2022). DATA SET DESCRIPTION 1. Location The temperature and humidity data loggers were located at five different sites at the Nafingalm in the Weer Valley, Tyrol, Austria (see table below). Four sites were over land and one in a small lake, the so-called Nafingsee. At one of these sites an automatic weather station (AWS) was operated (see <a href="https://doi.org/10.5281/zenodo.8172308">DOI: 10.5281/zenodo.8172308</a>). Logger H06 to H32 measured air temperature and air humidity at three sites on two levels (2 and about 0.3 m above ground level) and at one site on one level (2 m above ground level). Logger T01 and T02 measured land surface temperature at two sites. Logger T03 and T04 measured lake water temperature at one site on two levels (0.3 and 1.0 m below lake level). Each logger was equipped with a single temperature/humidity probe. Hence, for each level a separate logger had to be used. Therefore, each data file provided here contains data from a single logger at a single site on a single level. For reasons of redundancy, two sites were equipped with two loggers at 2 m above ground level (main logger and backup logger) in order to fill data gaps in the event of a failure of the main logger. However, data gaps did not occur. <table> <thead> <tr> <th scope="col">Location</th> <th scope="col">Latitude (°N)</th> <th scope="col">Longitude (°E)</th> <th scope="col">Altitude (m MSL)</th> <th scope="col">Parameters</th> <th scope="col">Logger names</th> </tr> </thead> <tbody> <tr> <td>north of the lake at the valley floor at the AWS</td> <td>47.215141</td> <td>11.712628</td> <td>1928</td> <td>air temperature and air humidity on two levels</td> <td>H32 (upper), H26 (lower)</td> </tr> <tr> <td>south of the lake at the valley floor</td> <td>47.212760</td> <td>11.713030</td> <td>1921</td> <td>air temperature and air humidity on two levels, surface temperature</td> <td>H06 (upper main), H07 (lower), H28 (upper backup), T01 (surface)</td> </tr> <tr> <td>in the lake at the valley floor</td> <td>47.213603</td> <td>11.712433</td> <td>1921</td> <td>lake water temperature on two levels</td> <td>T03 (upper), T04 (lower)</td> </tr> <tr> <td>on the slope</td> <td>47.208150</td> <td>11.721200</td> <td>2241</td> <td>air temperature and air humidity on two levels, surface temperature</td> <td>H08 (upper main), H09 (lower), H11 (upper backup), T02 (surface)</td> </tr> <tr> <td>at the peak</td> <td>47.202940</td> <td>11.730160</td> <td>2531</td> <td>air temperature and air humidity on one level</td> <td>H21</td> </tr> </tbody> </table> 2. Period The TEAMx-PC22 lasted from mid-May 2022 to early October 2022. However, the temperature and humidity logger data set provided here contains the period from 16 June to 12 September 2022. The time series has a measurement interval of 5 minutes and, depending on the parameter, contains both mean values and instantaneous values. 3. Instrument details Air temperature and air humidity were measured with Onset HOBO MX2302 temperature and humidity probes mounted on a pole above the surface in a RS3-B naturally aspirated multi-plate radiation shield. Land surface temperature was measured with Onset HOBO MX2201 temperature probes mounted on a pole at the surface under a home-made double-plate radiation shield. Lake water temperature was measured with Onset HOBO MX2201 temperature probes mounted on a rope under a buoy. Despite the double-plate radiation shield used to protect the land surface temperature measurements from radiation errors, such errors have to be expected, especially at low solar elevation angle in the morning and late afternoon. Therefore, use the land surface temperature data with caution. A detailed description of the sensors and parameters is provided as part of the netCDF file metadata as well as in a PDF file containing the netCDF header extracted with the Linux command ncdump -h. 4. Data file The data set is provided in multiple netCDF files together with a data description in multiple PDF files, one for each data logger (teamx_pc22_aws_nafingalm_HOBOID.nc and teamx_pc22_aws_nafingalm_HOBOID_ncdump_output.pdf). Here, HOBOID represents the logger name (see table above). 5. Analysis A first analysis of the data was performed in a Bachor thesis (Viebahn, 2023), which is available upon request from the author of this data set. 6. Contact Contact alexander.gohm(at)uibk.ac.at for any questions regarding the data set. 7. References Rotach, M. W., S. Serafin, H. C. Ward, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. D. Wekker, V. Grubi&scaron;ic, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Bailey, J.  Schmidli, C. Wittmann, G. Wohlfahrt, D. Zardi, 2022: A collaborative effort to better understand, measure, and model atmospheric exchange processes over mountains. Bulletin of the American Meteorological Society, 103, E1282–E1295. <a href="https://doi.org/10.1175/bams-d-21-0232.1">https://doi.org/10.1175/bams-d-21-0232.1</a> Serafin, S., M. W. Rotach, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. De Wekker, M. Evans, V. Grubi&scaron;ić, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Raudzens Bailey, J. Schmidli, G. Wohlfahrt, B. Zardi, 2020: Multi-scale transport and exchange processes in the atmosphere over mountains: Programme and experiment. Innsbruck University Press. <a href="https://doi.org/10.15203/99106-003-1">https://doi.org/10.15203/99106-003-1</a> Viebahn, T., 2023: Windregime und Stabilität in einem alpinen Seitental im Sommer: Eine Standortcharakterisierung im Rahmen der TEAMx Vorkampagne 2022. Bachlor thesis, University of Innsbruck, 75 pp.

