Find research datasets worth reusing

openapache2.0Jan 2024View details →

Using RS-DAT to study continental-scale phenology at high-spatial resolution

This repository includes the notebooks employed to calculate and analyze a gridded phenological model, as computed from a set of daily meteorological variables.

opencc-by-4.0May 2023View details →

[Dataset & scripts] to "Spatial scales of kinetic energy in the Arctic Ocean", dataset from Caili Liu

## "Spatial scales of kinetic energy in the Arctic Ocean" Available dataset for each figure (1~9) and figure10 in the main text, including Jupyter notebook scripts (Fig1, Fig2, Fig5, Fig10) and Matlab scripts (Fig3, Fig4, Fig6, Fig7, Fig8, Fig9). ## Description This dataset is as the supplementary to the manuscript "Spatial scales of kinetic energy in the Arctic Ocean", including jupyter notebook scripts and matlab scripts of visualization directly for figures1~9. 1) Jupyter notebook scripts for visualization the MESH and BG are used for visualization, and *.mat are the dataset for Fig1/2/5/10. The load path in the script should be changed to your files accordingly. 2) Matlab scripts for plots All figures/panels are directly produced, but it is composed of panels for Fig7/8/9 additionally.

opencc-by-sa-4.0Sep 2024View details →

MeanDRS River Width Sampling: Data products corresponding to "Intrinsic spatial scales of river stores and fluxes and their relative contributions to the global water cycle"

