cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17044/scilifelab.13567355.v2
title: COMMUNITY study

description: The COMMUNITY study is a longitudinal cohort study including 2149 healthcare workers and 118 COVID-19 patients. <br>Dataset includes:1. Serological data at baseline April-May 2020 and at four-month follow-up.<br>2. Self-reported symptoms compatible with COVID-19 since 1 January 2020, occupation, work location and exposure to patients infected with COVID-19 prior to blood sampling (healthcare workers). <br>3. Clinical data including co-morbidities, laboratory data, disease severity, on-going medications, demography (COVID-19 patients).<br><br><b>Abstract</b>The COMMUNITY study is a large longitudinal cohort study including 2149 healthcare workers and 118 hospitalized COVID-19 patients. The aim is to obtain a deeper understanding of the generation and dynamics of SARS-CoV-2 immune memory (humoral and cellular). We also aim to map and investigate virus and immune-mediated persistent symptomatology, so called long-COVID, and susceptibility/resistance to SARS-CoV-2. Study inclusion was initiated April 2020, and serial blood samples are obtained every four months. 19% of healthcare workers had been infected with COVID-19 at study inclusion, and the vast majority reported no or mild symptoms, thus representing the portion of society generating the most transmission. The cohort, including the extensive database, allows for a comprehensive mapping of long-COVID which can be used to help target early interventions aimed at capturing, preventing and treating this condition.<br><b>Publications</b><b> </b><b>SARS-CoV-2 exposure, symptoms and seroprevalence in healthcare workers in Sweden. </b>Nat Commun 2020. (2020-10-08) https://doi.org/10.1038/s41467-020-18848-0<br><b>SARS-CoV-2 induces a durable and antigen specific humoral immunity after asymptomatic to mild COVID-19 infection.</b> (2021-01-04) DOI: https://doi.org/10.1101/2021.01.03.21249162<br><b>Circulating Markers of Neutrophil Extracellular Traps Are of Prognostic Value in Patients With COVID-19. </b>Arterioscler Thromb Vasc Biol. 2020. (2020-12-03) 10.1161/ATVBAHA.120.315267<br><b>Patients With COVID-19 Have Elevated Levels of Circulating Extracellular Vesicle Tissue Factor Activity That Is Associated With Severity and Mortality. </b>Arterioscler Thromb Vasc Biol. 2020. (2020-12-03) 10.1161/ATVBAHA.120.315547 <b>Elevated factor V activity and antigen levels in patients with COVID-19 are related to disease severity and 30-day mortality. </b>Am J Hematol 2020. (2021-01-04) https://doi.org/10.1002/ajh.26085<br> <b>Development and external validation of a logistic regression derived formula based on repeated routine hematological measurements predicting survival of hospitalized Covid-19 patients</b>. (2020-12-22)<b> </b>https://doi.org/10.1101/2020.12.20.20248563<br>
DOI: 10.7483/opendata.cms.bcdw.kmtf
title: /TprimeBToTH_TLep_Hbb_LH_MT2400_MH500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17044/scilifelab.12771614
title: HASTE miniTEM Example Images

description: Series of images of a sample of kidney tissue captured from the miniTEM (a 25keV transmission electron microscope) at Vironova offices in Stockholm (2019-02-04), for purposes of testing software related to the HASTE project (http://haste.research.it.uu.se/).<br>The dataset consists of 759 8-bit greyscale PNG images.<br><br>Used in this publication (and others):<br>Resource- and Message Size-Aware Scheduling of Stream Processing at the Edge with application to Realtime MicroscopyBen Blamey, Ida-Maria Sintorn, Andreas Hellander, Salman Toorhttps://arxiv.org/abs/1912.09088<br>Also"Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit, <em>GigaScience</em>, Volume 10, Issue 3, March 2021, giab018, https://doi.org/10.1093/gigascience/giab018<br>
DOI: 10.7483/opendata.cms.tc64.tu3d
title: /TprimeBToTH_TLep_Hbb_LH_MT2400_MH50_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.cflm.36xz
title: /TprimeBToTH_TLep_Hbb_LH_MT2400_MH50_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17632/gc6bx573rj.1
title: Numerical modeling of water dynamics in Railway embankments