opencc-by-4.0Aug 2023View details →

PANDEM-2 European COVID-19 training data set

The PANDEM-2 COVID-19 European training dataset is a large collection of time series either of real or realistic synthetic (generated) data and indicators associated with the European pandemic response to the COVID-19 pandemic. It is intended to be used for training in pandemic management.   This dataset is the result of a data gathering requirement process for pandemic management involving feedback and inputs from several public health and first responder professionals as well as researchers and military personnel directly involved in the European COVID-19 pandemic response. This work is part of the PANDEM-2 project funded by the Horizon 2020 Secure Societies program. To collect this data, an open source software was developed named PANDEM-Source allowing reproducibility and customisation of this dataset.    The dataset includes indicators for cases, deaths, hospitalisation, testing and laboratory data including pathogen genomic information, vaccination, non-pharmaceutical interventions, participatory surveillance, social media, flights resources (human and material such as beds or vaccines), and contact tracing activities. When no open available data was found, realistic synthetic data and indicators were generated with the goal of producing a data set to be used for pandemic management  training.  The project received funding from the European Union’s Horizon 2020 Research and Innovation programme under the Grant Agreement No. 883285. The material presented and views expressed here are the responsibility of the author(s) only. The EU Commission takes no responsibility for any use made of the information set out. References

opencc-by-4.0Oct 2023View details →

OneNet Portuguese demonstration - Open Data sets

File containing the open data sets from the Portuguese demonstration of the OneNet project. The file includes the flexibility assets data used for the demonstration, as well as: 1) the data series for the estimation of the accumulated flexibility potential of MV customers (supermarkets) connected at the two substations considered; 2) the consumption and generation forecasts, with generation disaggregated by source; 3) short-circuit current values calculated at the EHV/HV interface level, including the TSO contribution, the DSO contribution and the joint TSO-DSO contribution. Scope/objective of the demonstration: Test an optimized procedure for data exchange between the Portuguese DSO and TSO for flexibility and operational planning purposes.

