cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19660587
title: Figure 5 - calcium activity in the anterior constriction

description: Each .mat file contains the processed GCaMP fluorescence activity from imaging calcium pulses in the anterior constriction of the midgut.<br> <br> Each dataset contains a kymograph for a particular embryo.<br> The position relative to the constriction location is stored as apGrid, and the timestamps for the kymograph are stored as timeGrid. sinterp gives the estimated background signal as a kymograph. dinterp gives the estimated transient activity, which is the difference between adjacent images, summed across the lateral axis (vertical axis in the images):<br> dinterp = (|I1-I2| + |I1-I3| + |I2-I3|)/3 - sinterp, <br> where sinterp is the estimated background taken as the minimum of each image difference min(|I1-I2|, |I1-I3|, |I2-I3|).<br> Here I1 would be the 1D measure of GCaMP fluorescence of the maximum intensity projection of the first of three rapidly-acquired confocal stacks parameterized by position along the AP axis of the organ. <br> <br> <br> In addition, xfixed is a parameterization of the AP axis for time-averaged activity measures activity00, activity05, activity10, activity15, etc. Here activityXX corresponds to the mean activity measured across the first XX minutes after the onset of constriction.
DOI: 10.6084/m9.figshare.19660587.v1
title: Figure 5 - calium activity in the anterior constriction

description: Each .mat file contains the processed GCaMP fluorescence activity from imaging calcium pulses in the anterior constriction of the midgut.<br> <br> <br> Each dataset contains a kymograph for a particular embryo.<br> The position relative to the constriction location is stored as apGrid, and the timestamps for the kymograph are stored as timeGrid. sinterp gives the estimated background signal as a kymograph. dinterp gives the estimated transient activity, which is the difference between adjacent images, summed across the lateral axis (vertical axis in the images):<br> dinterp = (|I1-I2| + |I1-I3| + |I2-I3|)/3 - sinterp, <br> where sinterp is the estimated background taken as the minimum of each image difference min(|I1-I2|, |I1-I3|, |I2-I3|).<br> Here I1 would be the 1D measure of GCaMP fluorescence of the maximum intensity projection of the first of three rapidly-acquired confocal stacks parameterized by position along the AP axis of the organ. <br> <br> <br> <br> <br> In addition, xfixed is a parameterization of the AP axis for time-averaged activity measures activity00, activity05, activity10, activity15, etc. Here activityXX corresponds to the mean activity measured across the first XX minutes after the onset of constriction.
DOI: 10.48567/z5cc-0062
title: Relay Station Automatic Weather Station, 2021 quality-controlled observational data.
DOI: 10.48567/jr6j-3n32
title: Possession Island Automatic Weather Station, 2021 quality-controlled observational data.
DOI: 10.5066/p9b6zdur
title: Spatial Models of Wild Bird Risk Factors for Highly Pathogenic A(H5N1) Avian Influenza Virus Transmission

description: Wild waterfowl (family Anatidae) are reported as secondary transmitters of HPAIV and primary reservoirs for low-pathogenic avian influenza viruses, yet spatial inputs for disease risk modeling for this group have been lacking. Using geographic information software and Monte Carlo simulations, we developed geospatial indices of waterfowl abundance at 1 km resolutions for the breeding and wintering seasons for China, the epicenter of H5N1. Two types of spatial layers were developed: cumulative waterfowl abundance (WAB), a measure of predicted abundance by species, and cumulative abundance weighted by H5N1 prevalence (WPR), whereby abundance for each species was adjusted based on species specific prevalence values. Spatial patterns of the model output differed between seasons, with higher WAB and WPR in the northern and western regions of China for the breeding season and in the southeast for the wintering season. Uncertainty measures indicated highest error in southeastern China for both WAB and WPR.
DOI: 10.5281/zenodo.5806523
title: Data and code for: Impacts of changing snowfall on seasonal complementarity of hydroelectric and solar power

