cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3523350
title: MesoWat_Source simulation: evaporation sources of the target (southern France and northern Italy) 10-14 October 2000

description: The dataset contains the sources of surface and virga evaporation of the hourly rainfall during the heavy rainfall episode October 10-14, 2002 in southern France and northern Italy, inside the latitude-longitude box: 41-48.6N and 1.875-15E. The data corresponds to the output of the MesoWat_Source modeling system developed by the authors. The 3D position/concentration of the target vapor (evaporated from the sources) during its approach to the target region is also included. 1) * iee files: the data is in binary format (**iee files) and can be represented using the Grid Analysis and Visualization System (GrADS). GrADS is copyrighted under the terms of the GNU Public License and is freely distributed. 2) File "oct2000_Turd0_v3_pbl3.ctl" is the Grads control file (data descriptor file). It contains in ASCII format the mapping of the binary data (*iee). It can be edited with Wordpad, Kwrite ... and it has to be changed to point to the directory that contains the binary files (**iee) .This file also contains a description of all variables and units.
DOI: 10.5281/zenodo.3523913
title: JGR-Atmosphere-wengang

description: updated datasets for manuscript of JGR-Atmosphere of wengang
DOI: 10.5281/zenodo.3523914
title: JGR-Atmosphere-wengang

description: datasets for manuscript of JGR-Atmosphere of wengang
DOI: 10.25345/c5rq3q
title: MassIVE MSV000084520 - From plants to ants: Evaluating lipidomic changes in leaf-cutter ant fungus gardens
title: Leaf-cutter ants use fresh plant material to cultivate an obligate mutualistic fungus Leucoagaricus gongylophorus in specialized fungus gardens to access and transform nutrients from plant biomass that would otherwise be unavailable to the ants. Here, we evaluated the lipidomic differences between the leaves feeding the gardens, gongylidia produced by the fungus to feed the ants, and spatially-resolve regions of the fungus garden at initial to advanced stages of leaf degradation.
DOI: 10.7910/dvn/bfsgzi/aehrke
title: rcode.txt

description: R code that can be used to reproduce the results (includes commands and outputs). This should run on any machine so long as the user saves all the files in the target directory.
DOI: 10.7910/dvn/bfsgzi/iornz4
title: PortlandAll.csv

description: Data on all residents, the offer they received (framename/frame), whether they were a new mover (type/typecode), and whether they took up the offer (used)
DOI: 10.7910/dvn/bfsgzi/lll1ts
title: PortlandNewDocks.tab

description: Data on only those residents who had a new dock built near their residence. The offer they received (framename/frame), whether they were a new mover (type/typecode), and whether they took up the offer (used)
DOI: 10.7910/dvn/bfsgzi/lrvgwq
title: PortlandNewMovers.tab

description: Data on only those residents who had newly moved to the area. The offer they received (framename/frame), whether they were a new mover (type/typecode), and whether they took up the offer (used)
DOI: 10.5281/zenodo.3523339
title: Reads and truth variant set for benchmarking variant calling/genotyping

description: <em>downsampled.fasta</em> is created by converting this file (ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG002_NA24385_son/NIST_HiSeq_HG002_Homogeneity-10953946/HG002Run01-11419412/HG002run1_S1.bam) to fasta and picking every second read (to get half the coverage and half the number of reads). <em>The HG002_GRCh37_GIAB_highconf_CG</em>... file is created by picking variants on chromosome from this file (ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/release/AshkenazimTrio/HG002_NA24385_son/NISTv3.3.2/GRCh37/HG002_GRCh37_GIAB_highconf_CG-IllFB-IllGATKHC-Ion-10X-SOLID_CHROM1-22_v.3.3.2_highconf_triophased.vcf.gz). <strong>These two files can be used in benchmarking variant calling/genotyping.</strong>
DOI: 10.5281/zenodo.3523340
title: Reads and truth variant set for benchmarking variant calling/genotyping

