cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6760619
title: First study of the susceptibility of Culex quinquefasciatus from the Amazon Region to become infected and transmit the West Nile virus strain isolated in Brazil

description: <em>West Nile virus</em> is characterized as a neurotropic pathogen transmitted by <em>Culex</em> genus mosquitoes that cause West Nile fever. In 2018, the Evandro Chagas Institute performed the first isolation of a WNV strain in Brazil, from a horse brain sample. This study aimed to evaluate the susceptibility of <em>Cx. quinquefasciatus</em> from the Amazon region of Brazil to become infected and transmit the WNV strain isolated in 2018. Oral infection of naive F1 generation females was performed with sheep bloodmeal artificially infected with WNV, followed by analysis of infection, dissemination, and transmission rates, as well as viral titers of body, head, and saliva samples. The infection rate was 100% in all dpe evaluated. The dissemination rates varied in the three dpe, with a 40%, 37.5%, and 100% in the 7<sup>th</sup>, 14<sup>th</sup>, and 21<sup>st</sup> dpe, respectively. The transmission rate was 100% from the 21<sup>st</sup> dpe. These results indicate that that <em>Cx. quinquefasciatus</em> is susceptible to oral infection by the Brazilian strain of WNV
DOI: 10.25625/bsqw3c/8wppo1
title: aris_balance_sheet.tab

description: :unav
DOI: 10.25625/bsqw3c
title: Aris Water Solutions - Financials Data

description: What is Aris Water Solutions? According to Finsheet, they are: “We are a leading, growth-oriented environmental infrastructure and solutions company that directly helps our customers reduce their water and carbon footprints. Our integrated pipelines and related infrastructure creates long-term value by delivering high-capacity, comprehensive produced water management, recycling and supply solutions to operators in the core areas of the Permian Basin.”. The company's current WACC value is 7.0% and their stock price in Excel is $16.52. NOTE- ConocoPhillips owns 24% and also accounts for 34% of ARIS 1Q 2022 revenue. As a result, its DCF valuation is around $11.34, 32% lower than the market price. This result is illustrated in their form 10 k. I have to imagine that portion of revenue will be stable as ConocoPhillips will continue to use ARIS for their water management needs as they have a vested interest in them. Full Cycle Water Management By the Numbers From a value investing perspective, as we can see with their increased water volumes (up 45%) , growing volume profile, new partnerships (Chevron) and expansion with existing customers (Texas Pacific Land Corp), Aris is growing revenues at 54% (Q/Q). As a result, their intrinsic value is super promising when you factor in all the growth components. Future Growth Plans It appears Aris is not only focusing on the oil and gas industry, but looking for ways to expand into new business/revenue channels. To the Charts Aris is a recent IPO name showing accumulation and relative strength during general market weakness. While it is true many Oilers and energy names have been showing strength and working (really the only sector that has been lately) The best stock research websites all really like the technical action of ARIS. With a composite rating of 95 and U/D Volume ratio of 1.5 (strong) ARIS broke out of its recent pivot high around 19.50 on 5/27 on 778% on normal volume, very heavy and strong. I always pay attention to names with increasing fund ownership, 3 consecutive qtrs and look at for their newest filings in the Edgar Company Search system.. of sales growth making new highs on increased volume (demand) during market corrections. Conclusion: As long as ARIS can hold its recent breakout pivot based on their recent SEC filings, I believe its worth your attention. There might not be a resource more vital to life than water. It also appears we are at an intersection of possibly needing more domestic oil production (current geo-political/ war) and a developing water shortage (remember seeing those pictures of lake Mead?). ARIS is positioned to benefit by both tailwinds. As always, do your own research and make your own decisions, but don’t forget about this little IPO name making waves in the water market.
DOI: 10.6084/m9.figshare.19416806
title: clinvar_discordances.xlsx

description: Pre-computed lists of ‘variant pairs’ with two ClinVar variants for - PS1 Strong Evidence (VUS and P/LP variant encoding the same amino acid substitution) PM5 Moderate Evidence (VUS and P/LP variant encoding distinct amino acid substitutions at the same codon)
DOI: 10.6084/m9.figshare.19416845
title: dbnsfp_variant_pairs.xlsx