description: Data and code to reproduce analyses in manuscript entitled: Influence of changing snowfall on seasonal complementarity of hydroelectric and solar power. Submitted to Environmental Research: Infrastructure and Sustainability. The contents include the following scripts and files, listed below. Scripts are listed in the order needed to reproduce the analysis, though intermediate data products have been saved so it is not necessary to reproduce the initial analytical steps. R/ eia923_860.R: extracts solar and hydropower production data; requires local download of EIA data. gridMET_swep.R: downloads and summarises gridmet data; does not require prior local download. fdr.R: function to calculate the p-value associated with a given false discovery rate as described in the associated manuscript. combine_data.R combines solar, hydropower, and SWE/P data analysis.Rmd: primary script in which analyses are conducted data/ annual_swep.csv: output from gridMET_swep.R with annual SWE/P for each watershed in the study monthly_hydro.csv: output from eia923_860.R monthly_solar.csv: output from eia923_860.R combined_variables.csv: combines variables above in one CSV watersheds_wbd_ss: shapefiles for watersheds that drain to each dam used in the study, derived as described in the manuscript.
DOI: 10.5281/zenodo.5806522
title: Data and code for: Impacts of changing snowfall on seasonal complementarity of hydroelectric and solar power

description: Data and code to reproduce analyses in manuscript entitled: Influence of changing snowfall on seasonal complementarity of hydroelectric and solar power. Submitted to Environmental Research: Infrastructure and Sustainability. The contents include the following scripts and files, listed below. Scripts are listed in the order needed to reproduce the analysis, though intermediate data products have been saved so it is not necessary to reproduce the initial analytical steps. R/ eia923_860.R: extracts solar and hydropower production data; requires local download of EIA data. gridMET_swep.R: downloads and summarises gridmet data; does not require prior local download. fdr.R: function to calculate the p-value associated with a given false discovery rate as described in the associated manuscript. combine_data.R combines solar, hydropower, and SWE/P data analysis.Rmd: primary script in which analyses are conducted data/ annual_swep.csv: output from gridMET_swep.R with annual SWE/P for each watershed in the study monthly_hydro.csv: output from eia923_860.R monthly_solar.csv: output from eia923_860.R combined_variables.csv: combines variables above in one CSV watersheds_wbd_ss: shapefiles for watersheds that drain to each dam used in the study, derived as described in the manuscript.
DOI: 10.6084/m9.figshare.19660677
title: Supplementary Tables

description: This file contains Supplementary Tables 1-4.
DOI: 10.6084/m9.figshare.19660677.v1
title: Supplementary Tables

description: This file contains Supplementary Tables 1-4.
DOI: 10.5066/p9c2j23x
title: Groundwater Well Yield in Wake County, North Carolina

description: The U.S. Geological Survey, in cooperation with Wake County Environmental Services, initiated a comprehensive groundwater resource investigation to better understand the quantity and quality of groundwater in Wake County, North Carolina. In 2020, as part of the investigation, groundwater records were compiled electronically from 7,689 wells in and within five miles of Wake County and groundwater yield data was interpolated to estimate maximum well yield across the county. Maximum well yield values estimate the maximum well yield possible at a given location; the maximum well yield is not guaranteed and actual well yield likely falls within the range of 0 and the maximum well yield value. This data release includes the well record compilation (well_inventory.csv) and estimated raster surface of maximum well yield (max_well_yield.zip). The well inventory includes the characteristics: well location, well depth, casing depth, and well yield. The data contained in this data release and the methodology for development of the maximum well yield raster is further described in the associated U.S. Geological Survey Scientific Investigations Report (https://doi.org/10.3133/sir2021XXXX).
DOI: 10.48567/t5t1-4d13
title: Phoenix Automatic Weather Station, 2021 quality-controlled observational data.
DOI: 10.48567/fzxa-bx82
title: PANDA-South Automatic Weather Station, 2021 quality-controlled observational data.
DOI: 10.48567/pbyp-pr75
title: Nico Automatic Weather Station, 2021 quality-controlled observational data.
DOI: 10.34740/kaggle/dsv/3479723
title: Data Science Trends on Google
DOI: 10.48567/6w42-cx29
title: Mizuho Automatic Weather Station, 2021 quality-controlled observational data.
DOI: 10.48567/rw7q-6n19
title: Marilyn Automatic Weather Station, 2021 quality-controlled observational data.
DOI: 10.5066/p904apik
title: Species distribution models from a habitat suitability approach: waterfowl of China

description: This is a data release for models indicating suitable habitat for waterfowl at a 1km resolution across China. Faced with limited data, we built species distribution models using a habitat suitability approach for China's breeding and non-breeding waterfowl. An extensive review of the literature was used to determine model parameters for habitat modeling. Habitat relationships were implemented in GIS using land cover covariates. Wintering models were validated using waterfowl census data, while breeding models, though developed for many species, were only validated for the one species with sufficient telemetry data available. We developed suitability models for 42 waterfowl species (30 breeding and 39 wintering) at 1 km resolution for the extent of China.
DOI: 10.6084/m9.figshare.19660686
title: 10x Single-Cell RNAseq Lung (External Test Set)