description: This dataset contains input parameters for numerical modeling using HYDRUS to simulate water flow under railway embankments. It was generated to investigate water flow dynamics and distribution in railway embankments, with implications for the transport of herbicides affecting tracks and underlying structures. Numerical experiments using a univariate approach assessed how embankment and site-specific factors influence water content and flow patterns. Key parameters included hydraulic properties (saturated/residual water content, Van Genuchten parameters, and hydraulic conductivity), recharge rate, groundwater level, horizontal impermeable layers, and embankment thickness. The data for the reference model was based on drilling information from the funder and the hydraulic parameters estimated with the help of ROSETTA (Hydrus tool). The results were later visualized and post-processed in ParaView by importing the VTK files from Hydrus. Analysis of percolation fronts and flow paths identified groundwater levels and the Van Genuchten parameter (α) as the most influential factors. 

The simulations mimicked soil with and without moisture conditions (i.e. 30 days of average recharge rates in Germany). All the folders contain the input files for each scenario for the soil moisture conditions. To run the simulations without soil moisture conditions, exchange the file Atmosph.in for the file in the folder amts_dry. Brief description of the following parameters that were inspected (values of the parameters are written on the file name):
1. α (Van Genuchten Shape Parameter) - (1/m) controls the air entry value of the soil-water retention curve. 
2. 𝑛 (Van Genuchten Pore-Size Distribution Parameter) - (dimensionless) defines the pore-size distribution's impact on the soil-water retention curve. 
3. IMP - lower permeable Layer refers to a less permeable horizontal layer introduced at multiple depths (m) in the simulation domain.
4. GW - Groundwater Levels (m) indicates the depth or position of the groundwater table relative to the embankment structure.
5. qs - The saturated water content (dimensionless) represents the maximum volumetric water content of the soil, corresponding to full saturation. 
6. qr - The residual water content (dimensionless) indicates the volumetric water content below which water movement ceases due to adhesion forces. 
7. Kf - Saturated hydraulic conductivity (m/s) measures the soil's ability to transmit water under fully saturated conditions. 
8. EMB - Embankment Thickness (m) defines the height or vertical extent of the embankment structure. It directly influences the overall storage capacity and the flow path lengths within the embankment, affecting water content distribution and hydraulic response.
DOI: 10.17044/scilifelab.14207462
title: SPARC single-cell protein data

description: This data set contains protein measurements from single cell and 100 cells from early human neural differentiation cells. Cells were harvested at time points 0 h, 24 h and 48 h.<br>Protein data measurements were carried out using PEA and quantitative values on protein concentrations were measures using qPCR. Following the completion of the qPCR run we visually inspected the amplification curves. Samples showing evidence of failed or poor amplification reactions were excluded from further analysis. Next, the raw Cq data (log 2 scale) was exported and processed. First, samples were excluded if no signal was detected in any of control assays (extension control, incubation control, or detection control), or if the signal in any of the controls was greater or less than 2 standard deviations (SD) of the mean value across all samples measured on a 96.96 IFC Biomark chip. Next, the remaining Cq values were normalized for intra-plate variation with the extension control (Cqassay—CqExtCtl) yielding dCq values. Then, for each assay, the dCq values were subtracted from the negative control computed as the lysis buffer mean + 2 × SD. This ensures that observed signals for each assay in the presence of a cell are at least 2 SDs away from any signals observed in the absence of any antigen. Resulting values below zero were set to zero and the signal was deemed undetected. The cumulative protein sum was calculated by summing across all proteins measured (<i>n</i> = 92) per cell. <br><br>
DOI: 10.17044/scilifelab.14207462.v1
title: SPARC single-cell protein data

