Find research datasets worth reusing

opencc-by-4.0Mar 2020View details →

Murine norovirus virulence factor 1 (VF1) protein contributes to viral fitness during persistent infection [Primary data]

Primary data underlying journal article titled "Murine norovirus virulence factor 1 (VF1) protein contributes to viral fitness during persistent infection"

opencc-by-4.0Mar 2024View details →

A fading radius valley towards M-dwarfs, a persistent density valley across stellar types -- data

Open the record for dataset details and reuse information.

opencc-by-4.0Sep 2022View details →

Probing center vortices and deconfinement in SU(2) lattice gauge theory with persistent homology — data release

This release contains all data used to prepare the publication <a href="https://arxiv.org/abs/2207.13392">Probing center vortices and deconfinement in SU(2) lattice gauge theory with persistent homology</a>. Included are: <ul> <li>The raw log output from the simulations and computed persistence images for the analysis in Section IV.B of the <a href="https://arxiv.org/abs/2207.13392">paper</a> in 'raw_data.zip'.</li> <li>The values of the action and Polyakov loop from the above logs, along with the persistence images restructured into netCDF4 format for convenience, in the files 'Nt=*_Ns=*_pis_actions_polyakovs.nc'.</li> <li>The values of the observable m_2 (as defined in the <a href="https://arxiv.org/abs/2207.13392">paper</a>) for configurations for the twisted boundary conditions analysis in netCDF4 format in 'Nt=4_Ns=12_16_20_m2.nc'.</li> <li>The example persistence diagrams used in the <a href="https://arxiv.org/abs/2207.13392">paper</a> in netCDF4 format in 'Nt=4_Ns=12_example_pds.nc'.</li> </ul>

opencc-by-4.0Apr 2019View details →

Dataset to "Persistent Identifiers for File Formats: enabling preservation and re-use of research data"

This fileset includes a "preprint" and the main dataset fileformatRecognizer (as .xlsx and .csv) to the paper "Persistent identifiers for file formats: enabling preservation and re-use of research data" submitted to iPRES 2019, but subsequently rejected after peer review.  For the sake of transparency, permission to make available here the anonymous reviews motivating the rejection (ReviewsPIDs4fileFormats.odt) was asked, but was left without response. Some images (screendumps) and text result files from file identification tools tested are included. Further, a simple xquery command file (BaseX) for fetch:content-type(), used for getting MIME-types for files, is also provided.

opencc-by-4.0Jan 2021View details →

Data availability for "Photolytic Radical Persistence due to Anoxia in 1 Viscous Aerosol Particles"

Data availability for the paper titled "Photolytic Radical Persistence due to Anoxia in 1 Viscous Aerosol Particles" by Peter A. Alpert et al. This repository contains all data tables and files necessary to reproduce plots. Also included are open source ".hdf5” files that contain all data for X-ray microscopy images and ".dat" files having the raw data for mie resonance scattering to derive size change and mass loss. Please see the "Readme.pdf" file for more information.

opencc-by-4.0Apr 2024View details →

Data and analysis for: "Persistent Spatial Clustering and Predictors of Pediatric La Crosse Virus Neuroinvasive Disease Risk in Eastern Tennessee and Western North Carolina, 2003–2020"

This is the initial release of the data and code corresponding to the manuscript submitted to PLoS Neglected Tropical Diseases. Please refer to the README.md file for a description of the contents of this repository and how to use them. The README file can be opened with a text editor, or viewed directly in the GitHub repository. The data and code are provided within a project directory with a reproducible R package library for ease and accuracy of reproducibility.  Ethics Approval This study was approved by the University of Tennessee, Knoxville Institutional Review Board (UTK IRB-22-07079-XP) and the Tennessee Department of Health Institutional Review Board (TDH IRB 2021-0314). Data provided here is de-identified and aggregated (both temporally and spatially) to protect the privacy of individuals included in the study, in concordance with IRB and Data Use Agreements.

opencc-by-4.0Apr 2022View details →

data set to bioRxiv preprint 'Persistent cross-species SARS-CoV-2 variant infectivity predicted via comparative molecular dynamics simulation