opencc-by-4.0Jun 2023View details →

The NANOGrav 15-Year Data Set

<pre>The NANOGrav 15-Year Data Set Public release "v2.1.0" 2025/07/17 OVERVIEW -------- This file contains "narrowband" and "wideband" TOAs and timing solutions for the NANOGrav 15-year data set, covering data taken from 2004 to mid-2020 using Arecibo, the Green Bank Telescope (GBT), and the Very Large Array (VLA) with ASP/GASP and PUPPI/GUPPI/YUPPI backend instrumentation. The observations, data reduction, and analysis procedures used to produce these data are described in detail in the accompanying paper, "The NANOGrav 15-year Data Set: Observations and Timing of 68 Millisecond Pulsars" (Agazie et al., 2023, ApJL 951 L9, DOI 10.3847/2041-8213/acda9a, arXiv:2306.16217).   This release is available at Zenodo (DOI 10.5281/zenodo.16051178). You can also reference all versions (DOI 10.5281/zenodo.7967584).   All *.par and *.tim files are ASCII and are formatted for use with standard pulsar timing packages such as tempo2 and PINT, except for profile template files which are FITS format (narrowband) or python pickle files (wideband). For our narrowband results, we have included ASCII space-separated tables of our post-fit timing model residuals for all of our pulsars  (both un-whitened and whitened). These are available as both full  and epoch-averaged formats.   Correlation matrix files are available in three formats (*.txt, *.npz, and *.hdf5). See "description.txt" in both the narrowband and wideband ./correlations subdirectories for more details about these files. Questions about the contents of this data set can be addressed to Joe Swiggum (swiggumj@gmail.com) or comments@nanograv.org. DIRECTORY AND FILE STRUCTURE (FURTHER DETAILS BELOW) --------------------------------------------------- ./README This file. ./clock Files for tracing observatory-measured TOAs to clock standards. ./narrowband Directory containing the narrowband data set. Details are provided in README.narrowband in that directory. ./wideband Directory containing the wideband data set. Details are provided in README.wideband in that directory. ./correlations Directory containing the correlation matrix files for both the narrowband and wideband data. Details are provided in the /wideband/ and /narrowband/ subdirectories' description.txt files. SOFTWARE -------- This data set requires up-to-date installations of PINT or tempo2. Our original analysis used PINT v0.9.1 and tempo2 v2022.01.1. Up to date versions of these packages, as well as usage information and documentation can be found at the following repositories: PINT https://github.com/nanograv/PINT tempo2 https://bitbucket.org/psrsoft/tempo2 Note that we do not guarantee complete/correct functionality of these timing models in the older original tempo software package. Please also ensure that the clock files you are using cover the full range of the data set. Using the provided clock files (see below) will ensure this. All models included here are based on a generalized least squares (GLS) fit that includes a noise model with covariance between TOAs (ECORR/jitter parameters, if narrowband; RNAMP/RNIDX red noise parameters, if significant), as well as "traditional" EQUAD and EFAC parameters. Additional EFAC parameters for the wideband DM measurements are also included. All noise model parameters are included in the par files. CLOCK FILES ----------- The clock files used for our analysis are provided in the clock/ subdirectory. While the standard files distributed with tempo and tempo2 should be consistent with the clock files provided in the current release at the time of writing, this may be a source of inconsistent results in the future. Please see ./clock/README.clock directory for installation instructions. PLANNED REVISIONS -----------------   The initial release of the data set contained all fundamental data products needed for pulsar timing analysis:  Times of arrival (.tim files), timing models (.par files), standard template profiles, clock correction files, and noise modeling MCMC chains. In v2, parameter correlation matrices were added, as well as alternate versions of timing model parameter files ("NoRedNoise" and "predictive"). In v2.1,  post-fit timing residuals for our narrowband data set were made available.  A future release will add a number of other useful derived products as  mentioned in the paper, including dispersion measure time series. CHANGE LOG ---------- 2025/07/17     Addition of the Timing Model Residual files for the      narrowband dataset, including overview plots (v2.1.0). 2023/09/19 Addition of "NoRedNoise" and "predictive" par files, correlation matrices, noise modeling chains (v2). 2023/07/01 Correction to tar.gz directory structure (v1.0.1). 2023/06/28 Initial public release (v1).</pre>

openCC (other)Apr 2024View details →

REU data set from summer of 2022. Project was designed to understand how crayfish (Faxonius rusticus) respond to chemical cues from largemouth bass predators under different shelter distributions.

Research into predator–prey interactions has focused on the landscape of fear and nonconsumptive effects that result from prey responses. Prey behavior is influenced by predator presence and the location and quality of foraging resources in habitats. These areas have been fruitful, but the role of prey refuges has lagged. We investigated how refuge spatial distribution and quality influence prey behavior. To determine the role of the landscape of safety (LOS) in prey decision-making, we altered spatial relationships between refuges, refuge quality, and predation threats in mesocosms. Mesocosms were constructed such that prey only received predatory chemical cues. We employed a behavioral assay including largemouth bass (Micropterus salmoides (Lacepède, 1802): predator) and virile crayfish (Faxonius rusticus (Girard, 1852): prey). Crayfish shelter use was significantly influenced by quality and spatial relationship of shelters to predatory threats, and the interaction of these two factors. Particularly, crayfish used high-quality shelters more often when located closer to predatory cues than farther away and did not use low-quality shelters more than controls. High-quality shelter usage decreased as threat level (measured by gape ratio) decreased. These results support the idea that prey utilize an LOS, and information contained in these two landscapes may alter behavioral decisions.

openCC0Sep 2022View details →

Global data set of long-term summertime vertical temperature profiles in 153 lakes

Climate change and other anthropogenic stressors have led to long-term changes in the thermal structure, including surface temperatures, deepwater temperatures, and vertical thermal gradients, in many lakes around the world. Though many studies highlight warming of surface water temperatures in lakes worldwide, less is known about long-term trends in full vertical thermal structure and deepwater temperatures, which have been changing less consistently in both direction and magnitude. Here, we present a globally-expansive data set of summertime in-situ vertical temperature profiles from 153 lakes, with one time series beginning as early as 1894. We also compiled lake geographic, morphometric, and water quality variables that can influence vertical thermal structure through a variety of potential mechanisms in these lakes. These long-term time series of vertical temperature profiles and corresponding lake characteristics serve as valuable data to help understand changes and drivers of lake thermal structure in a time of rapid global and ecological change.

openCC (other)Jan 2020View details →

Biogeochemistry data set for soil waters, streams, and lakes near Toolik on the North Slope of Alaska.