Corresponding peer-reviewed publication This dataset corresponds to all input and output files that were used in the study reported in: <ul> <li>Wade, J., David, C.H., Collins, E.L., Denbina, M., Cerbelaud, A., Tom, M., Reager, J.T., Frasson, R.P.M., Famiglietti, J.S., Lee, T., Gierach, M.M. (In Review), Intrinsic spatial scales of river stores and fluxes and their relative contributions to the global water cycle.</li> </ul> When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. Summary The Earth’s rivers vary in size across several orders of magnitude. Yet, the relative significance of small upstream reaches compared to large downstream rivers in the global water cycle remains unclear, challenging the determination of adequate spatial resolution for observations. Using monthly simulations of river stores and fluxes from the MeanDRS river routing dataset, we sample global rivers by a range of estimated river width thresholds to investigate the intrinsic spatial scales of the global river water cycle. We frame these scale-dependent river dynamics in terms of observational capabilities, assessing how the size of rivers that can be resolved influences our ability to capture key global hydrologic stores and fluxes. We aim to answer two questions: 1.     What is the intrinsic spatial resolution of global river dynamics? 2.     How can the spatial scale of river processes be used to inform efficient monitoring and modeling strategies of global river stores and fluxes? Data sources The following sources were used to produce files in this dataset: <ul> <li>Mean Discharge Runoff and Storage (MeanDRS) dataset (version v0.4) available under a CC BY-NC-SA 4.0 license. <a href="../records/10013744">https://zenodo.org/records/10013744</a>. DOI: 10.5281/zenodo.10013744; 10.1038/s41561-024-01421-5</li> <li>MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7) available under a CC BY-NC-SA 4.0 license. <a href="https://www.reachhydro.org/home/params/merit-basins">https://www.reachhydro.org/home/params/merit-basins</a></li> </ul> Software The software that was used to produce files in this dataset are available at https://github.com/jswade/meandrs-width-sampling. Data Products The following files represent the primary outputs of the analysis. Each file class generally has 61 files, corresponding to the 61 global hydrologic regions (region ii). Riv_coast.zip contains shapefiles of corrected and uncorrected MeanDRS river reaches that intersect with the global coast and are inferred to drain to the ocean. ·       riv_coast.zip     o      cor: riv_coast_pfaf_ii_COR.shp     o      uncor: riv_coast_pfaf_ii_UNCOR.shp   Qout_rivwidth.zip contains csv files of the aggregate river discharge to the ocean (km3/yr) of under each tested river width sampling scenario for each of the 61 global hydrologic regions. ·       Qout_rivwidth.zip: Qout_pfaf_ii_rivwidth.csv   V_rivwidth_low.zip contains csv files of the aggregate river storage (km3) for the low residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions. ·       V_rivwidth_low.zip: V_pfaf_ii_rivwidth_low.csv   V_rivwidth_nrm.zip contains csv files of the aggregate river storage (km3) for the normal (medium) residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions. ·       V_rivwidth_nrm.zip: V_pfaf_ii_rivwidth_nrm.csv   V_rivwidth_hig.zip contains csv files of the aggregate river storage (km3) for the high residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions. ·       V_rivwidth_hig.zip: V_pfaf_ii_rivwidth_hig.csv   Largest_rivs.zip contains files related to our analysis of the relative contributions of discharge to the ocean from the 10 largest global river basins. ·       largest_rivs.zip     o      cat: cat_dis_top10_nxx.shp – dissolved catchments of reaches draining from the 10 largest basins     o      csv: Q_df_top10.csv – total discharge contributed by each basin     o      riv: riv_top10_nxx.shp – river reaches that drain the 10 largest basins   Smallest_rivs.zip contains files related to our analysis of the relative contributions of discharge to the ocean from global rivers narrower than 100 m. ·       smallest_rivs.zip     o      cat: cat_pfaf_pfaf_ii_small_100m.shp – dissolved catchments of narrow reaches draining to the ocean for each region ii     o      csv: Q_df_top10.csv – total discharge to the ocean from each narrow river reach     o      riv: riv_pfaf_ii_small_100m.shp – river reaches narrower than 100 m that drain to the ocean for each region ii   Global_summary.zip contains files related to the global aggregation of our region-specific river width sampling estimates for discharge to the ocean and river storage. ·       global_summary.zip     o      Qout_rivwidth: global summary files for discharge to the ocean (km3/yr) under river width sampling     o      V_rivwidth_low: global summary files for total river storage (km3) for the low residence time scenario under river width sampling     o      V_rivwidth_nrm: global summary files for total river storage (km3) for the normal (medium) residence time scenario under river width sampling     o      V_rivwidth_hig: global summary files for total river storage (km3) for the hig residence time scenario under river width sampling     o      cat_small_gl: cat_dis_global_small_100m.shp – global dissolved catchments contributing to all rivers narrower than 100 m that drain to the ocean   Rivwidth_sens.zip contains files related to our supplemental analysis of the sensitivity of our width estimation approach to choice of input discharge dataset. Here, we compute estimated river widths using 3 versions of MeanDRS discharge outputs (VIC, CLSM, NOAH) and compare the results of river width sampling from those runs to that of the primary analysis. The file formats and explanations follow those presented above, with added information for the land surface model used to generate those discharge simulations. ·       Rivwidth_sens.zip     o      riv_coast     o      Qout_rivwidth_VIC     o      Qout_rivwidth_CLSM     o      Qout_rivwidth_NOAH     o      V_rivwidth_low_VIC     o      V_rivwidth_nrm_VIC     o      V_rivwidth_hig_VIC     o      V_rivwidth_low_CLSM     o      V_rivwidth_nrm_CLSM     o      V_rivwidth_hig_CLSM     o      V_rivwidth_low_NOAH     o      V_rivwidth_nrm_NOAH     o      V_rivwidth_hig_NOAH     o      global_summary_VIC     o      global_summary_CLSM     o      global_summary_NOAH   Cor_sens.zip contains files related to our supplemental analysis of the sensitivity use of corrected ensemble MeanDRS discharge and volume simulations as opposed to uncorrected ensemble simulations. Here, we repeat our primary analysis using only uncorrected simulations throughout, rather than performing river width sampling using corrected simulations. The file formats and explanations follow those presented above, with the files using uncorrected ensemble (ENS) discharge and storage values in contrast to the primary analysis. ·       Cor_sens.zip     o      Qout_rivwidth_ENS     o      V_rivwidth_low_ENS     o      V_rivwidth_nrm_ENS     o      V_rivwidth_hig_ENS     o      global_summary_ENS   Width_val.zip contains files related to our supplemental validation of river widths estimated from MeanDRS discharge simulations through comparison with optical measurements of widths from the Global River Widths from Landsat (GRWL) Databse (Allen & Pavelsky, 2018). ·       Width_val.zip: width_validation_pfaf_ii.csv   Known bugs in this dataset or the associated manuscript No bugs have been identified at this time.   References Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. Science, 361(6402), 585-588. https://doi.org/10.1126/science.aat0636 Collins, E. L., David, C. H., Riggs, R., Allen, G. H., Pavelsky, T. M., Lin, P., Pan, M., Yamazaki, D., Meentemeyer, R. K., & Sanchez, G. M. (2024). Global patterns in river water storage dependent on residence time. Nature Geoscience, 1–7. https://doi.org/10.1038/s41561-024-01421-5 Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., David, C. H., Durand, M., Pavelsky, T. M., Allen, G. H., Gleason, C. J., & Wood, E. F. (2019). Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches. Water Resources Research, 55(8), 6499–6516. https://doi.org/10.1029/2019WR025287 Yang, Y., Pan, M., Lin, P., Beck, H. E., Zeng, Z., Yamazaki, D., David, C. H., Lu, H., Yang, K., Hong, Y., & Wood, E. F. (2021). Global Reach-Level 3-Hourly River Flood Reanalysis (1980–2019). Bulletin of the American Meteorological Society, 102(11), E2086–E2105. https://doi.org/10.1175/BAMS-D-20-0057.1