This is supporting data and software code for the following preprint in bioRxiv Persistent cross-species SARS-CoV-2 variant infectivity predicted via comparative molecular dynamics simulation https://www.biorxiv.org/content/10.1101/2022.04.18.488629v1

opencc-by-4.0Mar 2023View details →

Data from: Is a community state reachable, and why?, and Coexistence and collapse: an experimental investigation of the persistent communities of a protist species pool

Deterministic models have difficulties to take into account stochasticity during community assembly. As a tool to circumvent this problem, we present a qualitative discreteevent model, where consequences of interspecific interactions are described as rules. This model provides a map of all possible future dynamics for a given system, which allows to exhaustively describe the possible pathways during an assembly process. Such a description does not rely on species traits details and is insensitive to stochastic effects. This allows to show that subsets of species are sometimes impossible to reach starting from larger sets of species, and therefore to question the reachability of community states during the system’s dynamics. Applying the model to an experimental dataset studying the collapse of protist communities, we obtain a very good theory-experiment agreement. We finally discuss what the notion of reachability can bring to community assembly.

opencc-by-4.0Apr 2023View details →

Data from: Mammal persistence and abundance in tropical rainforest remnants in the southern Western Ghats, India

This dataset contains data from the following publication: Sridhar, H., Raman, T. R. S. & Mudappa, D. 2008. <a href="https://www.currentscience.ac.in/Volumes/94/06/0748.pdf">Mammal persistence and abundance in tropical rainforest remnants in the southern Western Ghats, India</a>. Current Science 94: 748-757. URL: <a href="https://www.currentscience.ac.in/Volumes/94/06/0748.pdf">https://www.currentscience.ac.in/Volumes/94/06/0748.pdf</a> URL2: <a href="https://www.jstor.org/stable/24100628">https://www.jstor.org/stable/24100628</a> Corrigendum: Sridhar, H., Raman, T. R. S. & Mudappa, D. 2009. <a href="https://www.currentscience.ac.in/Volumes/97/05/0612.pdf">Corrigendum: mammal persistence and abundance in tropical rainforest remnants in the southern Western Ghats, India</a>. Current Science 97: 612-613. URL: <a href="https://www.currentscience.ac.in/Volumes/97/05/0612.pdf">https://www.currentscience.ac.in/Volumes/97/05/0612.pdf</a> Description of dataset: The data contains detections of mammals and hornbills (and few incidental records of other species) made along line transect surveys and opportunistic surveys in the Valparai Plateau and Anamalai Tiger Reserve, Tamil Nadu, India. Further details of the Study Area and methods are available in Sridhar et al. (2008), but methods are briefly described below. Five rainforest patches were chosen within IGWLS and four privately-owned rainforest fragments in the Valparai plateau. Fifteen line transects, ranging in length from 1 to 3 km were laid across the nine sites, with the three largest sites having 2–4 transects each. The total distance covered by all transects was 32.02 km. Each transect was walked five times between September 2005 and April 2006 following standard distance sampling protocol. Two observers walked each transect at 0.75–1 km/h. For each detection, we recorded species, group size and perpendicular distance (measured using a rangefinder) from the transect. For animals which occurred in groups, perpendicular distances were measured to group centres. Apart from detections on transects, attempts were made to obtain group sizes of mammal species whenever incidentally detected. All transects were walked between 0630 and 1000 h. Indirect evidence (scat, tracks) on transects and incidental sightings (direct and indirect) of mammals