cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.15070803
title: VL Mosquito data

description: VL Mosquito data
DOI: 10.6084/m9.figshare.15070803.v1
title: VL Mosquito data

description: VL Mosquito data
DOI: 10.6084/m9.figshare.15102129
title: VL Temperature data - data logger raw

description: VL Temperature data - data logger raw
DOI: 10.6084/m9.figshare.15102150
title: TN Temperature - data logger raw

description: TN Temperature - data logger raw
DOI: 10.6084/m9.figshare.15102150.v1
title: TN Temperature - data logger raw

description: TN Temperature - data logger raw
DOI: 10.6084/m9.figshare.15102177
title: VL Cross sectional surveys

description: VL Cross sectional surveys
DOI: 10.6084/m9.figshare.15102180
title: TN Water Quality Survey

description: TN Water Quality Survey
DOI: 10.6084/m9.figshare.15102186
title: TN Water container surveys wMel prevalence

description: TN Water container surveys wMel prevalence
DOI: 10.6084/m9.figshare.15102186.v1
title: TN Water container surveys wMel prevalence

description: TN Water container surveys wMel prevalence
DOI: 10.6084/m9.figshare.15102195
title: VL Insecticide resistance

description: VL Insecticide resistance
DOI: 10.6084/m9.figshare.15102195.v1
title: VL Insecticide resistance

description: VL Insecticide resistance
DOI: 10.6084/m9.figshare.15102198
title: VL Wolbachia colony maternal transmission

description: VL Wolbachia colony maternal transmission
DOI: 10.6084/m9.figshare.15102204
title: VL Wolbachia colony infection rate

description: VL Wolbachia colony infection rate
DOI: 10.6084/m9.figshare.15102210
title: VL Fecundity and hatch rate

description: VL Fecundity and hatch rate
DOI: 10.6084/m9.figshare.15102210.v1
title: VL Fecundity and hatch rate

description: VL Fecundity and hatch rate
DOI: 10.5281/zenodo.5236440
title: Database of non-communicable disease reports contributing to UN high-level meeting process (2000-2020)

description: Database of the key non-communicable disease reports, policy papers, strategies and journal series published 2000-2020 that fed into the UN high-level meeting process
DOI: 10.5281/zenodo.5236439
title: Database of non-communicable disease reports contributing to UN high-level meeting process (2000-2020)

description: Database of the key non-communicable disease reports, policy papers, strategies and journal series published 2000-2020 that fed into the UN high-level meeting process
DOI: 10.5281/zenodo.4482135
title: Specimens from Brand et al. 2021, "Large-scale phylogenomics of the genus Macrostomum (Platyhelminthes) reveals cryptic diversity and novel sexual traits"

description: The deposited folders contain image and video material of free-living flatworm specimens that were documented in vivo. These data support the following publication: Brand, J. N., G. Viktorin, R. A. W. Wiberg, C. Beisel, and L. Schärer. 2019. Large-scale phylogenomics of the genus <em>Macrostomum </em>(Platyhelminthes) reveals cryptic diversity and novel sexual traits. in preparation. For more information about these specimens see also http://macrostomorpha.info.
DOI: 10.5281/zenodo.4585622
title: Results for 'Health and sustainability of glaciers in High Mountain Asia'