description: <em>downsampled.fasta</em> is created by converting this file (ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG002_NA24385_son/NIST_HiSeq_HG002_Homogeneity-10953946/HG002Run01-11419412/HG002run1_S1.bam) to fasta and picking every second read (to get half the coverage and half the number of reads). <em>The HG002_GRCh37_GIAB_highconf_CG</em>... file is created by picking variants on chromosome from this file (ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/release/AshkenazimTrio/HG002_NA24385_son/NISTv3.3.2/GRCh37/HG002_GRCh37_GIAB_highconf_CG-IllFB-IllGATKHC-Ion-10X-SOLID_CHROM1-22_v.3.3.2_highconf_triophased.vcf.gz). <strong>These two files can be used in benchmarking variant calling/genotyping.</strong>
DOI: 10.21227/69hf-9c82
title: Urban GIS and charging station data

description: Dataset consists of various open GIS data from the Netherlands as Population Cores, Neighbhourhoods, Land Use, Neighbourhoods, Energy Atlas, OpenStreetMaps, openchargemap and charging stations. The data was transformed for buffers with 350m around each charging stations. The response variable is binary popularity of a charging pool.
DOI: 10.21227/ymby-sg91
title: Urban GIS and charging station data

description: Dataset consists of various open GIS data from the Netherlands as Population Cores, Neighbhourhoods, Land Use, Neighbourhoods, Energy Atlas, OpenStreetMaps, openchargemap and charging stations. The data was transformed for buffers with 350m around each charging stations. The response variable is binary popularity of a charging pool.
DOI: 10.21227/gfes-gm78
title: Urban GIS and charging station data

description: Dataset consists of various open GIS data from the Netherlands as Population Cores, Neighbhourhoods, Land Use, Neighbourhoods, Energy Atlas, OpenStreetMaps, openchargemap and charging stations. The data was transformed for buffers with 350m around each charging stations. The response variable is binary popularity of a charging pool.
DOI: 10.7910/dvn/rjl2dk/axwexg
title: simas_clifford_kirkland_study1_replication.tab

description: :unav
DOI: 10.7910/dvn/rjl2dk/m6eaqe
title: simas_clifford_kirkland_study2_replication.tab

description: :unav
DOI: 10.7910/dvn/rjl2dk/tvg8hy
title: simas_clifford_kirkland_study2_replication.do

description: :unav
DOI: 10.7910/dvn/rjl2dk/vsd8ox
title: simas_clifford_kirkland_study1_replication.do

description: :unav
DOI: 10.17632/ny476ykyf8.1
title: Data for: An economic investigation of the dengue incidence as a result of tailings dam accidents in Brazil
DOI: 10.21227/pw0w-7241
title: Urban GIS and charging station data

description: Dataset consists of various open GIS data from the Netherlands as Population Cores, Neighbhourhoods, Land Use, Neighbourhoods, Energy Atlas, OpenStreetMaps, openchargemap and charging stations. The data was transformed for buffers with 350m around each charging stations. The response variable is binary popularity of a charging pool.
DOI: 10.5281/zenodo.3524057
title: COST-ELTeC/ELTeC-srp: Release with 30 novels encoded as level 1.

description: No description provided.
DOI: 10.17632/htj68h4j4z.1
title: Data for: Algorithmic Methods for the Use of Industrial Site Data in the Construction and Evaluation of an Actuator Disk Offshore Wind Farm Model

description: Results from the models used in the work.
DOI: 10.5519/qd.rdtiw8o7
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (26 records)
DOI: 10.7910/dvn/rsv1vh/5pl3vd
title: PerC39.A.C.low.robust0.25.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/agkj98
title: PerC37.A.C.low.robust0.25.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/fmc6kx
title: PerC35.A.C.low.robust0.25.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/gt3cag
title: PerC38.A.C.low.robust0.25.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/op2vl0
title: PerC38.A.C.high.robust0.25.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/rdh76t
title: PerC37.A.C.high.robust0.25.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/thjklx
title: PerC36.A.C.low.robust0.25.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/tqzmh1
title: PerC36.A.C.high.robust0.25.cont.image.pbcor.fits
DOI: 10.5281/zenodo.3523219
title: Spanish Antarctica campaign 2018/2019 ionosphere sounding dataset Caleta Argentina