were also recorded. AUTHOR #1 1. Name: Hari Sridhar 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India 3. Current Work Address: Konrad Lorenz Institute for Evolution and Cognition Research, A-3400 Klosterneuburg, Austria 4. Email address: harisridhar1982@gmail.com 5. ORCID: https://orcid.org/0000-0003-3286-0120 AUTHOR #2 1. Name: T. R. Shankar Raman 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India 3. Work Phone: +91 821 2515601 4. Email address: trsr@ncf-india.org 5. ORCID: https://orcid.org/0000-0002-1347-3953 AUTHOR #3 1. Name: Divya Mudappa 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India 3. Work Phone: +91 821 2515601 4. Email address: divya@ncf-india.org 5. ORCID: https://orcid.org/0000-0001-9708-4826 Keywords: tropical rainforest, tea plantation, coffee plantation, line transect, population density, distance sampling, Anamalai Tiger Reserve, Valparai Plateau, Anamalai Hills, Western Ghats, mammals, hornbills Geographic Coverage: 1. Location/Study Area: Valparai Plateau, Tamil Nadu, India; Anamalai Tiger Reserve, Tamil Nadu, India 2. GPS coordinates: Valparai Plateau (10°15'- 10°22'N, 76°52' - 76°59'E); Anamalai Tiger Reserve (10°12' - 10°35'N, 76°49' - 77°24'E) Temporal Coverage: 1. Begins: 2005-09-01 (Year, Month, Day) 2. Ends: 2006-10-31 (Year, Month, Day)   Dataset files: Besides the 00_README.txt file, the dataset includes 4 comma-delimited text (csv) files with the data in columns as explained below: 01_Transect_locations.csv — contains transect location details and descriptions 02_Transects_and_opportunistic_surveys.csv — contains main dataset of observations on line transect and opportunistic surveys 03_Opportunistic_observations_locations.csv — contains location details of opportunistic surveys 04_Lion-tailed_macaque_counts.csv — contains counts of lion-tailed macaque (Macaca silenus) troops 05_allmammals_raw.xls — raw data NOT for use, for reference only in original Microsoft Excel format   Data variables and descriptions: 01_Transect_locations.csv TransectCode: Unique transect code (as used in Appendix 1 of Sridhar et al. 2008 paper), labelled as EXTRA for opportunistic surveys and incidental observations TransectLength_m: Length of line transect in metres decimalLatitude: approximate midpoint latitude in decimal degrees (N), WGS84 datum decimalLongitude: approximate midpoint longitude in decimal degrees (E), WGS84 datum StartLat: transect starting point latitude in decimal degrees (N), WGS84 datum StartLon: transect starting point longitude in decimal degrees (E), WGS84 datum EndLat: transect ending point latitude in decimal degrees (N), WGS84 datum EndLon: transect ending longitude in decimal degrees (E), WGS84 datum MidLat: approximate mid-way location latitude in decimal degrees (N), WGS84 datum MidLon: approximate mid-way longitude in decimal degrees (E), WGS84 datum ExtraLatLon: additional pairs of latitude and longitude points along transect in decimal degrees (E, N), WGS84 datum RouteDescription: description of transect route 02_Transects_and_opportunistic_surveys.csv eventID: unique ID of sampling event corresponding to a single on-foot survey of a line transect, with elements separated by colons and last two elements referring to TransectCode and replicate survey number occurrenceID: unique ID assigned to each occurrence (detection) along line transect Sno: serial number TransectCode: Unique transect code (as used in Appendix 1 of Sridhar et al. 2008 paper), labelled as EXTRA for opportunistic surveys and incidental observations locality: name of transect Transectno: unique number assigned to each transect survey or resurvey Replicate: number indicating repeat survey of same transect SiteCategory: Category indicating whether transcet was in Protected Area or Rainforest Fragment Date: date of transect survey or opportunistic observation Weather: Weather at time of survey Habitat: Habitat where observation was made Time: time in 24 h HH:MM format verbatimIdentification: Identification as originally entered scientificName: Scientific name of species or taxon observed vernacularName: Common English name of species or taxon observed Perpdist: Perpendicular distance in metres Freshness: rating of freshness of faeces found (d=day, wk=week, mt=month) individualCount: number of individuals counted, taken as minimum 1 if not noted in field rawNumber: number as originally entered DetectionType: type of observation classified as Call, Faeces, Indirect, Sighting, Track verbatimDetection: raw entry corresponding to previous column Height: height of observed animal above ground in metres occurrenceRemarks: remarks on occurrence 03_Opportunistic_observations_locations.csv locality: name of place or transect where opportunistic observation was made decimalLatitude: approximate midpoint latitude in decimal degrees (N), WGS84 datum decimalLongitude: approximate midpoint longitude in decimal degrees (E), WGS84 datum coordinateUncertaintyInMeters: approximate/estimated uncertainty in location coordinates (in metres) 04_Lion-tailed_macaque_counts.csv Sno: Serial number of entry Place_or_Transect: Transect (TransectCode) or place where lion-tailed macaques were counted Date: Date of observation Time: Time of observation in 24h HH:MM format Weather: Weather Groupid: ID of Lion-tailed macaque troop, if known Total: Total number of individuals recorded AM: number of adult males AF: number of  adult females A: number of adults (unsexed) SA: number of sub-adults (unsexed) SAM: number of sub-adult males SAF: number of sub-adult females JUV: number of juveniles INF: number of infants CARINF: number of infants carried by mother UNID: number of unclassified Remarks: other notes   05_allmammals_raw.xls Raw data file in Microsoft Excel format -- for reference only (not advised for use) ADDITIONAL NOTES General notes taken about survey: Pannimade transect 3/11/05 - Most giant squirrel detections were made after squirrel alarm called on seeing a soaring raptor. 36TH hpb transect - very poor visibility on one side as it is very steep Giant squirrels present within LTM  troops might go undetected. Need to look carefully and check every movement KO transect 20/01/06 - 1 GS which wasn’t detected when walking transect detected when measuring at less than 20 metres SHK transect 23/01/06 - Abandoned 100 metres from end because of elephants BAN - Ignore detections after 2.05 KM for first two replicates KSPV 26/01/2006 - 1 GS  not detected on transect detected while returning at < 40 m Anaigundi - Transect in december strayed slightly from correct path Var 30/01/2006 transect  Do not include for indirect signs encounter rate since replicates were done on consecutive days Is there a difference in visibility between wet and dry months; atleast in the more deciduous forests like varagaliar that is the case should I consider only january and afterwards for MGH  numbers since vocal activity is much higher then? TF transect 12/02/06 4 GS heard calling from coffee estate adjoining TF; could fewer detections on last transect be because they are moving into coffee, maybe because some tree is fruiting Do NL individuals move solitarily; what average group size to use visibility in BAN and VAR is much better than other sanctuary sites such as IYAK, AN, MA rained on 1st & 2nd of March after a long dry spell KSPV  31/03/06 - Could have missed some calls because of cicada noise KSWT  01/04/06 - Could have missed some calls because of cicada noise KSPV  02/04/06 - Could have missed some calls because of cicada noise KSPV 02/04/06  Do not include for indirect signs encounter rate since replicates were done on consecutive days Great hornbills seem to be more vocal during april. To do with end of nesting?? malabar grey hornbills more vocal from february onwards