description: Pre-computed lists of ‘variant pairs’ with one ClinVar variant and one variant not in ClinVar (any possible nsSNV from dbNSFP) for - PS1 Strong Evidence (Unclassified and P/LP variant encoding the same amino acid substitution) PM5 Moderate Evidence (Unclassified and P/LP variant encoding distinct amino acid substitutions at the same codon)
DOI: 10.6084/m9.figshare.19417682
title: pvs1_all_variants.xlsx

description: Pre-computed lists of variants from ClinVar and exome-wide (dbNSFP) for - PVS1 Very Strong Evidence (Nonsense or canonical splice affecting variant classified as VUS in a gene where &gt;50% of high confidence LOF SNVs are classified as P/LP)
DOI: 10.5281/zenodo.6787196
title: RNA-seq pupal epidermis

description: RNA-seq from pupal epidermis
DOI: 10.25934/pr00007501
title: COVID-19 clinical trial data utilization to develop external control datasets in COVID-19 therapeutics development

description: We are requesting access to the dataset for the purpose of conducting the COVID-19 (coronavirus disease 2019) clinical trial data utilization to develop external control datasets in COVID-19 therapeutics development study. Such investigation is necessary to expedite the development of novel therapeutics against COVID-19. In our study, we tried to create an external control group through propensity score matching from previous COVID-19 clinical studies. Propensity score matching (PSM) is a experimental method in which the researcher uses statistical techniques to construct an artificial control group by matching each treated unit with a non-treated unit of similar characteristics. Using these matches, the researcher can estimate the impact of an intervention. If an external control group is created and evaluated through this study, when developing a COVID-19 treatment using external control group in the future, the time and cost of research for developing a COVID-19 treatment can be reduced and accelerated.
DOI: 10.5281/zenodo.6788898
title: Electronic Supplementary Data to "Zika vector competence data reveals risks of outbreaks: the contribution of the European ZIKAlliance project"

description: Electronic Supplementary Data to "Zika vector competence data reveals risks of outbreaks: the contribution of the European ZIKAlliance project"
DOI: 10.5281/zenodo.6788897
title: Electronic Supplementary Data to "Zika vector competence data reveals risks of outbreaks: the contribution of the European ZIKAlliance project"

description: Electronic Supplementary Data to "Zika vector competence data reveals risks of outbreaks: the contribution of the European ZIKAlliance project"
DOI: 10.15482/usda.adc/1461500
title: Data from: Differential gene expression in red imported fire ant (Solenopsis invicta) (Hymenoptera: Formicidae) larval and pupal stages

description: 12 files (6 sets of paired Illumina FASTQ files) transcriptomes (expressed genes) of the fourth (final) larval and early (white) pupal stages of Solenopsis invicta from Mississippi Delta.
DOI: 10.6084/m9.figshare.20216858
title: RNA-Seq counts datasets

description: Processed gene expression profiles of tissue types from eight species. The genome assembly (FASTA file), GTF annotation, and transcriptomic data were obtained from Ensembl v94. The raw RNA-Seq FASTQ files for eight species were collected from NCBI GEO and EBI ArrayExpress. Reads were mapped to the corresponding genome using STAR (v2.7.1a) after trimming adapters using Cutadapt (v1.16). The library preparation protocol strand type of each library was inferred using the infer_experiment.py module in RSeQC (v4.0). Genes annotated in the GTF files were quantified using HTSeq-count (v0.13.5).
DOI: 10.6084/m9.figshare.19633131
title: GO analysis files

description: - allwayOrthologs_hsapiens-ptroglodytes-mmulatta-mmusculus-rnorvegicus-btaurus-sscrofa-ggallus_ens94.tsv: shared 1-to-1 orthologs in eight species (Ensembl v94) - hg38_geneID2GO.EnsemblID2GO.EnsembleV94.Robj : hg38 Ensembl v94 geneID mapping to GO ID list R object. - mm10_geneID2GO.EnsemblID2GO.EnsembleV94.Robj: mm10 Ensembl v94 geneID mapping to GO ID list R object. - EnsemblID2GO.R: script to generate Ensembl gene ID mapping to GO ID list. - hg38_length.EnsemblV94.RData: hg38 gene length Robject - mm10_length_EnsemblV94.RData: mm10 gene length Robject <br> <br>
DOI: 10.6084/m9.figshare.19633131.v1
title: GO analysis files