opencc-by-4.0Apr 2021View details →

Fine-scale population spatialization data of China in 2018 based on real location-based big data

This data contains a geospatial population raster layer in GeoTIFF format with 1*1 km resolution for 31 provincial regions (2851 counties) of China in 2018 (pop2018.tif). It also provides the Tencent positioning data in 2018 (TN_hSum2018.tif), the table of statistical population of 2851 counties (statistical_population_2018_china_county.xls) and its vector map (statisitcal_pop.shp) and codes (code.docx).

opencc-by-4.0Nov 2014View details →

Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data

Overview: The Global Inundation Extent from Multi-Satellites (GIEMS; Prigent et al. 2007, Papa et al. 2010) downscaled at 15 arc-second (GIEMS-D15; Fluet-Chouinard et al. 2015) was produced through the downscaling of the GIEMS database (natively at 0.25°).  The downscaling procedure predicts the location of surface water cover with an inundation ranking surface generated by bagged decision trees. The decision trees were trained on binary presence/absence of wetland in the GLC2000 global land cover map (Bartholomé & Belward 2005) and used 13 topographic and hydrographic predictors derived from the SRTM-derived HydroSHEDS database (Lehner, Verdin & Jarvis 2008). The downscaling technique to three temporal aggregation of the GIEMS dataset representing three states of land surface inundation extents: mean annual minimum (MAMin; total area, 6.5 × 106 km2), mean annual maximum (MAMax; 12.1 × 106 km2), and long-term maximum (LTMax; 17.3 × 106 km2). The area of MAMin and MAMax from GIEMS were supplemented with the minimum area value from lakes, river and reservoirs from GLWD (Lehner & Döll 2004; classes 1,2,3). LTMax was corrected as the mean area from 3-year rolling maximum from GIEMS and the total wetland area from GLWD (classes 1-12). The accuracy of GIEMS-D15 reflects distribution errors introduced by the downscaling process as well as errors from the original satellite estimates. Yet, a comparison against independent regional wetland maps showed adequate agreement over large floodplains and wetlands. GIEMS-D15 offers a higher resolution delineation of inundated areas than originally offered by GIEMS, allowing for the assessment of global freshwater resources and the study of large floodplain and wetland ecosystems. Projection: WGS84 (EPSG:4326) Geographic extent: <ul> <li>Longitude: -180° to 180°</li> <li>Latitude: -56° to 84°</li> </ul> Spatial resolution: 15 arc-second (500m at equator) Legend (for discrete pixel values): <ul> <li>0 = Upland</li> <li>1 = Mean Annual Minimum (MAMin)</li> <li>2 = Mean Annual Maximum (MAMax)</li> <li>3 = Long Term Maximum (LTMax)</li> </ul>

opengpl-3.0-or-laterOct 2024View details →

Characterizing cell-type spatial relationships across length scales in spatially resolved omics data: data repository