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Data from "Grassland woody plant management rapidly changes woody vegetation persistence and abiotic habitat conditions but not herbaceous community composition"

These files contain microhabitat, soil, vegetation structure, and woody plant species data used in the paper "Grassland woody plant management rapidly changes woody vegetation persistence and abiotic habitat conditions but not herbaceous community composition". The project was conducted at seven publicly accessible remnant (i.e., unplowed or old-growth) tallgrass prairie within 100 miles of Madison, Wisconsin, United States starting in the 2020 growing season and commencing following the 2022 growing season. The goal was to assess the initial effects of different management interventions on woody vegetation persistence, abiotic habitat conditions, and herbaceous community composition, including physical and chemical management interventions and their combination.

openCC (other)Jun 2024View details →

opencc-zeroDec 2016View details →

Data from: Multimodal in situ datalogging quantifies inter-individual variation in thermal experience and persistent origin effects on gaping behavior among intertidal mussels (Mytilus californianus)

In complex habitats, environmental variation over small spatial scales can equal or exceed larger-scale gradients. This small-scale variation may allow motile organisms to mitigate stressful conditions by choosing benign microhabitats, whereas sessile organisms may rely on other behaviors to cope with environmental stresses in these variable environments. We developed a monitoring system to track body temperature, valve gaping behavior, and posture of individual mussels (Mytilus californianus) in field conditions in the rocky intertidal zone. Neighboring mussels' body temperatures varied by up to 14°C during low tides. Valve gaping during low tide and postural adjustments, which could theoretically lower body temperature, were not commonly observed. Rather, gaping behavior followed a tidal rhythm at a warm, high intertidal site; this rhythm shifted to a circadian period at a low intertidal site and for mussels continuously submerged in a tidepool. However, individuals within a site varied considerably in time spent gaping when submerged. This behavioral variation could be attributed in part to persistent effects of mussels' developmental environment. Mussels originating from a wave-protected, warm site gaped more widely, and they remained open for longer periods during high tide than mussels from a wave-exposed, cool site. Variation in behavior was modulated further by recent wave heights and body temperatures during the preceding low tide. These large ranges in body temperatures and durations of valve closure events - which coincide with anaerobic metabolism - support the conclusion that individuals experience "homogeneous" aggregations such as mussel beds in dramatically different fashion, ultimately contributing to physiological variation among neighbors.

opencc-zeroFeb 2020View details →

Data from: Long-term persistence of monotypic dengue transmission in small size isolated populations, French Polynesia, 1978-2014

Understanding the transition of epidemic to endemic dengue transmission remains a challenge in regions where serotypes co-circulate and there is extensive human mobility. French Polynesia, an isolated group of 72 inhabited islands, distributed among five geographically separated subdivisions, has recorded mono-serotype epidemics since 1944, with long inter-epidemic periods of circulation. Laboratory confirmed cases have been recorded since 1978, enabling exploration of dengue epidemiology under monotypic conditions in an isolated, spatially structured geographical location. A database was constructed of confirmed dengue cases, geolocated to island for a 35-year period. Statistical analyses of viral establishment, persistence and fade-out as well as synchrony among subdivisions were performed. Seven monotypic and one heterotypic dengue epidemic occurred, followed by low-level viral circulation with a recrudescent epidemic occurring on one occasion. Incidence was asynchronous among the subdivisions. Complete viral die-out occurred on several occasions with invasion of a new serotype, but also in the absence of any novel serotype. Island population size had a strong impact on the establishment, persistence and fade-out of dengue cases and endemicity was estimated achievable only at a population size in excess of 175 000. Despite island remoteness and low population size, dengue cases were observed somewhere in French Polynesia almost constantly, in part due to the spatial structuration generating asynchrony among subdivisions. Long-term persistence of dengue virus in this group of island populations may be enabled by island hopping, although could equally be explained by a reservoir of sub-clinical infections on the most populated island, Tahiti.

opencc-by-4.0Nov 2023View details →

Data and code corresponding to the article "Interaction network structure explains species temporal persistence in empirical plant-pollinator communities"

This upload contains the Datasets and code to generate the results of the article "Interaction network structure explains species temporal persistence in empirical plant-pollinator communities".The database comprises two files containing the abundances of plants and pollinators, and one containing the interaction networks among plants and pollinators. The code folder contains the code to generate the results, and to generate the figures of the manuscript.

opencc-zeroJan 2024View details →

R scripts, input and output data for: Season of death, pathogen persistence and wildlife behaviour alter number of anthrax secondary infections from environmental reservoirs

An important part of infectious disease management is predicting factors that influence disease outbreaks, such as R, the number of secondary infections arising from an infected individual. Estimating R is particularly challenging for environmentally transmitted pathogens given time lags between cases and subsequent infections. Here, we calculated R for Bacillus anthracis infections arising from anthrax carcass sites in Etosha National Park, Namibia. Combining host behavioural data, pathogen concentrations, and simulation models, we show that R is spatially and temporally variable, driven by spore concentrations at death, host visitation rates and early preference for foraging at infectious sites. While spores were detected up to a decade after death, most secondary infections occurred within two years. Transmission simulations under scenarios combining site infectiousness and host exposure risk under different environmental conditions led to dramatically different outbreak dynamics, from pathogen extinction (R<1) to explosive outbreaks (R>10). These transmission heterogeneities may explain variation in anthrax outbreak dynamics observed globally, and more generally, the critical importance of environmental variation underlying host-pathogens interactions. Notably, our approach allowed us to estimate the lethal dose of a highly virulent pathogen non-invasively from observational studies and epidemiological data, useful when experiments on wildlife are undesirable or impractical.

opencc-by-4.0Feb 2024View details →

Supporting material for von Fromm et al (2024) Global synthesis of SOC abundance and persistence profile data