description: This data set contains the IQ data files acquired in Caleta Argentina during the Spanish Antarctica campaign from 4-2-2019 at 15:07 UTC from, the 8-2-2019 at 12:59 UTC from the founded project by CTM2015-68902-R (MINECO/FEDE). This data are the ionosphere sensing data, the sensing channel is realized by modifying the power transmission, channel BW, and the modulation (from 2QAM to 32QAM, 2PSK to 32PSK and 2FSK to 32FSK).
DOI: 10.7910/dvn/rsv1vh/8858yz
title: PerC5.A.C.low.robust0.25.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/8drobt
title: PerC3.A.C.low.robust0.25.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/kiql5a
title: PerC3.A.C.high.robust0.25.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/mosvqw
title: PerC4.A.C.high.robust0.25.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/qludb0
title: PerC5.A.C.high.robust0.25.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/xtirw8
title: PerC4.A.C.low.robust0.25.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/zxbqin
title: PerC39.A.C.high.robust0.25.cont.image.pbcor.fits
DOI: 10.5281/zenodo.3523218
title: Spanish Antarctica campaign 2018/2019 ionosphere sounding dataset Caleta Argentina

description: This data set contains the IQ data files acquired in Caleta Argentina during the Spanish Antarctica campaign from 4-2-2019 at 15:07 UTC from, the 8-2-2019 at 12:59 UTC from the founded project by CTM2015-68902-R (MINECO/FEDE). This data are the ionosphere sensing data, the sensing channel is realized by modifying the power transmission, channel BW, and the modulation (from 2QAM to 32QAM, 2PSK to 32PSK and 2FSK to 32FSK).
DOI: 10.7910/dvn/rsv1vh/a0qirz
title: PerC6.A.C.high.robust0.25.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/bjem67
title: PerC8.A.C.low.robust0.25.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/earq6m
title: PerC8.A.C.high.robust0.25.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/ji5n6m
title: PerC7.A.C.high.robust0.25.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/plcdmz
title: PerC6.A.C.low.robust0.25.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/s5lito
title: PerC9.A.C.high.robust0.25.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/xeddag
title: PerC7.A.C.low.robust0.25.cont.image.pbcor.fits
DOI: 10.17632/35fjmfw3x7.2
title: Data for: Sensitivity of deep ocean mixing to local internal tide breaking and mixing efficiency

description: Global maps of internal tide energy conversion and breaking efficiency based on Vic et al., 2019 (Nat Comm). These maps allow users to reproduce results presented in "Sensitivity of deep ocean mixing to local internal tide breaking and mixing efficiency" by Cimoli et al., 2019. The file in this database is in NetCDF format and contains the following variables: 1. internal tide energy conversion (Et) 2. internal tide local breaking efficiency (q)
DOI: 10.7910/dvn/rsv1vh/dwwh0l
title: PerC9.A.C.low.robust0.25.cont.image.pbcor.fits
DOI: 10.17632/bnfzdtbmzf.1
title: Open dataset for: "Delta-beta cross-frequency coupling as an index of stress regulation during social-evaluative threat"