<h1>CRAWDAD</h1> Spatially resolved omics (SRO) technologies enable the identification of cell types while preserving their organization within tissues. Application of such technologies offers the opportunity to delineate cell-type spatial relationships, particularly across different length scales, and enhance our understanding of tissue organization and function. To quantify such multi-scale cell-type spatial relationships, we develop CRAWDAD, Cell-type Relationship Analysis Workflow Done Across Distances, as an open-source R package with source code and additional documentation at https://jef.works/CRAWDAD/. During CRAWDAD's development, we generated simulated datasets and new cell-type annotations for human spleen data, provided here. The external datasets such as the mouse cerebellum, mouse embryo, mouse brain, and human breast cancer data used in the paper can be found in their original publication. See more information in CRAWDAD's data availability statement. <h2>Simulated Datasets</h2> <ul> <li>sim.csv: the simulated data. Used in Figure 1 b-g, Supplementary Figure 1 a-c, and Supplementary Figure 9 a-b.</li> <li>ext_sim.csv: the extended simulated data. Used in Supplementary Figure 1 d-f.</li> <li>null_sim_visualization.csv: the null simulated data. Used to generate the plots Supplementary Figure 2 a-d.</li> <li>null_sim_1.csv - null_sim_10.csv: the 10 null simulated datasets. Used to quantitatively compare CRAWDAD, Squidpy’s co-occurrence implementation, and Ripley’s K Cross.</li> </ul> <h2>HuBMAP Datasets</h2> <ul> <li>pkhl.csv: annotated cell types and positions of sample HBM389.PKHL.936 from donor HBM966.VNKN.965. Used in Figure 5 a-h, Supplementary Figure 5 a, Supplementary Figure 7 a-c, and Supplementary Figure 8 c. doi:10.35079/HBM389.PKHL.936</li> <li>xxcd.csv: annotated cell types and positions of sample HBM772.XXCD.697 from donor HBM966.VNKN.965. Used in Figure 5 d-h, Supplementary Figure 5 a-c, and Supplementary Figure 7 a-c. doi:10.35079/HBM772.XXCD.697</li> <li>fsld.csv: annotated cell types and positions of sample HBM342.FSLD.938 from donor HBM245.ZWNT.288. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM342.FSLD.938</li> <li>pbvn.csv: annotated cell types and positions of sample HBM825.PBVN.284 from donor HBM245.ZWNT.288. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM825.PBVN.284</li> <li>ksfb.csv: annotated cell types and positions of sample HBM556.KSFB.592 from donor HBM298.KGNJ.374. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM556.KSFB.592</li> <li>ngpl.csv: annotated cell types and positions of sample HBM568.NGPL.345 from donor HBM298.KGNJ.374. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM568.NGPL.345</li> </ul> <h2>External Datasets</h2> <ul> <li>Mouse cerebellum: Used in Figure 2 a-e, Supplementary Figure 3 a-b, Supplementary Figure 4 a-d, and Supplementary Figure 8 a.</li> <li>Mouse embryo: Used in Figure 2 f-j, Supplementary Figure 3 c-d, Supplementary Figure 4 e-h, and Supplementary Figure 8 b.</li> <li>Human breast cancer: Used in Figure 3 a-c.</li> <li>Mouse brains: Used in Figure 4 a-e.</li> </ul>

opencc-by-4.0Oct 2024View details →

Dataset for "Scaling of ultrashort-pulsed laser structuring processes for electromobility applications using a spatial light modulator"

The dataset represents the experimental data for publication "Scaling of ultrashort-pulsed laser structuring processes for electromobility applications using a spatial light modulator"

opencc-by-4.0Jun 2021View details →

Deep Learning Reach-level Estimates of Mean River Depth at the Conterminous United States Spatial Scale