description: This data set contains protein measurements from single cell and 100 cells from early human neural differentiation cells. Cells were harvested at time points 0 h, 24 h and 48 h.<br>Protein data measurements were carried out using PEA and quantitative values on protein concentrations were measures using qPCR. Following the completion of the qPCR run we visually inspected the amplification curves. Samples showing evidence of failed or poor amplification reactions were excluded from further analysis. Next, the raw Cq data (log 2 scale) was exported and processed. First, samples were excluded if no signal was detected in any of control assays (extension control, incubation control, or detection control), or if the signal in any of the controls was greater or less than 2 standard deviations (SD) of the mean value across all samples measured on a 96.96 IFC Biomark chip. Next, the remaining Cq values were normalized for intra-plate variation with the extension control (Cqassay—CqExtCtl) yielding dCq values. Then, for each assay, the dCq values were subtracted from the negative control computed as the lysis buffer mean + 2 × SD. This ensures that observed signals for each assay in the presence of a cell are at least 2 SDs away from any signals observed in the absence of any antigen. Resulting values below zero were set to zero and the signal was deemed undetected. The cumulative protein sum was calculated by summing across all proteins measured (<i>n</i> = 92) per cell. <br><br>
DOI: 10.7483/opendata.cms.c7ao.qqj9
title: /TprimeBToTH_TLep_Hbb_LH_MT2400_MH75_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17044/scilifelab.14222692
title: IPR0220 - InterPepRank set

description: Peptide-protein interactions between a smaller or disordered peptide stretch and a folded receptor make up a large part of all protein-protein interactions. A common approach for modelling such interactions is to exhaustively sample the conformational space by fast-fourier-transform docking, and then refine a top percentage of decoys. Commonly, methods capable of ranking the decoys for selection in short enough time for larger scale studies rely on first-principle energy terms such as electrostatics, Van der Waals forces, or on pre-calculated statistical pairwise potentials.<br><br> InterPepRank is a machine-learning based method for peptide-protein complex scoring and ranking, which encodes the structure of the complex as a graph; with physical pairwise interactions as edges and evolutionary and sequence features as nodes. The graph-network is trained to predict the LRMSD of decoys by using edge-conditioned graph convolutions on a large set of peptide-protein complex decoys.<br>Here we present the complete dataset used to train InterPepRank. The set contains 679 receptor-peptide pairs, each pair has 50 different peptide conformations docked by 70000 different rotations. in total 2.5 billion conformations. This is too large to be distributed as flat files. As such, the dataset is distributed as a set of ft-files describing which rotations and translations to apply to the corresponding peptide ligands to generate decoy poses docked to the receptor structures. To generate these structures, the apply_ftresult_improved.py script is available.<br>In addition it also contains a set of apo and holo models that was used to benchmark unbound docking.<br>All files and scripts are given as-is with no warranty.
DOI: 10.17632/gkyrfn7dxt.1
title: Supplementary data

description: This data contains mean, SD, minimum, and maximum values. Biomarkers level with number of patients.
DOI: 10.17044/scilifelab.14222692.v1
title: IPR0220 - InterPepRank set

description: Peptide-protein interactions between a smaller or disordered peptide stretch and a folded receptor make up a large part of all protein-protein interactions. A common approach for modelling such interactions is to exhaustively sample the conformational space by fast-fourier-transform docking, and then refine a top percentage of decoys. Commonly, methods capable of ranking the decoys for selection in short enough time for larger scale studies rely on first-principle energy terms such as electrostatics, Van der Waals forces, or on pre-calculated statistical pairwise potentials.<br><br> InterPepRank is a machine-learning based method for peptide-protein complex scoring and ranking, which encodes the structure of the complex as a graph; with physical pairwise interactions as edges and evolutionary and sequence features as nodes. The graph-network is trained to predict the LRMSD of decoys by using edge-conditioned graph convolutions on a large set of peptide-protein complex decoys.<br>Here we present the complete dataset used to train InterPepRank. The set contains 679 receptor-peptide pairs, each pair has 50 different peptide conformations docked by 70000 different rotations. in total 2.5 billion conformations. This is too large to be distributed as flat files. As such, the dataset is distributed as a set of ft-files describing which rotations and translations to apply to the corresponding peptide ligands to generate decoy poses docked to the receptor structures. To generate these structures, the apply_ftresult_improved.py script is available.<br>In addition it also contains a set of apo and holo models that was used to benchmark unbound docking.<br>All files and scripts are given as-is with no warranty.
DOI: 10.17044/scilifelab.14229410
title: PRONMED Uppsala COVID-19 ICU Biobank