This file contains the supporting material for von Fromm et al (2024) Controls and relationships of soil organic carbon abundance and persistence vary across pedo-climatic regions published in Global Change Biology (https://doi.org/10.1111/gcb.17320).  The data comes originally from the International Soil Radiocarbon Database (ISRaD; <a href="https://soilradiocarbon.org/">https://soilradiocarbon.org/</a>). We filtered the data and applied a mass-preserving spline function. For more details see the corresponding manuscript and the github repository (https://github.com/SophievF/Global_SOC_Abundance_Persistence).

opencc-zeroMar 2024View details →

Data from: Do pheromones contribute to the persistence of asexual populations in a facultatively parthenogenetic stick insect?

Facultative parthenogenesis is a form of reproduction in which females can either lay unfertilised eggs that typically develop into female offspring only, or mate and lay fertilised eggs that develop into male and female offspring. Facultative parthenogens often occur in mixed-sex populations where reproduction is mostly sexual, and all-female populations where reproduction is asexual. How all-female populations avoid invasion by males remains unknown. Here, we investigated the role of volatile and non-volatile (cuticular hydrocarbons, CHCs) pheromones in the persistence of all-female populations in the facultatively parthenogenetic stick insect, Megacrania batesii. We found that M. batesii exhibits slight sexual dimorphism in antenna morphology, and behavioural assays provided little evidence that males could locate females solely by volatile pheromones. However, CHC profiles differed substantially between different types of females. Analysis of CHC structure and abundance indicated a clear genetic difference between females from all-female versus mixed-sex populations, as well as a maternal effect of female parthenogenesis versus sexual development. Together, our results suggest that males might rely more on close-range chemical cues to differentiate females, and chemical communication could play a role in the persistence of all-female populations.

opencc-by-4.0Jul 2022View details →

Data and Software Archive for "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada"