Data file describing the biogeochemistry of samples collected at various sites near Toolik Lake, North Slope of Alaska. Sample site descriptors include a unique assigned number (sortchem), site, date, time, depth, distance (downstream), elevation, treatment, date-time, category, and water type (lake, surface, soil). Physical measures collected in the field include temperature (water, soil, well water), conductivity, pH, average thaw depth, well height, discharge, stage height, and light (lakes). Chemical analysis for the sample include alkalinity; dissolved organic carbon (DOC); inorganic and total dissolved nutrients (NH4, PO4, NO3, TDN, TDP); particulate carbon, nitrogen, and phosphorus (PC, PN, and PP); cations (Ca, Mg, Na, K); anions (SO4 and Cl); silica and oxygen.

openCC (other)Oct 2019View details →

Stationary camera observations, set, and environmental data from Shark Bay Marine Park, Western Australia from July 2011 to June 2012

This dataset provides information on stationary video cameras set within the study area from 2011 to 2012, including animals viewed along with relevent environmental and camera data. These data provide insight into teleost communities that utilize various habitats within Shark Bay.

openCC (other)Oct 2019View details →

Fish trap catch, set, and environmental data from Shark Bay Marine Park, Western Australia from May 2010 to July 2012

This dataset provides information on fish traps set within the study area from 2010 to 2012, including animals caught, relevent environmental and trap data, animal specific measurements and logging of samples retained. Additionally the dataset contains stable isotope values for individuals that were retained for Stable Isotope Analysis in addition to stomach content data. These data provide insight into teleost communities that utilize various habitats within Shark Bay with further insights into their trophic relationships.

openCC (other)Nov 2023View details →

Regeneration after Hurricane Hugo, woody species > 10 cm tall (9Ha grid, El Verde) (9Ha Plots Small Data Set)

The purpose of this data set is to document vegetation damage and recovery following Hurricane Hugo, a borderline category 3-4 hurricane with winds from 130 to 160mph (110kts) and a pressure of 945 to 946.1 mb, which hit Puerto Rico in September 18th, 1989 Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.

opencc-by-4.0Nov 2019View details →

MACREL software benchmark data set: Simulated metagenomes with sequencing quality, errors profile and abundance distributions derived from real samples

These metagenomes were used in the benchmarking of FACS pipeline, and were designed after NGLess benchmark dataset (doi.org/10.5281/zenodo.2560288).  Metagenomes were simulated with <a href="https://www.niehs.nih.gov/research/resources/software/biostatistics/art/index.cfm">ART-bin-MountRainier-2016.06.05</a> using real abundance profiles (.abund files) available <a href="https://doi.org/10.5281/zenodo.2560288">elsewhere</a>, and <a href="http://progenomes1.embl.de/data/repGenomes/representatives.contigs.fasta.gz">proGenomes' representative contigs</a> as reference genomes. There are available metagenomes with 40, 60 and 80 M (million of reads) based in the reference genomes and abundances of the following samples: <pre><code>SAMEA2466916 SAMEA2466953 SAMEA2466965 SAMEA2621107 SAMEA2621229 SAMEA2621247</code></pre> To convert them from the CRAM format back to fastq files: <pre><code> ## 1. converting from cram to bam format: samtools view -b -T refgenome.fa -o file.bam file.cram ## 2. sorting the bam file: samtools sort -n file.bam -o input_sorted.bam # sort reads by identifier-name (-n) ## 3. converting from bam to fastq format: bedtools bamtofastq -i input_sorted.bam -fq output_r1.fastq -fq2 output_r2.fastq </code></pre>

opencc-by-4.0Dec 2019View details →

Predicting evaporation from mountain streams -- data set

These files constitute the data sets used for the analysis, and generation of figures and tables reported in the manuscript titled "Predicting evaporation from mountain streams" by Andras J. Szeitz and R. Dan Moore. The manuscript was submitted for publication in the journal 'Hydrological Processes'.