Abstract: Estimates of riverine channel geometry play a vital role in the physical representation of stream networks in models used to predict flood and drought conditions, manage water resources, and increase our knowledge of fluvial conditions under a changing climate. A well established body of literature exists that explains the relationship between channel geometry parameters width, depth, and velocity to instantaneous river discharge using a log-log linear power-law regression. In this study, a state-of-the-art deep learning regression model is presented and compared against the power-law method to evaluate their ability to estimate cross-sectional mean river depth. Results reveal three key findings, the neural network: (1) decreases RMSE by 22% verse a CONUS scale power-law equation, (2) reduces prediction variance across Strahler stream orders, and (3) generally outperforms regional power-law equations with an average decrease in RMSE of 8.7% Lastly, a reach-level CONUS dataset of estimated mean river depth is delivered.   The deep learning model was trained using the following features: <ul> <li>AI - Mean aridity index of unit catchment - Trabucco and Zomer, 2019</li> <li>area - Upstream drainage area (km2) - P. Lin et al., 2020</li> <li>CLY - Mean clay content (mass percentage, %) of unit catchment - Hengl et al., 2017</li> <li>DOR - Stream segment degree of dam regulation (Scale 0. – 100.) - Grill et al., 2019</li> <li>Elev - Stream segment mean elevation - P. Lin et al., 2020</li> <li>K - Mean bedrock permeability of unit catchment surrounding stream segment - Huscroft et al., 2018</li> <li>LAI - Mean leaf area index of unit catchment - Zhu et al., 2013</li> <li>order - Strahler-Horton stream order - P. Lin et al., 2020</li> <li>P - Mean bedrock porosity of unit catchment - Huscroft et al., 2018</li> <li>QMEAN - Stream segment mean annual discharge (m3/s) - P. Lin et al., 2019</li> <li>Sin - Stream segment sinuosity - P. Lin et al., 2020</li> <li>Slp - Stream segment mean longitudinal slope - P. Lin et al., 2020</li> <li>SLT - Mean silt content (mass percentage, %) of unit catchment - Hengl et al., 2017</li> <li>SND - Mean sand content (mass percentage, %) of unit catchment - Hengl et al., 2017</li> <li>stream_wdth_va - Measured stream cross-sectional width (m) - Canova et al., 2016</li> <li>Urb - Mean urban fraction of unit catchment - Liu et al., 2018</li> </ul> The deep learning model was trained using the following label: <ul> <li>mean_depth_va - Measured stream mean depth (m) - Canova et al., 2016</li> </ul> Predictions of mean depth were made by replacing stream_wdth_va from Canova et al., (2016) with bankfull width estimates (width_m) from P. Lin et al., (2020). Missing records from the P. Lin et al., (2020) dataset were excluded when making predictions, thus there are missing reaches in the dataset.

edi44/100

Identifying the drivers and responses of abrupt changes across spatial and temporal scales in ecology: a review

Recently, the theoretical basis for understanding abrupt changes in ecosystems relative to regime shifts has emerged (Ratajczak et al. 2018). Abrupt changes are defined as, “substantial changes in the mean or variability of a system that occur in a short period of time relative to typical rates of change” (Ratajczak et al. 2018). Despite a driver-response framework to guide the environmental conditions under which abrupt changes are likely to occur coupled with many examples of unexpected changes from long-term ecological research, our theoretical basis of understanding of abrupt changes doesn’t include long-term scales, variability in drivers and responses, changes in the magnitude or direction of drivers, or the interactions among multiple drivers across spatiotemporal scales (sensu Ratajczak et al. 2018). Further, a critical review of the literature is lacking and essential to further understanding how common abrupt changes are detected and reported, as well as patterns and scales of drivers and responses of abrupt change in ecosystems. To address this knowledge gap, we searched the existing ecological literature for evidence and commonalities of abrupt change across ecosystems to identify commonalities and differences of abrupt change drivers and responses across terrestrial, freshwater, and marine ecosystems. We specifically asked the following questions: (1) How common are abrupt changes reported in the ecological literature? (2) How do driver and response temporal and spatial scales of abrupt changes compare and vary across terrestrial, freshwater, and marine ecosystem types? (3) Is there relative congruence between the temporal and spatial scale of drivers and responses? (3) What are common types of drivers and responses to abrupt changes, and how do they vary across ecosystem types? (4) What terms are most associated with drivers and responses of abrupt changes among ecosystem types?