This is the Zenodo archive for the manuscript "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada" (Mucaki EJ, Shirley BC and Rogan PK. F1000Research 2021, 10:1312, DOI: <a href="http://dx.doi.org/10.12688/f1000research.75891.1">10.12688/f1000research.75891.1</a>). This study aimed to produce community-level geo-spatial mapping of patterns and clusters of symptoms, and of confirmed COVID-19 cases, in near real-time in order to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals. This archive will contain data and image files from this study, which were too numerous to be included in the manuscript for this study. It also provides all program files pertaining to the Geostatistical Epidemiology Toolbox (Geostatistical analysis software package to be used in ArcGIS), as well as all other scripts described in this manuscript and other software developed (cluster, outlier, streak identification and pairing).. We also provide a guide which provides a general description of the contents of the four sections in this archive (Documentation_for_Sections_of_Zenodo_Archive.docx). If you have any intent to utilize the data provided in Section 3, we greatly advise you to review this document as it describes the output of all geostatistical analyses performed in this study in detail. Data Files: Section 1. "Section_1.Tables_S1_S7.Figures_S1_S11.zip" This section contains all additional tables and figures described in the manuscript "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada". Additional tables S1 to S7 are presented in an Excel document. These 7 tables provide summary statistics of various geostatistical tests described in the study (“Section 1 – Tables S1-S4”) and lists all identified single and paired high-case cluster streaks (“Section 1 – Tables S5-S7”). This section also contains 11 additional figures referred to in the manuscript (“Section 1 – Figures S1-S11”) both individually and within a Word document which describes them. Section 2. "Section_2.Localized_Hotspot_Lists.zip" All localized hotspots (identified through kriging analysis) were catalogued for each municipality evaluated (Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, Windsor/Essex). These files indicate the FSA in which the hotspot was identified, the date in which it was identified (utilizing 3-day case data at the postal code level), the amount of cases which occurred within the FSA within these 3 dates, the range of cases interpolated by kriging analysis (between 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40, 40-50, >50), and whether or not the FSA was deemed a hotspot by Gi* relative to the rest of Ontario on any of the three dates evaluated. Please see Section 4 for map images of these localized hotspots. Section 3. "Section_3.All-Data_Files.Kriging_GiStar_Local_and_GlobalMorans.2020_2021" Section 3 – All output files from the geostatistical tests performed in this study are provided in this section. This includes the output from Ontario-wide FSA-level Gi* and Cluster and Outlier analyses, and PC-level Cluster and Outlier, Spatial Autocorrelation, and kriging analysis of 6 municipal regions. It also includes kriging analysis of 7 other municipal regions adjacent to Toronto (Ajax, Brampton, Markham, Mississauga, Pickering, Richmond Hill and Vaughan). This section also provides data files from our analyses of stratified case data (by age, gender, and at-risk condition). All coordinates presented in these data files are given in “PCS_Lambert_Conformal_Conic” format. Case values between 1-5 were masked (appear as “NA”). Section 4. "Section_4.All_Map_Images_of_Geostat_Analyses.zip" Sets of image files which map the results of our geostatistical analyses onto a map of Ontario or within the municipalities evaluated (Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, Windsor/Essex) are provided. This includes: Kriging analysis (PC-level), Local Moran's I cluster and outlier analysis (FSA and PC-level), normal and space-time Gi* analysis, and all images for all analyses performed on stratified data (by age, gender and at-risk condition). Kriging contour maps are also included for 7 other municipal regions adjacent to Toronto (Ajax, Brampton, Markham, Mississauga, Pickering, Richmond Hill and Vaughan).  Software: This Zenodo archive also provides all program files pertaining to the Geostatistical Epidemiology Toolbox (Geostatistical analysis software package to be used in ArcGIS), as well as all other scripts described in this manuscript. This geostatistical toolbox was developed by CytoGnomix Inc., London ON, Canada and is distributed freely under the terms of the GNU General Public License v3.0. It can be easily modified to accommodate other Canadian provinces and, with some additional effort, other countries.  This distribution of the Geostatistical Epidemiology Toolbox does not include postal code (PC) boundary files (which are required for some of the tools included in the toolbox). The PC boundary shapefiles used to test the toolbox were obtained from <a href="https://www.dmtispatial.com/">DMTI</a> (<a href="https://www.google.com/url?q=https://www.dmtispatial.com/canmap/&sa=D&source=hangouts&ust=1637875735980000&usg=AOvVaw2wG3iVnyGyrkTIkN5FQ4NS">https://www.dmtispatial.com/canmap/</a>) through the Scholar's Geoportal at the University of Western Ontario (<a href="http://geo2.scholarsportal.info/">http://geo2.scholarsportal.info/</a>). The distribution of these files (through sharing, sale, donation, transfer, or exchange) is strictly prohibited. However, any equivalent PC boundary shape file should suffice, provided it contains polygon boundaries representing postal code regions (see guide for more details). Software File 1. "Software.GeostatisticalEpidemiologyToolbox.zip" The Geostatistical Epidemiology Toolbox is a set of custom Python-based geoprocessing tools which function as any built-in tool in the ArcGIS system. This toolbox implements data preprocessing, geostatistical analysis and post-processing software developed to evaluate the distribution and progression of COVID-19 cases in Canada. The purpose of developing this toolbox is to allow external users without programming knowledge to utilize the software scripts which generated our analyses and was intended to be used to evaluate Canadian datasets. While the toolbox was developed for evaluating the distribution of COVID-19, it could be utilized for other purposes.  