description: Human lung cells from five cell-types with labels in a SingleCellExperiment object. These data can be used as an external test set to evaluate cell-type annotation classifiers. The cells are taken from the following study: <br> Wang, A., Chiou, J., Poirion, O. B., Buchanan, J., Valdez, M. J., Verheyden, J. M., ... & NHLBI LungMap Consortium. (2020). Single-cell multiomic profiling of human lungs reveals cell-type-specific and age-dynamic control of SARS-CoV2 host genes. <em>Elife</em>, <em>9</em>, e62522.
DOI: 10.6084/m9.figshare.19658553.v1
title: 10x Single-Cell RNAseq Brain (External Test Set)

description: Human microglia cells grown in a mouse brain environment in a SingleCellExperiment object. These data can be used as an external test set for evaluating cell-type annotation classifiers. These cells are from the following study: <br> Hasselmann, J., & Blurton‐Jones, M. (2020). Human iPSC‐derived microglia: A growing toolset to study the brain's innate immune cells. <em>Glia</em>, <em>68</em>(4), 721-739.
DOI: 10.6084/m9.figshare.19660665
title: 10x Single-Cell RNAseq Lung (Withheld Test Set)

description: Human lung cells comprising thirteen cell-types from the Human Lung Cell Atlas, consisting of 100 cells each withheld from a larger reference set, with labels in a SingleCellExperiment object. These data can be used to evaluate cell-type annotation classifiers. These cells are from the following study:<br> <br> <br> Travaglini, K. J., Nabhan, A. N., Penland, L., Sinha, R., Gillich, A., Sit, R. V., ... & Krasnow, M. A. (2020). A molecular cell atlas of the human lung from single-cell RNA sequencing. <em>Nature</em>, <em>587</em>(7835), 619-625.
DOI: 10.6084/m9.figshare.19658175
title: 10x Single-Cell RNAseq Colon (Withheld Test Set)

description: Human epithelial, B, and stromal cells from the colon in a SingleCellExperiment object with labels. These data consist of 100 cells withheld from a larger reference set and can be used for evaluating cell-type annotation classifiers. These three different cell-types were combined from the following original studies:<br> <br> <br> Epithelial cells: Wang, Y., Song, W., Wang, J., Wang, T., Xiong, X., Qi, Z., ... & Chen, Y. G. (2020). Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine. <em>Journal of Experimental Medicine</em>, <em>217</em>(2).<br> <br> <br> B cells: Huang, B., Chen, Z., Geng, L., Wang, J., Liang, H., Cao, Y., ... & Zhang, Y. (2019). Mucosal profiling of pediatric-onset colitis and IBD reveals common pathogenics and therapeutic pathways. <em>Cell</em>, <em>179</em>(5), 1160-1176.<br> <br> <br> Stromal cells: Kinchen, J., Chen, H. H., Parikh, K., Antanaviciute, A., Jagielowicz, M., Fawkner-Corbett, D., ... & Simmons, A. (2018). Structural remodeling of the human colonic mesenchyme in inflammatory bowel disease. <em>Cell</em>, <em>175</em>(2), 372-386.
DOI: 10.6084/m9.figshare.19658472
title: 10x Single-Cell RNAseq Brain (Withheld Test Set)

description: Human astrocytes and oligodendrocytes from the brain with labels in a SingleCellExperiment object. These are 100 cells each withheld from a larger reference set, which can be used to assess cell-type annotation classifiers. These cells have been subsetted from data from the following study (Allen Brain Atlas): <br> Bakken, T., Jorstad, N., Hu, Q., Lake, B., Tian, W., Kalmbach, B., ... & Lein, E. (2020). Evolution of cellular diversity in primary motor cortex of human, marmoset monkey, and mouse. <em>BioRxiv</em>.
DOI: 10.6084/m9.figshare.19652754
title: 10x Single-Cell RNAseq Colon (Reference)