description: Open data and R analysis scripts for the paper as submitted for publication: "Poppelaars, E. S., Klackl, J., Pletzer, B., & Jonas, E. (2019). Delta-beta cross-frequency coupling as an index of stress regulation during social-evaluative threat." Hypotheses and analyses were preregistered: Poppelaars, E. S., Klackl, J., Pletzer, B., & Jonas, E. (2018). Delta-beta cross-frequency coupling as an index of stress regulation during social-evaluative threat. Open Science Framework. https://osf.io/8gchf/register/565fb3678c5e4a66b5582f67. Description of the dataset: A dataset of 37 men and 30 women (tested in the luteal phase of their menstrual cycle) participated in a public speaking task to induce social-evaluative threat. Responses of multiple stress systems were measured (sympathetic and parasympathetic nervous system activity, self-reported affect, and hypothalamic–pituitary–adrenal axis activity), as well as personality traits (e.g. trait social anxiety), and EEG delta-beta cross-frequency coupling (e.g., frontal and parietal amplitude-amplitude correlation and phase-amplitude coupling). Description of analyses files: - File 'SET_CFC_MatlabOutput.xlsx' contains the delta-beta coupling data, calculated using MATLAB scripts from https://github.com/ESPoppelaars/Cross-frequency-coupling. - File 'SETData.sav' contains the raw stress and personality data, taken from "Poppelaars, E. S., Klackl, J., Pletzer, B., Wilhelm, F. H., & Jonas, E. (2019). Open dataset for: “Social-evaluative threat: Stress response stages and influences of biological sex and neuroticism.” Mendeley Data. https://doi.org/10.17632/7vj8r76s6f". - File 'LSA_HSA_brief.RData' contains data to use as informed priors for the Bayesian analyses, calculated from data published as: "Poppelaars, E. S., Harrewijn, A., Westenberg, P. M., & van der Molen, M. J. W. (2018). Frontal delta-beta cross-frequency coupling in high and low social anxiety: An index of stress regulation? Cognitive, Affective, & Behavioral Neuroscience, 18(4), 764–777. https://doi.org/10.3758/s13415-018-0603-7". - File '01_CalculationOfData.R' imports the raw data, calculates new variables, and imputes missing data via multiple imputation using the 'predictorMatrixAdj.xlsx' file. - File '02_AnalysisOfImputedData.R' calculates descriptive statistics, creates plots, and tests hypotheses using t-tests, Bayesian statistics, and multiple lineair regressions. Also uses the custom functions: 'BF.evidence.R', 'cohen.d.magnitude.R' and 'p.value.sig.R', as well as the 'BF_t.R' file as taken from "Wiens, S. (2017). Aladins Bayes Factor in R. Stockholm University. https://doi.org/10.17045/sthlmuni.4981154.v3". - Files 'SET_CFC.outl.del.imp.RData' and 'SET_CFC.outl.del.imp.extra.RData' contain the imputed dataset that can be used to reproduce results from the paper.
DOI: 10.17632/4hc54vnzx6.3
title: Nuclear Safety, 2nd edition - Gianni Petrangeli

description: CALCULATION OF THE RELEASES DISPERSION : INSTANTANEOUS RELEASE CALCULATION OF RELEASES DISPERSION: INSANT. REL. WITH FUMIGATION CALCULATION OF THE RELEASE DISPERSION : CONTINUOUS RELEASE CALCULATION OF THE RELEASES DISPERSION : CONTINUOUS REL. WITH FUMIGATION CALCULATION OF THE DUHAMEL INTEGRAL OF AN ACCELEROGRAM WITH 1000 POINTS IN COLUMNS A AND B (ROWS 15 &gt; 1014) CALCULATION OF TEMPERATURES IN A DRY CORE PRESCONT PROGRAM "PS" : INPUT DATA AND LAST STEP DATA
DOI: 10.17632/g2tf2vgprz.1
title: Data for MS#2019WR026651

description: Data used in the manuscript submitted to Water Resources Research. The manuscript number is 2019WR026651.
DOI: 10.5281/zenodo.3464541
title: Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics

description: Datasets for manuscript "Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics" <strong>Metadata.zip</strong> <strong>phenotypes.txt: </strong>tabular file containing binary resistance phenotypes based on CLSI guidelines, where the rows are the isolates and the columns correspond to different drugs. Resistance : 1, susceptibility: 0, missing: intermediate resistant <strong>Features_gpa_exp_snps.zip </strong> We provide the processed molecular data as Numpy compressed files (npz.). You can use the Numpy load method to read in these tables https://docs.scipy.org/doc/numpy/reference/generated/numpy.load.htm. The row (strains_list) and column labels (feature_lists) are stored separately. <strong>genexp</strong>: gene expression table directory genexp_feature_vect.npz: The feature matrix in the numpy format genexp_feature_list.txt: The columns of the feature matrix (features) genexp_strains_list.txt: The rows of the feature matrix (isolates) <strong>gpa: </strong>gene presence/absence table directory gpa_feature_vect.npz: The feature matrix in the numpy format gpa_feature_list.txt: The columns of the feature matrix (features) gpa_strains_list.txt: The rows of the feature matrix (isolates) <strong>snps: </strong>SNPs table directory snps_feature_vect.npz: The feature matrix in the numpy format snps_feature_list.txt: The columns of the feature matrix (features) snps_strains_list.txt: The rows of the feature matrix (isolates)
DOI: 10.5281/zenodo.3464542
title: Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics

description: Datasets for manuscript "Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics" <strong>Metadata.zip</strong> <strong>phenotypes.txt: </strong>tabular file containing binary resistance phenotypes based on CLSI guidelines, where the rows are the isolates and the columns correspond to different drugs. Resistance : 1, susceptibility: 0, missing: intermediate resistant <strong>Features_gpa_exp_snps.zip </strong> <strong>genexp</strong>: gene expression table directory genexp_feature_vect.npz: The feature matrix in the numpy format genexp_feature_list.txt: The columns of the feature matrix (features) genexp_strains_list.txt: The rows of the feature matrix (isolates) <strong>gpa: </strong>gene presence/absence table directory gpa_feature_vect.npz: The feature matrix in the numpy format gpa_feature_list.txt: The columns of the feature matrix (features) gpa_strains_list.txt: The rows of the feature matrix (isolates) <strong>snps: </strong>SNPs table directory snps_feature_vect.npz: The feature matrix in the numpy format snps_feature_list.txt: The columns of the feature matrix (features) snps_strains_list.txt: The rows of the feature matrix (isolates)
DOI: 10.21227/cjnz-4t55
title: Supplementary_Datasheet_Consensus-Based_Coordination_Flexible_Demand_Energy_Storage

description: This is the datasheet for the examined 30-unit test system including 14 DG (indexed from 1 to 14), 4 IG (indexed from 15 to 18), 4 FD (indexed from 19 to 22), 4 ID (indexed from 23 to 26) and 4 ES units (indexed from 27 to 30), and thedatasheet for the plug-and-play test system with 4 new plugged in DER including2DG (indexed from 31 to 32) and 2 FD(indexed from 33 to 34).
DOI: 10.21227/h0p9-b426
title: Supplementary_Datasheet_Consensus-Based_Coordination_Flexible_Demand_Energy_Storage

description: This is the datasheet for the examined 30-unit test system including 14 DG (indexed from 1 to 14), 4 IG (indexed from 15 to 18), 4 FD (indexed from 19 to 22), 4 ID (indexed from 23 to 26) and 4 ES units (indexed from 27 to 30), and thedatasheet for the plug-and-play test system with 4 new plugged in DER including2DG (indexed from 31 to 32) and 2 FD(indexed from 33 to 34).
DOI: 10.17632/kxg88nv3wg.1
title: Basalt & komatiite analyses, Agnew-Wiluna belt, Yilgarn, Australia

description: The aim of obtaining the data was to help constrain the stratigraphy of the Archean Agnew-Wiluna greenstone belt, Yilgarn Craton, Western Australia. Basalt samples were collected from the Cliffs (n=12) & Mount Keith (n=34) Ni deposits and the area around the Wiluna Au mines (n=25). In addition 6 samples from a 54 m intersection of mafic schists hosted within dacitic schists in the eastern-most felsic sequence at Albion Downs. All analyses were undertaken by Bureau Veritas (Ultratrace), Perth, Western Australia with major, minor and the trace elements Cl, Cr, Ni, S, and Zn determined by X-ray florescence. Au, Pd and Pt were analysed by Lead Collection Fire Assay-ICP-MS, LOI by gravimetry after ignition at 1000oC, and all other elements, including REE, by Fused Bead Laser Ablation ICP-MS.
DOI: 10.17632/f98p5ftvtc.1
title: Nitrogen induced two-step hydrolysis on epitaxial passivation for nitrided AISI 304L in borate buffer solutions with pH = 6.5-8.9

description: dataset for "Nitrogen induced two-step hydrolysis on epitaxial passivation for nitrided AISI 304L in borate buffer solutions with pH = 6.5-8.9"
DOI: 10.5281/zenodo.3244155
title: Data supplementing the article "Diatom DNA metabarcoding for biomonitoring : strategies to avoid major taxonomical and bioinformatical biases limiting molecular indices capacities" K. Tapolczai, F. Keck, A. Bouchez, F. Rimet, M. Kahlert and V. Vasselon submitted to "Frontiers in Ecology and Evolution" journal