openCC (other)Apr 2022View details →

opencc-by-4.0Dec 2019View details →

Darwin's naturalization conundrum can be explained by spatial scale: R replication code

1) Code for extracting climatic data: 1_extract_env.R 2_extract_env_county.R 3_relatePlotCounty.R 2) Code for generating composite phylogenies: 1_making_sunplin_trees.R 3) Code for relatedness analyses: DNH_calculations_PHY_obs.cluster.R DNH_calculations_PHY_random.cluster.R DNH_calculations_TAX.cluster_obs.R DNH_calculations_TAX.cluster_random.R

opencc-by-4.0Dec 2020View details →

[Database] Urban Water Consumption at Multiple Spatial and Temporal Scales. A Review of Existing Datasets

This file contains the complete catalog of datasets and publications reviewed in: Di Mauro A., Cominola A., Castelletti A., Di Nardo A.. Urban Water Consumption at Multiple Spatial and Temporal Scales. A Review of Existing Datasets. Water 2021.The complete catalog contains: <ul> <li>92 state-of-the-art water demand datasets identified at the district, household, and end use scales;</li> <li>120 related peer-reviewed publications;</li> <li>57 additional datasets with electricity demand data at the end use and household scales.</li> </ul> The following metadata are reported, for each dataset: <ul> <li>Authors</li> <li>Year</li> <li>Location</li> <li>Dataset Size</li> <li>Time Series Length</li> <li>Time Sampling Resolution</li> <li>Access Policy.</li> </ul> The following metadata are reported, for each publication: <ul> <li>Authors</li> <li>Year</li> <li>Journal</li> <li>Title</li> <li>Spatial Scale</li> <li>Type of Study: Survey (S) / Dataset (D)</li> <li>Domain: Water (W)/Electricity (E)</li> <li>Time Sampling Resolution</li> <li>Access Policy</li> <li>Dataset Size</li> <li>Time Series Length</li> <li>Location</li> </ul> Authors: Anna Di Mauro - Department of Engineering | Università degli studi della Campania Luigi Vanvitelli (Italy) |  <a href="mailto:anna.dimauro@unicampania.it">anna.dimauro@unicampania.it</a>; Andrea Cominola - Chair of Smart Water Networks | Technische Universität Berlin - Einstein Center Digital Future (Germany) |  <a href="mailto:andrea.cominola@tu-berlin.de">andrea.cominola@tu-berlin.de</a>;  Andrea Castelletti - Department of Electronics, Information and Bioengineering | Politecnico di Milano (Italy) | <a href="mailto:andrea.castelletti@polimi.it">andrea.castelletti@polimi.it</a> Armando Di Nardo -Department of Engineering | Università degli studi della Campania Luigi Vanvitelli (Italy) |   <a href="mailto:armando.dinardo@unicampania.it">armando.dinardo@unicampania.it</a> Citation and reference: If you use this database, please consider citing <a href="https://www.mdpi.com/2073-4441/13/1/36">our paper</a>  Di Mauro, A., Cominola, A., Castelletti, A., & Di Nardo, A. (2021). Urban Water Consumption at Multiple Spatial and Temporal Scales. A Review of Existing Datasets. Water, 13(1), 36, https://doi.org/10.3390/w13010036 Updates and Contributions: The catalogue stored in this public repository can be collaboratively updated as more datasets become available. The authors will periodically update it to a new version.  New requests can be submitted to the authors, so that the dataset collection can be improved by different contributors. Contributors will be cited, step by step, in the updated versions of the dataset catalogue. Updates history: <ol> <li>March 1st, 2021 - Pacheco, C.J.B., Horsburgh, J.S., Tracy, J.R. (Utah State University, Logan, UT - USA) --- The dataset associated with paper <a href="https://doi.org/10.3390/s20133655">Bastidas Pacheco, C.J.; Horsburgh, J.S.; Tracy, R.J.. A Low-Cost, Open Source Monitoring System for Collecting High Temporal Resolution Water Use Data on Magnetically Driven Residential Water Meters. Sensors 2020, 20, 3655.</a> is published in the HydroShare repository, where it is available as an OPEN dataset. Data can be found here: <a href="https://doi.org/10.4211/hs.4de42db6485f47b290bd9e17b017bb51">https://doi.org/10.4211/hs.4de42db6485f47b290bd9e17b017bb51</a></li> </ol>