description: - allwayOrthologs_hsapiens-ptroglodytes-mmulatta-mmusculus-rnorvegicus-btaurus-sscrofa-ggallus_ens94.tsv: shared 1-to-1 orthologs in eight species (Ensembl v94) - hg38_geneID2GO.EnsemblID2GO.EnsembleV94.Robj : hg38 Ensembl v94 geneID mapping to GO ID list R object. - mm10_geneID2GO.EnsemblID2GO.EnsembleV94.Robj: mm10 Ensembl v94 geneID mapping to GO ID list R object. - EnsemblID2GO.R: script to generate Ensembl gene ID mapping to GO ID list. - hg38_length.EnsemblV94.RData: hg38 gene length Robject - mm10_length_EnsemblV94.RData: mm10 gene length Robject <br> <br>
DOI: 10.6084/m9.figshare.20216876
title: Annotation files

description: Information about genes: gene name, gene length, chromosome, etc.
DOI: 10.6084/m9.figshare.20216876.v1
title: Annotation files

description: Information about genes: gene name, gene length, chromosome, etc.
DOI: 10.22003/xfel.eu-data-002867-00
title: A systematic study of the liquid-liquid transitions in the no-man’s land of phase-change materials
DOI: 10.22003/xfel.eu-data-002794-00
title: Ultrafast probing of the atomic structure of supercooled liquid metals
DOI: 10.22003/xfel.eu-data-003247-00
title: Solid-state crystallography and XRD
DOI: 10.6084/m9.figshare.13681423
title: An mHealth Tool for Community Health Workers to Improve Caregiver Knowledge of Child Health in the Amazon: An Effectiveness-Implementation Hybrid Evaluation

description: An mHealth Tool for Community Health Workers to Improve Caregiver Knowledge of Child Health in the Amazon: An Effectiveness-Implementation Hybrid Evaluation
DOI: 10.6084/m9.figshare.13681423.v1
title: Implementation Research for the Evaluation of the Child Health Education and Surveillance Tool Application

description: Implementation Research for the Evaluation of the Child Health Education and Surveillance Tool Application
DOI: 10.5281/zenodo.6786912
title: Spatial Transcriptomics of RA Synovium

description: Seurat object for Cosmx data of RA tissue.
DOI: 10.5281/zenodo.6786911
title: Spatial Transcriptomics of RA Synovium

description: Seurat object for Cosmx data of RA tissue.
DOI: 10.6084/m9.figshare.3761562.v2266
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.20216960
title: S2_NMDS_Data_OK2018_2022

description: This is a collection of mosquitoes collected in the Oklahoma City metropolitan area from 2018-2021. The data set list the species and their abundace per site and year. This data set is formated for an odination analysis.
DOI: 10.6084/m9.figshare.20216960.v1
title: S2_NMDS_Data_OK2018_2022

description: This is a collection of mosquitoes collected in the Oklahoma City metropolitan area from 2018-2021. The data set list the species and their abundace per site and year. This data set is formated for an odination analysis.
DOI: 10.17604/2h87-n675
title: The Influence of Radiation on the Prediction of Tropical Cyclone Intensification in a Forecast Model

description: This data set contains the HWRF retrospective reanalysis for 2018 hurricanes that was used to study the role of radiative heating in predicting tropical cyclone intensification. The concept that radiative heating can provide substantial energy for tropical cyclone intensification has been identified in numerical simulations and been observed in satellite measurements. However, how the distribution of radiative heating in forecasting models affects the numerical prediction skills of tropical cyclone intensification remains unsolved. The radiative heating and tropical cyclone information provided here can be used to estimate how the ability of capturing radiative heating in numerical models affects the performance of tropical cyclone intensification. This data are associated with the article: Wu, S.-N., Soden, B., Alaka, G. “Examining the Role of Cloud Radiative Interactions in Tropical Cyclone Development using Satellite Measurements and WRF Simulations”
description: The provided reanalysis data are produced by Hurricane Weather and Research Forecasting model (HWRF) for 2018 tropical cyclones. For the details regarding HWRF, readers are referred to NOAA website (https://www.weather.gov/sti/coastalact_hwrfreanalysis). 