description: Contains 1 .csv file including the glacier health metrics used in Miles and others (2021) for all RGI glacier outlines larger than 2km2 in High Mountain Asia (regions 13/14/15). Also contains 1 .zip file with principal regridded inputs and results for continuity-derived glacier specific mass balances of High Mountain Asia, 2000-2016. A subdirectory contains the following for each glacier, identified by its Randolph Glacier Inventory identification number (RGIID), all in geotiff format and at the same resolution: '*_AW3D.tif': regridded digital elevation model based on the ASTER GDEM3 (apologies for misleading name) '*_debris.tif': binary rasterized debris-cover map based on the results of Scherler et al (2018) '*_dH.tif': regridded elevation change rate from Brun et al (2017), in m per year '*_dHe.tif': regridded elevation change rate uncertainty from Brun et al (2017), in m per year '*_FDIV.tif': raster of flux divergence, in m per year '*_FDIVe.tif': raster of flux divergence uncertainty, in m per year '*_Hdensity.tif': raster of estimated density of dH signal, in 1000 kg per m3 '*_SMB.tif': raster of specific mass balance, in m w.e. per year '*_SMBe.tif': raster of specific mass balance uncertainty, in m w.e. per year '*_Smean.tif': raster of column-average surface speed based on regridded data from ITS_LIVE (Gardner et al, 2019), in m per year '*_THX.tif': raster of glacier thickness from consensus estimate of Farinotti et al (2019), in m '*_zFDIV.tif': raster of zonally-aggregated flux divergence, in m per year '*_zFDIVe.tif': raster of zonally-aggregated flux divergence uncertainty, in m per year '*_zones.tif': raster of elevation-based zonal segmentation for each glacier '*_zSMB.tif': raster of zonally-aggregated specific mass balance, in m w.e. per year '*_zSMBe.tif': raster of zonally-aggregated specific mass balance uncertainty, in m w.e. per year
description: {"references": ["Scherler, Dirk; Wulf, Hendrik; Gorelick, Noel (2018): Supraglacial Debris Cover. V. 1.0. GFZ Data Services. http://doi.org/10.5880/GFZ.3.3.2018.005", "Brun, Fanny; Berthier, Etienne; Wagnon, Patrick; K\u00e4\u00e4b, Andreas; Treichler, D\u00e9sir\u00e9e (2017): Elevation changes of High Mountain Asia from 2000 to 2016, links to GeoTIFFs. PANGAEA, https://doi.org/10.1594/PANGAEA.876545", "Gardner, A. S., M. A. Fahnestock, and T. A. Scambos, 2019: ITS_LIVE Regional Glacier and Ice Sheet Surface Velocities. Data archived at National Snow and Ice Data Center; doi:10.5067/6II6VW8LLWJ7.", "Farinotti, D., Huss, M., F\u00fcrst, J. J., Landmann, J., Machguth, H., Maussion, F., & Pandit, A. (2019). A consensus estimate for the ice thickness distribution of all glaciers on Earth. Nature Geoscience, 12(March), 1. https://doi.org/10.1038/s41561-019-0300-3"]}
DOI: 10.3886/e148081
title: Population Health Assessment in Cancer Center Catchment Areas

description: We have determined racial disparities in cancer incidence and mortality within the University of Michigan Rogel Cancer Center catchment area, however, we know less about the underlying sociodemographic, psychosocial, and behavioral factors driving these disparities. In order to obtain data to plan effective strategies to reduce disparities, we conducted a survey in our catchment area, oversampling key demographic groups including Arab Americans (Middle Eastern and African Descent or MENA).<br><br>We used three methods of survey data collection in our catchment area: two non-random sampling strategies (community events and an online listed panel through SSI) and a “gold standard”, population probability telephone survey conducted by Harris Insights and Analytics.<br>
description: mail questionnaire; on-site questionnaire; self-enumerated questionnaire; telephone interview; web-based survey;
DOI: 10.3886/e148081v1
title: Population Health Assessment in Cancer Center Catchment Areas

description: We have determined racial disparities in cancer incidence and mortality within the University of Michigan Rogel Cancer Center catchment area, however, we know less about the underlying sociodemographic, psychosocial, and behavioral factors driving these disparities. In order to obtain data to plan effective strategies to reduce disparities, we conducted a survey in our catchment area, oversampling key demographic groups including Arab Americans (Middle Eastern and African Descent or MENA).<br><br>We used three methods of survey data collection in our catchment area: two non-random sampling strategies (community events and an online listed panel through SSI) and a “gold standard”, population probability telephone survey conducted by Harris Insights and Analytics.<br>
description: mail questionnaire; on-site questionnaire; self-enumerated questionnaire; telephone interview; web-based survey;
DOI: 10.6084/m9.figshare.14573787
title: Data & Code: Ghost of hosts past

description: Scripts and data to reproduce figures in "The ghost of hosts past: impacts of host extinction on parasite specificity" by Farrell et al. as part of the Phil Trans B Special Issue “Infectious disease macroecology: parasite diversity and dynamics across the globe"
DOI: 10.6084/m9.figshare.14573787.v1
title: Data & Code: Ghost of hosts past

description: Scripts and data to reproduce figures in "The ghost of hosts past: impacts of host extinction on parasite specificity" by Farrell et al. as part of the Phil Trans B Special Issue “Infectious disease macroecology: parasite diversity and dynamics across the globe"
DOI: 10.6084/m9.figshare.15164295
title: Data from: Inter-species interactions alter antibiotic efficacy in bacterial communities