dryad40/100

The impact of within-host coinfection interactions on between-host parasite transmission dynamics varies with spatial scale

Within-host interactions among coinfecting parasites can have major consequences for individual infection risk and disease severity. However, the impact of these within-host interactions on between-host parasite transmission, and the spatial scales over which they occur, remain unknown. We developed and applied a novel spatially explicit analysis to parasite infection data from a wild wood mouse (Apodemus sylvaticus) population. We previously demonstrated a strong within-host negative interaction between two wood mouse gastrointestinal parasites, the nematode Heligmosomoides polygyrus, and the coccidian Eimeria hungaryensis, using drug-treatment experiments. Here, we show this negative within-host interaction can significantly alter the between-host transmission dynamics of E. hungaryensis, but only within spatially-restricted neighbourhoods around each host. However, for the closely related species E. apionodes, which experiments show does not interact strongly with H. polygyrus, we did not find any effect on transmission over any spatial scale. Our results demonstrate that the effects of within-host coinfection interactions can ripple out beyond each host to alter the transmission dynamics of the parasites, but only over local scales that likely reflect the spatial dimension of transmission. Hence there may be knock-on consequences of drug treatments impacting the transmission of non-target parasites, altering infection risks even for non-treated individuals in the wider neighbourhood.

opencc-zeroMar 2024View details →

F I G U R E 3 A in A low-density single nucleotide polymorphism panel for brown trout (Salmo trutta L.) suitable for exploring genetic diversity at a range of spatial scales

F I G U R E 3 A priori discriminant analysis of principal components (DAPC) plot of Camel trout. Each point represents the genotype of an individual fish, with centroids for each site labelled. Discriminant function 1 (DF1) is represented by the x axis, and discriminant function 2 (DF2) by the y-axis

F I G U R E 1 in A low-density single nucleotide polymorphism panel for brown trout (Salmo trutta L.) suitable for exploring genetic diversity at a range of spatial scales

F I G U R E 1 Map showing the location of rivers sampled for brown trout within the UK, France and Ireland. The left panel shows the rivers used to assess the performance of the single nucleotide polymorphisms (SNP) panel at characterising genetic parameters within and outside the target region. The top right (blue) panel shows the locations of the four sampled rivers in Mount's Bay, Cornwall (Case Study 1). The bottom right (red) panel shows the location of the sample locations in the Camel catchment (Case Study 2). The red box within the bottom right panel gives the position of the impassable De Lank quarry site

F I G U R E 2 A in A low-density single nucleotide polymorphism panel for brown trout (Salmo trutta L.) suitable for exploring genetic diversity at a range of spatial scales

F I G U R E 2 A priori discriminant analysis of principal components (DAPC) of trout genotypes from rivers flowing into Mount's Bay, Cornwall. Individuals are represented by individual points, with centroids for each river labelled. Discriminant function 1 (DF1) is represented by the x axis, and discriminant function 2 (DF2) by the y-axis

F I G U R E 4 in A low-density single nucleotide polymorphism panel for brown trout (Salmo trutta L.) suitable for exploring genetic diversity at a range of spatial scales

F I G U R E 4 Correlation between geographic distance (km) against genetic distance (linear FST) for the trout samples from the River Camel. The red points represent those between the De Lank and all other sites, the black points for all pair-wise comparisons excluding the De Lank. Linear regression for all sites including the De Lank is given by the red line (r2 = 0.321, P = 0.231), and linear regression for all pair-wise sites excluding the De Lank is given by the black line (r2 = 0.658, P = 0.0671)