description: Human epithelial, B, and stromal cells from the colon in a SingleCellExperiment object with labels. These data can be used as a reference set for training cell-type annotation classifiers. These three different cell-types were combined from the following original studies: <br> Epithelial cells: Wang, Y., Song, W., Wang, J., Wang, T., Xiong, X., Qi, Z., ... & Chen, Y. G. (2020). Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine. <em>Journal of Experimental Medicine</em>, <em>217</em>(2). <br> B cells: Huang, B., Chen, Z., Geng, L., Wang, J., Liang, H., Cao, Y., ... & Zhang, Y. (2019). Mucosal profiling of pediatric-onset colitis and IBD reveals common pathogenics and therapeutic pathways. <em>Cell</em>, <em>179</em>(5), 1160-1176. <br> Stromal cells: Kinchen, J., Chen, H. H., Parikh, K., Antanaviciute, A., Jagielowicz, M., Fawkner-Corbett, D., ... & Simmons, A. (2018). Structural remodeling of the human colonic mesenchyme in inflammatory bowel disease. <em>Cell</em>, <em>175</em>(2), 372-386.
DOI: 10.6084/m9.figshare.19658253
title: 10x Single-Cell RNAseq Colon (External Test Set)

description: Human epithelial, stromal, and B cells from colon with labels in a SingleCellExperiment object. These data can be used as an external test set for evaluating cell-type annotation classifiers. These cells are subsetted from data from the following study: <br> Smillie, C. S., Biton, M., Ordovas-Montanes, J., Sullivan, K. M., Burgin, G., Graham, D. B., ... & Regev, A. (2019). Intra-and inter-cellular rewiring of the human colon during ulcerative colitis. <em>Cell</em>, <em>178</em>(3), 714-730.
DOI: 10.6084/m9.figshare.19658406
title: 10x Single-Cell RNAseq Brain (Reference)

description: Human astrocytes, oligodendrocytes, and microglia from the brain with labels in a SingleCellExperiment object. These data can be used as a reference set for training cell-type annotation classifiers. These cells have been subsetted from data from the following study (Allen Brain Atlas): <br> Bakken, T., Jorstad, N., Hu, Q., Lake, B., Tian, W., Kalmbach, B., ... & Lein, E. (2020). Evolution of cellular diversity in primary motor cortex of human, marmoset monkey, and mouse. <em>BioRxiv</em>.
DOI: 10.6084/m9.figshare.19658670
title: 10x Single-Cell RNAseq Lung (Reference)

description: Human lung cells comprising thirteen cell-types from the Human Lung Cell Atlas with labels in a SingleCellExperiment object. These data can be used as a reference when training cell-type annotation classifiers. These cells are from the following study: <br> Travaglini, K. J., Nabhan, A. N., Penland, L., Sinha, R., Gillich, A., Sit, R. V., ... & Krasnow, M. A. (2020). A molecular cell atlas of the human lung from single-cell RNA sequencing. <em>Nature</em>, <em>587</em>(7835), 619-625.
DOI: 10.6084/m9.figshare.19652601.v1
title: 10x Single-Cell RNAseq PBMCs (Reference)

description: Human CD4, CD8, CD14, and NK cells in a SingleCellExperiment object with annotation labels. This dataset is used as a reference set to train cell-type annotation classifiers. These cells are compiled from data downloaded from the 10x website, and originate from the following publication: <br> Zheng, G. X., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., ... & Bielas, J. H. (2017). Massively parallel digital transcriptional profiling of single cells. <em>Nature communications</em>, <em>8</em>(1), 1-12.
DOI: 10.6084/m9.figshare.19652748.v1
title: 10x Single-Cell RNAseq PBMCs (External Test Set)

description: Human CD14 cells from two donors in a SingleCellExperiment. These data can be used as an external test set to assess cell-type annotation classifiers. These data were downloaded from GEO and originate from the following study: <br> Goudot, C., Coillard, A., Villani, A. C., Gueguen, P., Cros, A., Sarkizova, S., ... & Segura, E. (2017). Aryl hydrocarbon receptor controls monocyte differentiation into dendritic cells versus macrophages. <em>Immunity</em>, <em>47</em>(3), 582-596.
DOI: 10.6084/m9.figshare.19658553
title: 10x Single-Cell RNAseq Brain (External Test Set)

description: Human microglia cells grown in a mouse brain environment in a SingleCellExperiment object. These data can be used as an external test set for evaluating cell-type annotation classifiers. These cells are from the following study: <br> Hasselmann, J., & Blurton‐Jones, M. (2020). Human iPSC‐derived microglia: A growing toolset to study the brain's innate immune cells. <em>Glia</em>, <em>68</em>(4), 721-739.
DOI: 10.6084/m9.figshare.19652601
title: 10x Single-Cell RNAseq PBMCs (Reference)

description: Human CD4, CD8, CD14, and NK cells in a SingleCellExperiment object with annotation labels. This dataset is used as a reference set to train cell-type annotation classifiers. These cells are compiled from data downloaded from the 10x website, and originate from the following publication: <br> Zheng, G. X., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., ... & Bielas, J. H. (2017). Massively parallel digital transcriptional profiling of single cells. <em>Nature communications</em>, <em>8</em>(1), 1-12.
DOI: 10.6084/m9.figshare.19652655
title: 10x Single-Cell RNAseq PBMCs (Withheld Test Set)