description: The efficacy of antibiotic treatments targeting polymicrobial communities is not well predicted by conventional <i>in vitro</i> susceptibility testing based on determining minimum inhibitory concentration (MIC) in monocultures. One reason for this is that inter-species interactions can alter the community members’ susceptibility to antibiotics. Here we quantify, and identify mechanisms for, community-modulated changes of efficacy for clinically relevant antibiotics against a focal pathogen <i>Pseudomonas aeruginosa </i>in model cystic fibrosis lung communities derived from clinical samples. We demonstrate that multi-drug resistant <i>Stenotrophomonas maltophilia</i> can provide high levels of antibiotic protection to otherwise sensitive <i>P. aeruginosa. </i>Exposure protection to imipenem was provided by chromosomally encoded metallo-β-lactamase which detoxified the environment; protection was dependent upon <i>S. maltophilia</i> cell density and provided by clinically isolated strains, increasing the MIC of <i>P. aeruginosa</i> by up to 16-fold. In contrast, the presence of <i>S. maltophilia </i>provided no protection against meropenem, another routinely used carbapenem. Mathematical modelling shows that the level of exposure protection provided against different carbapenems can be explained by differences in antibiotic efficacy and inactivation rate. Together, these findings reveal that exploitation of pre-occurring antimicrobial resistance, and inter-specific competition, can have large impacts on pathogen antibiotic susceptibility, highlighting the importance of microbial ecology for designing successful antibiotic treatments for multi-species communities.
DOI: 10.6084/m9.figshare.15164295.v1
title: Data from: Inter-species interactions alter antibiotic efficacy in bacterial communities

description: The efficacy of antibiotic treatments targeting polymicrobial communities is not well predicted by conventional <i>in vitro</i> susceptibility testing based on determining minimum inhibitory concentration (MIC) in monocultures. One reason for this is that inter-species interactions can alter the community members’ susceptibility to antibiotics. Here we quantify, and identify mechanisms for, community-modulated changes of efficacy for clinically relevant antibiotics against a focal pathogen <i>Pseudomonas aeruginosa </i>in model cystic fibrosis lung communities derived from clinical samples. We demonstrate that multi-drug resistant <i>Stenotrophomonas maltophilia</i> can provide high levels of antibiotic protection to otherwise sensitive <i>P. aeruginosa. </i>Exposure protection to imipenem was provided by chromosomally encoded metallo-β-lactamase which detoxified the environment; protection was dependent upon <i>S. maltophilia</i> cell density and provided by clinically isolated strains, increasing the MIC of <i>P. aeruginosa</i> by up to 16-fold. In contrast, the presence of <i>S. maltophilia </i>provided no protection against meropenem, another routinely used carbapenem. Mathematical modelling shows that the level of exposure protection provided against different carbapenems can be explained by differences in antibiotic efficacy and inactivation rate. Together, these findings reveal that exploitation of pre-occurring antimicrobial resistance, and inter-specific competition, can have large impacts on pathogen antibiotic susceptibility, highlighting the importance of microbial ecology for designing successful antibiotic treatments for multi-species communities.
DOI: 10.6073/pasta/de4f0ae6a2d304db6a8033a6340501e7
title: SEV-LTER quadrat plant species cover and height all sites and experiments

description: This dataset includes plant species cover and height data measured in 1 m x 1 m quadrats at several sites and experiments under the Sevilleta LTER program. Quadrat locations span four distinct ecosystems and their ecotones: creosotebush dominated Chihuahuan Desert shrubland (est. winter 1999), black grama-dominated Chihuahuan Desert grassland (est. winter 1999), blue grama-dominated Plains grassland (est. winter 2002), and pinon-juniper woodland (est. winter 2003). Data on plant cover and height for each plant species are collected per individual plant or patch (for clonal plants) within 1 m x 1 m quadrats. These data inform population dynamics of foundational and rare plant species. In addition, using plant allometries, these non-destructive measurements of plant cover and height can be used to calculate net primary production (NPP), a fundamental ecosystem variable that quantifies rates of carbon consumption and fixation. Estimates of plant species cover, total plant biomass, or NPP can inform understanding of biodiversity, species composition, and energy flow at the community scale of biological organization, as well as spatial and temporal responses of plants to a range of ecological processes and direct experimental manipulations. The cover and height of individual plants or patches are sampled twice yearly (spring and fall) in permanent 1m x 1m plots within each site or experiment. This dataset includes core site monitoring data (CORE, GRIDS, ISOWEB, TOWER), observations in response to wildfire (BURN), and experimental treatments of extreme drought and delayed monsoon rainfall (EDGE), physical disturbance to biological soil crusts on the soil surface (CRUST), interannual variability in precipitation (MEANVAR), intra-annual variability via additions of monsoon rainfall (MRME), additions of nitrogen as ammonium nitrate (FERTILIZER), additions of nitrogen x phosphorus x potassium (NutNet), and interacting effects of nighttime warming, nitrogen addition, and El Niño winter rainfall (WENNDEx). To build allometric equations that relate biomass to plant cover or volume, a separate dataset of selectively harvested plant species is provided in "SEV-LTER Plant species mass data for allometry." Together, these datasets produce “SEV-LTER Plant biomass all sites and experiments” using the scripts posted with that dataset. Data from the CORE sites in this dataset were designated as NA-US-011 in the Global Index of Vegetation-Plot Databases (GIVD). Data from the TOWER sites in this dataset are linked to Ameriflux sites: ameriflux.lbl.gov/doi/AmeriFlux/US-Seg and ameriflux.lbl.gov/sites/siteinfo/US-Ses.
DOI: 10.5522/04/14482977
title: Cerebellar granule cell axons support high dimensional representations