ALWC_HWRF.dat file contains atmospheric longwave convergence from HWRF simulations for 2018 tropical cyclones. The horizontal resolution is coarsening to 1-degree lat/lon in order to match CERES dataset. Each snapshot corresponds to one storm simulations listed in the TC_information.csv. The unit for the atmospheric longwave convergence is W/m2

ALWC_CERES.dat file contains atmospheric longwave convergence from CERES measurements corresponding to the storms simulated by HWRF at the same time. The storm center used to locate tropical cyclone is from IBTrACS (https://www.ncdc.noaa.gov/ibtracs/). The horizontal resolution is 1-degree lat/lon. The unit for the atmospheric longwave convergence is W/m2

TC_information.csv collects tropical cyclone information for HWRF simulated storms and storms compiled from CERES measurements, including but not limited to the storm name and time, initial storm intensity, and 24-h intensity change.

Plot_Diff_HWRF_CERES.py read two radiative heating files (Radiative_heating_HWRF.dat and Radiative_heating_HWRF.dat) and create composites of atmospheric longwave convergence based on TC intensification rates from TC_information.csv
DOI: 10.5281/zenodo.6789028
title: RRR/RAPID input and output files corresponding to "Underlying Fundamentals of Kalman Filtering for River Network Modeling"

description: <strong>Corresponding peer-reviewed publication</strong> This dataset corresponds to all the RRR/RAPID input and output files that were used in the study reported in: Emery, C. M., C. H. David, K. M. Andreadis, M. J. Turmon, J. T. Reager, and J. M. Hobbs (2020), Underlying Fundamentals of Kalman Filtering for River Network Modeling, Journal of Hydrometeorology, 21, 453-474, DOI: 10.1175/JHM-D-19-0084.1. When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. <strong>Known bugs and limitations in this dataset or the associated manuscript.</strong> In the final version of the published manuscript, Figure 5a, Figure 5b, Figure 5c, and Figure SF1 are inaccurate. The issue in these figures is that they were all prepared with an incorrect indexing relating observed and simulated discharge, hence observations at any one location were consistently being compared to simulations at another different location. As a result, all values of "measured" discharge errors (i.e. Bias, STDE, and RMSE) are incorrect. This issue did not affect the values of "estimated" errors, nor did it affect all values of the Nash-Sutcliffe effeciency that are presented. The figures published in the manuscript can all be recreated using the files in which "BUG_DO_NOT_USE" was appended to the name. Correct figures can also be created using corresponding file names that were not so appended. Note that corrected versions of Figure 5a, Figure 5b, Figure 5c, and Figure SF1 all retain the same strong linear relationships that are discussed in the paper. The slope of the daily discharge STDE trend initially reported as \(\alpha = 0.3876\) in Figure 5c changes to \(\alpha = 0.4507\) after correction. The resulting value of the ideal inflation factor hence changes from \(I = {1 \over 0.3876} \approx 2.58\) to \(I = {1 \over 0.4507} \approx 2.22\). This updated ideal inflation factor has no impact on the conclusions reached in the manuscript because it remains closer to \(I = 2.58\) than to \(I = 1\) or \(I = 5\), <em>i.e.</em> the three values that were evaluated. Additionally, a faulty version 1.3.1 of the Python toolbox netCDF4 led to incorrect interpretation of _FillValue in which every data point of value greater than _FillValue was interpreted as masked. This created discrepancies in the following three files, which were updated between V1 and V2 of this dataset: "timeseries_rap_exp01.csv", "timeseries_rap_exp18.csv", and "stats_rap_exp18.csv". Faulty versions of the same files have "BUG_NETCDF4" appended to their names. Correct files have been recreated with file names that were not so appended.
DOI: 10.5281/zenodo.3688690
title: RRR/RAPID input and output files corresponding to "Underlying Fundamentals of Kalman Filtering for River Network Modeling"