description: These data supplement the article "Diatom DNA metabarcoding for biomonitoring : strategies to avoid major taxonomical and bioinformatical biases limiting molecular indices capacities" K. Tapolczai, F. Keck, A. Bouchez, F. Rimet, M. Kahlert and V. Vasselon submitted to "Frontiers in Ecology and Evolution" journal. The directory contains the following files: <strong>464_samples_fastq_files_(mothur).rar </strong>- contains the 464 fastq files proceed together during the Mothur bioinformatics treatments to produce the OTUs and ISUs tables. As the contig and the demultiplexing steps were performed by the sequencing platform, there is 1 fastq file per sample. From this 464 samples OTU/ISU tables, only information regarding 76 samples were used in this study and are listed in the "<strong>76_samples_list_(mothur).xlsx" </strong>file<strong>.</strong> <strong>76_samples_list_(mothur).xlsx </strong>- contains the information regarding the 76 samples used to create the OTUs and ISUs tables presented in the paper. <strong>76_samples_R1_R2_fastq_files(DADA2).rar - </strong>contains the raw demultiplexed fastq files (R1.fastq and R2.fastq) for each of the 76 samples used in this study to produce the ESVs table using the DADA2 bioinformatics pipeline.
DOI: 10.5281/zenodo.3244156
title: Data supplementing the article "Diatom DNA metabarcoding for biomonitoring : strategies to avoid major taxonomical and bioinformatical biases limiting molecular indices capacities" K. Tapolczai, F. Keck, A. Bouchez, F. Rimet, M. Kahlert and V. Vasselon submitted to "Frontiers in Ecology and Evolution" journal

description: These data supplement the article "Diatom DNA metabarcoding for biomonitoring : strategies to avoid major taxonomical and bioinformatical biases limiting molecular indices capacities" K. Tapolczai, F. Keck, A. Bouchez, F. Rimet, M. Kahlert and V. Vasselon submitted to "Frontiers in Ecology and Evolution" journal. The directory contains the following files: <strong>464_samples_fastq_files_(mothur).rar </strong>- contains the 464 fastq files proceed together during the Mothur bioinformatics treatments to produce the OTUs and ISUs tables. As the contig and the demultiplexing steps were performed by the sequencing platform, there is 1 fastq file per sample. From this 464 samples OTU/ISU tables, only information regarding 76 samples were used in this study and are listed in the "<strong>76_samples_list_(mothur).xlsx" </strong>file<strong>.</strong> <strong>76_samples_list_(mothur).xlsx </strong>- contains the information regarding the 76 samples used to create the OTUs and ISUs tables presented in the paper. <strong>76_samples_R1_R2_fastq_files(DADA2).rar - </strong>contains the raw demultiplexed fastq files (R1.fastq and R2.fastq) for each of the 76 samples used in this study to produce the ESVs table using the DADA2 bioinformatics pipeline.
DOI: 10.7910/dvn/puijiu/bqycua
title: ReadMe.rtf

description: :unav
DOI: 10.7910/dvn/puijiu/d8vz3x
title: 2. DiD Regressions.do

description: :unav
DOI: 10.7910/dvn/puijiu/gpr999
title: House Data Outcomes for DiD and RDD Analyses.RData

description: :unav
DOI: 10.7910/dvn/puijiu/lpl774
title: in_out_district_data.RData

description: :unav
DOI: 10.7910/dvn/puijiu/r966sy
title: House Primary Candidates Data.RData

description: :unav
DOI: 10.7910/dvn/puijiu/sa56nc
title: Candidate Data with Candidate Race and District and State Data.RData

description: :unav
DOI: 10.7910/dvn/puijiu/c4lexm
title: 3. DiD Figures.R

description: :unav
DOI: 10.7910/dvn/puijiu/mozcay
title: Population Race Statistics (Pew).csv

description: :unav
DOI: 10.7910/dvn/puijiu/z4dpmq
title: 4. RDD Results.R

description: :unav
DOI: 10.7910/dvn/puijiu/zrequa
title: 1. Aggregating Data for Regressions.R

description: :unav