description: This page provides the links to the raw data and analysis scripts for the figures in the following publication:<br>Lanore, F., Cayco-Gajic, N.A., Gurnani, H., Coyle, C., Silver, R.A. (2021). Cerebellar granule cell axons support high dimensional representations. Nature Neuroscience 24(8):1142-1150. doi: 10.1038/s41593-021-00873-x<br>In this study we used Acousto-Optic Lens (AOL) 3D two-photon microscopy to image large populations of granule cell axons (parallel fibres) in the cerebellar cortex of spontaneously behaving mice. We examined how different behavioral states and sensorimotor variables are represented in granule cell population activity. We characterized the manifold structure of the coding subspace and estimated dimensionality of the representations.<br> Main project page:<br>https://figshare.com/projects/Cerebellar_granule_cell_axons_support_high_dimensional_representations/101456<br>The fibre direction data is here:<br>https://figshare.com/articles/dataset/Fibre_Direction_Data/14976822<br>Functional data after grouping is here:<br>https://figshare.com/articles/dataset/Functional_Data/14976891<br><br>The Metadata is here:<br>https://figshare.com/articles/dataset/Metadata_Files/14976837<br>The code used for data processing and analysis can be found on <br><br>GitHub here:<br> https://github.com/SilverLabUCL/ParallelFibres<br>Please cite the paper if you use this code in your research. For any questions, contact Alex Cayco-Gajic (natasha.cayco.gajic@ens.fr) or Angus Silver (a.silver@ucl.ac.uk).<br>
DOI: 10.5522/04/14482977.v1
title: Cerebellar granule cell axons support high dimensional representations

description: This page provides the links to the raw data and analysis scripts for the figures in the following publication:<br>Lanore, F., Cayco-Gajic, N.A., Gurnani, H., Coyle, C., Silver, R.A. (2021). Cerebellar granule cell axons support high dimensional representations. Nature Neuroscience 24(8):1142-1150. doi: 10.1038/s41593-021-00873-x<br>In this study we used Acousto-Optic Lens (AOL) 3D two-photon microscopy to image large populations of granule cell axons (parallel fibres) in the cerebellar cortex of spontaneously behaving mice. We examined how different behavioral states and sensorimotor variables are represented in granule cell population activity. We characterized the manifold structure of the coding subspace and estimated dimensionality of the representations.<br> Main project page:<br>https://figshare.com/projects/Cerebellar_granule_cell_axons_support_high_dimensional_representations/101456<br>The fibre direction data is here:<br>https://figshare.com/articles/dataset/Fibre_Direction_Data/14976822<br>Functional data after grouping is here:<br>https://figshare.com/articles/dataset/Functional_Data/14976891<br><br>The Metadata is here:<br>https://figshare.com/articles/dataset/Metadata_Files/14976837<br>The code used for data processing and analysis can be found on <br><br>GitHub here:<br> https://github.com/SilverLabUCL/ParallelFibres<br>Please cite the paper if you use this code in your research. For any questions, contact Alex Cayco-Gajic (natasha.cayco.gajic@ens.fr) or Angus Silver (a.silver@ucl.ac.uk).<br>
DOI: 10.6084/m9.figshare.15226722
title: Abundance

description: Abundance: A Kraken 2 report provides information of the bacterial taxa in each sample.<br>Kindom, Phylum, Class, Order, Family, Genus: Each column contains name of taxonomic classification of each sampleColorOrder: Numeric data representing the order that each taxa was plotted in our manuscript.<br>HexColor: Color code in hex format for each taxa395 columns using sample names as column names: Numeric data as the relative abundance of each sample. Every column should sum to 1.<br>NOTE: 1. The (U)nclassified reads from kraken2 are not included in the calculation.2. The sample names of those columns are modified to be compatible with the format requirement in matlab. To convert the names back to match the SampleID in the rest of the files, 's' at the beginning of each column name should be removed, and underscore ('_') needs to be converted to period (eg. 'sFMT_0001A' will become 'FMT.0001A').
DOI: 10.6084/m9.figshare.15226722.v1
title: Abundance