description: <strong>Corresponding peer-reviewed publication</strong> This dataset corresponds to all the RRR/RAPID input and output files that were used in the study reported in: Emery, C. M., C. H. David, K. M. Andreadis, M. J. Turmon, J. T. Reager, and J. M. Hobbs (2020), Underlying Fundamentals of Kalman Filtering for River Network Modeling, Journal of Hydrometeorology, 21, 453-474, DOI: 10.1175/JHM-D-19-0084.1. When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. <strong>Known bugs and limitations in this dataset or the associated manuscript.</strong> In the final version of the published manuscript, Figure 5a, Figure 5b, Figure 5c, and Figure SF1 are inaccurate. The issue in these figures is that they were all prepared with an incorrect indexing relating observed and simulated discharge, hence observations at any one location were consistently being compared to simulations at another different location. As a result, all values of "measured" discharge errors (i.e. Bias, STDE, and RMSE) are incorrect. This issue did not affect the values of "estimated" errors, nor did it affect all values of the Nash-Sutcliffe effeciency that are presented. The figures published in the manuscript can all be recreated using the files in which "BUG_DO_NOT_USE" was appended to the name. Correct figures can also be created using corresponding file names that were not so appended. Note that corrected versions of Figure 5a, Figure 5b, Figure 5c, and Figure SF1 all retain the same strong linear relationships that are discussed in the paper. The slope of the daily discharge STDE trend initially reported as \(\alpha = 0.3876\) in Figure 5c changes to \(\alpha = 0.4507\) after correction. The resulting value of the ideal inflation factor hence changes from \(I = {1 \over 0.3876} \approx 2.58\) to \(I = {1 \over 0.4507} \approx 2.22\). This updated ideal inflation factor has no impact on the conclusions reached in the manuscript because it remains closer to \(I = 2.58\) than to \(I = 1\) or \(I = 5\), <em>i.e.</em> the three values that were evaluated. Additionally, a faulty version 1.3.1 of the Python toolbox netCDF4 led to incorrect interpretation of _FillValue in which every data point of value greater than _FillValue was interpreted as masked. This created discrepancies in the following three files, which were updated between V1 and V2 of this dataset: "timeseries_rap_exp01.csv", "timeseries_rap_exp18.csv", and "stats_rap_exp18.csv". Faulty versions of the same files have "BUG_NETCDF4" appended to their names. Correct files have been recreated with file names that were not so appended.
DOI: 10.24431/rw1k6cn
title: Mooring N2 Aqua Monitor Nutrient Data, Arctic Integrated Ecosystem Research Program (ASGARD), 2017-2019

description: These data are part of the Arctic Shelf Growth, Advection, Respiration and Deposition (ASGARD) Rate Measurements project, funded as part of the North Pacific Research Board’s (NPRB’s) Arctic Integrated Ecosystem Research Program (IERP; http://www.nprb.org/arctic- program/). The program and research on this expedition was sponsored by NPRB, the Collaborative Alaskan Arctic Studies Program (CAASP, formerly the North Slope Borough/Shell Baseline Studies Program), the Bureau of Ocean Energy Management (BOEM), the National Science Foundation, and the Office of Naval Research Marine Mammals and Biology Program. In-kind support for this research cruise was contributed by the National Oceanic and Atmospheric Administration and the University of Alaska Fairbanks. For this study, 6 moorings were deployed in the northern Bering Sea and southern Chukchi Sea. Each mooring was equipped with a variety of instruments to provide time series of environmental conditions throughout the water column. Moorings were deployed during the R/V Sikuliaq cruise SKQ201709S in June 2017. With one exception, they were recovered during the R/V Sikuliaq cruise SKQ201813S in June 2018. One mooring, N5, was unable to be recovered because of problems with the mooring release, and was recovered by a small-boat operation staged out of Kivalina, AK, in July 2018. In the second year of the project, 6 mooring were re-deployed at the same locations during R/V Sikuliaq cruise SKQ201813S. Moorings were recovered during the R/V Ocean Starr (https://www.stabbertmaritime.com/vessels-2/ocean-starr/) ArcticIES Phase II Cruise, Leg 1 (no cruise id?). This dataset consists of laboratory analyses of whole water collections by a Green Eyes Aqua Monitor mounted on ASGARD mooring N2. Measurements include Silicate (SiO4), Phosphate (PO4), Nitrite (NO2), Nitrate (NO3) and Ammonium (NH4).