description: The dataset consists clinical data and biobankes samples from 250 critically ill COVID-19 patients admitted to intensive care at Uppsala University Hospital.<br>During intensive care patients are sampled during weekdays. Wholeblood is collected for DNA sequencing. EDTA- and citrate-plasma, with corresponding cell pellets as well as urine is collected and frozen at -80°C until further use. In addition, PBMCs, PAX tubed for RNA-stabilized blood, and CyTOF tubes for single cell proteomics are collected and frozen. At follow-up after 3-6 months and after 1 year from discharge the corresponding convalescent samples are collected and frozen. All included patients, or next of kin sign an informed consent at the inclusion in the study making it possible to use the samples directly for studies focusing on the genetic, physiological and cellular mechanisms behind the disease and to share samples and data with collaborators both nationally and internationally.<br>Any researcher interested in samples can contact the study to discuss their idea and sample requirements, whereafter material transfer agreements and data transfer agreements can be drafted. <br>For all patients there is detailed clinical information on underlying disease, disease severity, treatment while in ICU and remaining symptoms and physiology at follow-up after 3-6 months and after one year. The dataset includes all routine biochemistry from one year before admission to the ICU until follow-up. There is diagnostic virus quantification for most patients, antibody titers of IgG, IgA and IgM for the SARS-COV-2 spike protein on most patients, and multiplex data on plasma cytokine expression at admission in most patients. In addition all patients have been genotyped as part of the COVID-19 Host Genetics Initiative and most patients have Whole Exome Sequencing data.<br>Clinical patient data are collected in an electronic Case Report Form (eCRF) and stored in a pseudoanonymized database. Genetic and other datasets are identified through the same pseudoanonymization scheme. Any researcher interested in individual data can contact the study to discuss their idea and data requirements, whereafter data transfer agreements can be drafted.<br><br><i>Published articles</i><br>2021Lipcsey et al. 10.3389/fimmu.2021.627579<i></i>Zhou et al. https://doi.org/10.1038/s41591-021-01281-1Hultström et al. https://doi.org/10.1186/s13054-021-03461-4Ahlström et al.https://doi.org/10.1111/aas.13781Luther et al. 10.1111/aas.13746<br><br>2020Bülow Anderberg et al.10.1016/j.cyto.2020.155389Asif et al. https://doi.org/10.1186/s13054-020-03362-yFrithiof et al.10.1186/s13054-020-03302-wStattin et al. 10.1016/j.jcrc.2020.08.026Hultström et al.10.1186/s13054-020-03223-8Eriksson et al.10.1055/s-0040-1715835Hultström et al.10.1097/HJH.0000000000002531 <i>For full list see publications incl. preprints see authors Michael Hultström, Miklós Lipcsey, and Robert Frithiof in</i><br><i>https://publications-covid19.scilifelab.se/</i>
DOI: 10.17632/87334kb4hz.1
title: MATLAB files for the paper "Direct Sequential Generation of Optimal Eigenvectors of the Symmetric Kernel of the Discrete Hankel Transform"

description: A set of contributed MATLAB functions pertaining to the paper "Direct Sequential Generation of Optimal Eigenvectors of the Symmetric Kernel of the Discrete Hankel Transform"
DOI: 10.48579/pro/op0rto
title: Collection of rasters and social ecological indicators for Zone Atelier Brest Iroise

description: Open, world wide rasters (biogeoregions, land surface temperature, normalised difference vegetation index, population density and land cover) are cropped with respect to the polygon of the Zone Atelier Environmental Urban so as to compute indicators. This dataset contains the shapefile, rasters for variables BIOG (biogeoregion), LSTD (land surface temperature day), LSTN (land surface temperature night), NDVI (normalized difference vegetation index), GPW4 (population density), and LC (land cover) cropped with respect to the shapefile, and social ecological indicators for these variables computed as averages or quantiles.
DOI: 10.7483/opendata.cms.0ooc.pk0o
title: /TprimeBToTH_TLep_Hbb_LH_MT2400_MH75_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17044/scilifelab.14256317
title: Dataset of SARS-CoV-2, influenza A and influenza B virus content in wastewater samples from wastewater treatment plants in Sweden