description: Human CD4, CD8, CD14, and NK cells (100 of each, withheld from a larger reference set) in a SingleCellExperiment object with annotation labels. This dataset is used as a test set for cell-type annotation classifiers. These cells are compiled from data downloaded from the 10x website, and originate from the following publication:<br> <br> <br> Zheng, G. X., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., ... & Bielas, J. H. (2017). Massively parallel digital transcriptional profiling of single cells. <em>Nature communications</em>, <em>8</em>(1), 1-12.
DOI: 10.6084/m9.figshare.19652748
title: 10x Single-Cell RNAseq PBMCs (External Test Set)

description: Human CD14 cells from two donors in a SingleCellExperiment. These data can be used as an external test set to assess cell-type annotation classifiers. These data were downloaded from GEO and originate from the following study: <br> Goudot, C., Coillard, A., Villani, A. C., Gueguen, P., Cros, A., Sarkizova, S., ... & Segura, E. (2017). Aryl hydrocarbon receptor controls monocyte differentiation into dendritic cells versus macrophages. <em>Immunity</em>, <em>47</em>(3), 582-596.
DOI: 10.48567/myt3-2s38
title: Margaret Automatic Weather Station, 2021 quality-controlled observational data.
DOI: 10.5281/zenodo.6487583
title: Tests generated by Randoop for class integration test generation

description: Tests generated by Randoop for class integration test generation experiment
DOI: 10.5281/zenodo.6482922
title: An allozyme polymorphism is associated with a large chromosomal inversion in the marine snail Littorina fabalis

description: This Zenodo archive contains the dataset analysed in the paper "An allozyme polymorphism is associated with a large chromosomal inversion in the marine snail Littorina fabalis" published in Evolutionary Application in 2022: FAB_LG3_maf1_SNP_Hexcess_depth10.vcf : vcf for LG3 unpruned for LD containing 295 individuals genotyped at 58,246 filtered SNPs FAB_LG3_maf1_SNP_Hexcess_depth10_thin.vcf : vcf for LG3 pruned for LD containing 295 individuals genotyped at 9,905 filtered SNPs FAB_AK_maf1_SNP_Hexcess_depth10.vcf : vcf for contig265 containing the arginine kinase gene: 295 individuals genotyped at 70 filtered SNPs The archive also include some of the R script used to performed the analyses of the manuscrit: Population_genetic_Ark_analyses.R : Script to perform PCA +phenotypic cline + FST + Hobs + FIS Suspension_bridge_fit.R : Script to perform the suspension bridge fit used to found evidence of gene flux inside the inversion. Cline_function.R : function used to fit the allelic frequency variation (cline) along the transect The raw sequences are available in NCBI. Abstract of the study: Understanding the genetic targets of natural selection is one of the most challenging goalsof population genetics. Some of the earliest candidate genes were identified from associations between allozyme allele frequencies and environmental variation. One such example is the clinal polymorphism in the arginine kinase (<em>Ak</em>) gene in the marine snail <em>Littorina fabalis</em>. While other enzyme loci do not show differences in allozyme frequencies among populations, the <em>Ak</em> alleles are near differential fixation across repeated wave exposure gradients in Europe. Here, we use this case to illustrate how a new sequencing toolbox can be employed to characterize the genomic architecture associated with historical candidate genes. We found that the <em>Ak</em> alleles differ by 9 non-synonymous substitutions, which perfectly explain the different migration patterns of the allozymes during electrophoresis. Moreover, by exploring the genomic context of the <em>Ak</em> gene, we found that the three main <em>Ak</em> alleles are located on different arrangements of a putative chromosomal inversion that reaches near fixation at the opposing ends of two transects covering a wave exposure gradient. This shows <em>Ak</em> is part of a large (3/4 of the chromosome) genomic block of differentiation, in which <em>Ak</em> is unlikely to be the only target of divergent selection. Nevertheless, the non-synonymous substitutions among <em>Ak</em> alleles and the complete association of one allele with one inversion arrangement suggest that the <em>Ak</em> gene is a strong candidate to contribute to the adaptive significance of the inversion.
DOI: 10.5281/zenodo.6487582
title: Tests generated by Randoop for class integration test generation

description: Tests generated by Randoop for class integration test generation experiment