description: Abundance: A Kraken 2 report provides information of the bacterial taxa in each sample.<br>Kindom, Phylum, Class, Order, Family, Genus: Each column contains name of taxonomic classification of each sampleColorOrder: Numeric data representing the order that each taxa was plotted in our manuscript.<br>HexColor: Color code in hex format for each taxa395 columns using sample names as column names: Numeric data as the relative abundance of each sample. Every column should sum to 1.<br>NOTE: 1. The (U)nclassified reads from kraken2 are not included in the calculation.2. The sample names of those columns are modified to be compatible with the format requirement in matlab. To convert the names back to match the SampleID in the rest of the files, 's' at the beginning of each column name should be removed, and underscore ('_') needs to be converted to period (eg. 'sFMT_0001A' will become 'FMT.0001A').
DOI: 10.6084/m9.figshare.13520123.v1
title: Raw data used in the PDIA manuscript

description: Raw data from the PDIA survey as well as metadata and journal rankings from Scimago and Ulrich's. This is the raw data used in the analysis described in the article Prestige or Perish: Publishing Decisions in Academic Archaeology approved for publication in American Antiquity.
DOI: 10.6084/m9.figshare.15173412
title: CARD

description: A table provides information of the antibiotic resistance genes in each sample. <br>Template: Hit of resistant genes in CARD<br>Accession: NCBI accession number of the template<br>Genome: Strain names where the template gene is found<br>Species: The species of the strain <br>resistGene: Gene name if available. <br>resistMechanism: Mechanism of resistance interpreted by CARD<br>Zoliflodacin-unknown (multiple columns): Antibiotics whether the gene is predicted to be resistant to, including unknown.<br>Score, Expected, Template_length, Template_Identity, Template_Coverage, Query_Identity, Query_Coverage, Depth, q_value, p_value: Parameters reported by CARD to show how well the match is.<br>shotgunReadcount: Number of reads for each sample after decontamination of human reads.<br>RelavantPercentInCARD: The number of reads matched to the template resistant gene / Total reads matched to all CARD genes in the sample.<br>PercentageInShotgun: The number of reads matched to the template resistant gene / Total reads in the sample.<br>Mutation: Information whether the antibiotic resistance is conferred by mutation.<br>SampleID: Sample ID for each shotgun sequencing
DOI: 10.6084/m9.figshare.15173412.v1
title: CARD

description: A table provides information of the antibiotic resistance genes in each sample. <br>Template: Hit of resistant genes in CARD<br>Accession: NCBI accession number of the template<br>Genome: Strain names where the template gene is found<br>Species: The species of the strain <br>resistGene: Gene name if available. <br>resistMechanism: Mechanism of resistance interpreted by CARD<br>Zoliflodacin-unknown (multiple columns): Antibiotics whether the gene is predicted to be resistant to, including unknown.<br>Score, Expected, Template_length, Template_Identity, Template_Coverage, Query_Identity, Query_Coverage, Depth, q_value, p_value: Parameters reported by CARD to show how well the match is.<br>shotgunReadcount: Number of reads for each sample after decontamination of human reads.<br>RelavantPercentInCARD: The number of reads matched to the template resistant gene / Total reads matched to all CARD genes in the sample.<br>PercentageInShotgun: The number of reads matched to the template resistant gene / Total reads in the sample.<br>Mutation: Information whether the antibiotic resistance is conferred by mutation.<br>SampleID: Sample ID for each shotgun sequencing
DOI: 10.6084/m9.figshare.15226875
title: ReadCounts

description: SampleID: Name of samplesReadcount: Number of reads for each sample after decontamination of human reads
DOI: 10.21227/agyw-ws11
title: LoRa (868 MHz) Path Loss Measurements in Double-slope Environment

description: The dataset contains the path loss measurements obtained with a LoRa (868 MHz) radio and five receivers. The receivers move in a search area covering both outdoor and indoor areas wherein a double-slope path loss is experienced. The data can be used to test range-based localization algorithm through the received signal strength.
DOI: 10.6084/m9.figshare.16200747
title: Taxa_Not_In_16S

description: Taxa absent in 16S gene sequencing but present in shotgun metagenomic sequencing in our 395 samples<br>TaxaName: Name of missing taxa<br>Taxa: Classification level of taxa names (eg. Genus)<br>frequencyPresentInShotgun: Counts of non-zero abundance found in shotgun sequencing(non-zero) / Total number of samples<br>medianRelAbdInShotgun: Median of relative frequency in shotgun metagenomic sequencing of that taxa