description: This is a metadata record for a continuously updated dataset of SARS-CoV-2 RNA data in wastewater in Uppsala. 
The dataset is part of a research study led by associate professor Anna J. Székely (SLU, Swedish University of Agricultural Sciences) and her research groups in collaboration with Uppsala Vatten. The research group is part of the Environmental Virus Profiling Research Area of the SciLifeLab National COVID-19 Research Program.
The data is generated by weekly SARS-CoV-2, influenza A and influenza B virus measurements in wastewater samples from Sweden. The monitoring started in Uppsala in August 2020, while other places joined the program later. For all places, raw, untreated wastewater samples representative of a single day (24 hours) are collected by flow compensated samplers. All measurements represent only 1 day except for Uppsala, where since week 16, 2021 the measurements represent 1 week as samples are collected each day and then combined flow-proportionally into 1 composite weekly sample.
The samples are processed according to standard methods. Briefly, the viral genomic material is concentrated and extracted by the direct capture method using the Maxwell RSC Enviro TNA kit (Promega) and the copy number of SARS-CoV-2 genomes is quantified by RT-qPCR using the CDC RUO nCOV N1 assay (IDT DNA). To correct for variations in population size and wastewater flow, we also quantify the pepper mild mottle virus (PMMoV) which is the most abundant RNA virus in human feces and serves as an estimator of human fecal content (Symonds et al., 2019). For more about the evaluation of this normalization method, please consult the corresponding publication: Isaksson et al. (2022). The data is presented on the graph as the ratio of the copy numbers measured by the N1 and PMMoV-assays multiplied by 10^4. As N1 copy number is a proxy for SARS-CoV-2 virus content in the wastewater and PMMoV is a proxy of the fecal content, which is related to the contributing population, this ratio can be considered as proxy of the prevalence of infections in the population of the wastewater catchment area.
The dataset is available as part of the Environmental Virus Profiling data section "The amount of SARS-CoV-2 virus in wastewater across Sweden". It can be found here and downloaded under the heading "Dataset". Note that the dataset is preliminary. The team is still conducting method efficiency checks that might slightly affect the final results.
DOI: 10.7483/opendata.cms.r7r3.q1xx
title: /TprimeBToTH_TLep_Hbb_LH_MT2500_MH100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17044/scilifelab.14256317.v1
title: Dataset of SARS-CoV-2 wastewater data from Uppsala, and neighbouring towns Knivsta, Enköping, Östhammar and Älvkarleby, Sweden

description: This is a metadata record for a continuously updated dataset of SARS-CoV-2 RNA data in wastewater in Uppsala and neighbouring towns Knivsta, Enköping, Östhammar and Älvkarleby.<br>The dataset is part of a research study led associate professor Anna J. Székely (SLU, Swedish University of Agricultural Sciences) and her research groups in collaboration with Uppsala Vatten. The research group is part of the Environmental Virus Profiling Research Area of the SciLifeLab National COVID-19 Research Program.<br>The viral content is concentrated according to the protocol from Ahmed et al. https://doi.org/10.1016/j.scitotenv.2020.139960according to the modified electronegative filtration method. Viral RNA is extracted and SARS-CoV-2 gene copy nr quantified using R-qPCR. Recovery efficiency is measured using Bovine coronavirus (BCoV) as a virus surrogate. Pepper mild mottle virus (PPMoV) is used to correct for variations in water size and flow. For more information see https://www.covid19dataportal.se/data_types/environment/wastewater/#uppsala<br><br>The dataset for Uppsala started week 38 and updates weekly. Knivsta started week 12, Enköping week 13 and Östhammar and Älvkarleby week 14.<br>The dataset is available as part of the Environmental Virus Profiling data section. The amount of SARS-CoV-2 virus in wastewater across Sweden" https://www.covid19dataportal.se/data_types/environment/wastewater/#uppsala on the Swedish COVID-19 Data Portal (https://covid19dataportal.se). The dataset is also available as part of the CRUSH Covid project https://crush-covid.shinyapps.io/crush_covid/<br>Funded by SciLifeLab/KAW National COVID-19 Research Program https://www.scilifelab.se/covid-19/national-program/<br>