opencc-by-4.0Jan 2020View details →

Climate reconstructions for the SMPDSv1 modern pollen data set

The dataset contains estimates of three bioclimatic variables at modern pollen sites from the SMPDSv1 modern pollen data set (Harrison, 2019). The bioclimatic variables are mean temperature of the coldest month (MTCO), growing degree days above 0°C (GDD0), and an annual Moisture Index, defined as the ratio of annual precipitation to annual potential evapotranspiration (MI). Estimates of these bioclimatic variables were derived using geographically-weighted regression of gridded climate data in order to correct for elevation differences between each pollen site and the corresponding grid cell. The climatological data (mean monthly temperature, precipitation, and fractional sunshine hours) were derived from the CRU CL v2.0 gridded dataset of modern (1961-1990) surface climate at 10 arc minute resolution (~18 km) (New et al., 2002).Geographically- weighted regression (GWR) was carried out in ArcGIS (v10.3, ESRI, 2014). A fixed bandwidth kernel of 1.06 ° (~140km) was used in the GWR because this optimized model diagnostics and reduced spatial clustering of residuals relative to other bandwidths. The climate of each pollen site was then estimated based on its longitude, latitude, and elevation. MTCO was taken directly from the GWR regression. GDD0 were estimated from daily data using a mean-conserving interpolation of the monthly mean temperatures. MI was calculated for each pollen site using code modified from SPLASH v1.0 (Davis et al., 2017) based on daily values of precipitation, temperature and sunshine hours again obtained using a mean-conserving interpolation of the monthly values of each.

opengpl-3.0Jan 2020View details →

PetrocShelley/Measured-solid-state-and-sub-cooled-liquid-vapour-pressures-of-nitroaromatics-using-KEMS-Data-Set: Measured-solid-state-and-sub-cooled-liquid-vapour-pressures-of-nitroaromatics-using-KEMS-Data-Set

All data files for the Measured solid state and sub-cooled liquid vapour pressures of nitroaromatics using Knudsen effusion mass spectrometry by Shelley et al.

opencc-by-4.0Jan 2020View details →

InterFlex WP3 data set_ SGAM diagrams_interface data base_service identification

This data set contains the InterFlex demonstration use case descriptions in the form of SGAM diagrams as well as the interface data base which was used for different deliverables and the repective results within work package 3 "Impact and deployment analysis of the innovative solutions". There has also been one publication in this regard ( <a href="https://doi.org/10.1109/INDIN.2018.8472053">10.1109/INDIN.2018.8472053</a>) Furthermore, it includes the service mappings for the InterFlex (under GA 731289) demonstrators as an input for different WP3 3.1 subtasks, deliverbale (D3.2) as well as a scientific publication (ICRERA 2019, ID 239, online ISSN: 2572-6013)

opencc-by-4.0Feb 2020View details →

A Wi-Fi Channel State Information (CSI) and Received Signal Strength (RSS) data-set for human presence and movement detection

This data-set consists of antenna-wise received signal strength (RSS) and channel state information (CSI) data. Both types of data have been captured using the <a href="https://dhalperi.github.io/linux-80211n-csitool/">Intel CSI Tools</a>. The RSS data have been used in our paper "Detecting Human Movement from Ambient Wi-Fi Signal Strength". This release extends the README with a data dictionary for the annotations. We hope to add more information about the data acquisition process (e.g., data acquisition protocols).

opencc-by-4.0Mar 2020View details →

RNAPosers: Machine Learning Classifiers For RNA-Ligand Poses [Data Set]

<ul> <li>This dataset contains the decoys poses used to train and test RNAPosers, a set of RNA-ligand pose classifiers.</li> <li>The folder of each RNA-ligand complex (identified using its PDB ID) contains: <ul> <li>Ligand SMILES: lig.smi</li> <li>Ligand coordinate: lig.sd</li> <li>Receptor coordinate: receptor.mol2</li> <li>Pose coordinates: poses.sd</li> <li>Pose similarity data: rmsd.txt</li> </ul> </li> </ul>

opencc-by-4.0Apr 2020View details →

Highly multiplexed histology reveals phenotypic and spatial characteristics of human Innate Lymphoid Cells in chronic inflammation - MELC tonsil data-set

53 marker MELC Run in human tonsil. Each image depicts the same field of view, sequentially stained with the depicted fluorescence-labelled antibodies. Images contain 2048 x 2048 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have not been normalized and intensities have not been adjusted.