The toolbox was developed to evaluate statistically significant distributions of COVID-19 case data at Canadian Forward Sortation Area (FSA) and Postal Code-level in the province of Ontario utilizing geostatistical tools available through the ArcGIS system. These tools include: 1) Standard Gi* analysis (finds areas where cases are significantly spatially clustered),  2) spacetime based Gi* analysis (finds areas where cases are both spatially and temporally clustered), 3) cluster and outlier analysis (determines if high case regions are an regional outlier or part of a case cluster), 4) spatial autocorrelation (determines the cases in a region are clustered overall) and, 5) Empirical Bayesian Kriging analysis (creates contour maps which define the interpolation of COVID-19 cases in measured and unmeasured areas). Post-processing tools are included that import these all of the preceding results into the ArcGIS system and automatically generate PNG images.  This archive also includes a guide ("UserManual_GeostatisticalEpidemiologyToolbox_CytoGnomix.pdf") which describes in detail how to set up the toolbox, how to format input case data, and how to use each tool (describing both the relevant input parameters and the structure of the resultant output files). Software File 2: “Software.Additional_Programs_for_Cluster_Outlier_Streak_Idendification_and_Pairing.zip" In the manuscript associated with this archive, Perl scripts were utilized to evaluate postal code-level Cluster and Outlier analysis to identify significantly, highly clustered postal codes over consecutive periods (i.e., high-case cluster “streaks”). The identified streaks are then paired to those in close proximity, based on the neighbors of each postal code from PC centroid data ("paired streaks"). Multinomial logistic regression models were then derived in the R programming language to measure the correlation between the number of cases reported in each paired streak, the interval of time separating each streak, and the physical distance between the two postal codes. Here, we provide the 3 Perl scripts and the R markdown file which perform these tasks: “Ontario_City_Closest_Postal_Code_Identification.pl” Using an input file with postal code coordinates (by centroid), this program identifies the nearest neighbors to all postal codes for a given municipal region (the name of this region is entered on the command line). Postal code centroids were calculated in ArcGIS using the “Calculate Geometry” function against DMTI postal code boundary files (not provided). Input from other sources could be used, however, as long as the input includes a list of coordinates with a unique label associated with a particular municipality. The output of this program (for the same municipal region being evaluated) is required for the following two Perl scripts: “Local_Morans_Analysis.Recurrent_Clustered_PC_Identifier.pl” This program uses the output of postal code-level Cluster and Outlier analysis for a municipality (these files are available in a second Zenodo archive: <a href="http://doi.org/10.5281/zenodo.5585812">doi.org/10.5281/zenodo.5585812</a>) and the output from “Ontario_City_Closest_Postal_Code_Identification.pl” (for the same municipal region) as input to identify high-case clustered postal codes that occur consecutively over a course of several dates (referred to as high-case cluster “streaks”). The script allows for a single day in which the PC was either not clustered or did not meet the minimum case count threshold of ≥ 6 cases within the 3-day sliding window (i.e. if clustered for 3 days, then not significant for one, then clustered for 3 more days, it will considered a 7 day streak). This script also lists any neighbors that are also identified to have streaks during these same dates. “Local_Morans_Analysis.Clustered_Streak_Pairing_Program.pl” This program uses the output from “Local_Morans_Analysis.Recurrent_Clustered_PC_Identifier.pl” to pair streaks that were identified in two closely situated postal codes spatially (requires output from “Ontario_City_Closest_Postal_Code_Identification.pl”). The output of this script provides the postal codes of the streaks which are paired, describe the interval of each streak (and whether they occur concurrently), the number of cases which occurred during these streaks, and how these streaks are separated (both distance [in meters] and temporally [in days]). "Streak_Analysis_using_Multinomial_Logistic_Regression_Models.Rmd" This R Markdown file contains the code which derived multinomial logistic regression models to describe the relation between the number of COVID-19 case counts, physical distance (in meters), and the time interval between paired streaks (in days). The script then performs a Wald two-tailed z-test to identify which factors are significantly correlated (relative to total case counts between streaks [i.e., the response variable]). The p-values computed from the Wald test are then reported. This script requires the 'multinom' function of the 'nnet' package in R. Two data files in which these models were derived (a list of all consecutive Toronto paired streaks for COVID-19 wave 2 and wave 3) are also included.

opencc-zeroMay 2024View details →

Data from: Climate change and population persistence in a hibernating marsupial

Climate change has physiological consequences on organisms, ecosystems, and human societies, surpassing the pace of organismal adaptation. Hibernating mammals are particularly vulnerable as winter survival is determined by short-term physiological changes triggered by temperature. In these animals, winter temperatures cannot surpass certain threshold, above which hibernators arouse from torpor, increasing several fold their energy needs when food is unavailable. Here, we parameterized a numerical model predicting energy consumption in heterothermic species, and modeled winter survival at different climate change scenarios. As a model species, we used the arboreal marsupial monito del monte (genus Dromiciops) which is recognized as one of the few South America hibernators. We modeled four climate change scenarios (from optimistic to pessimistic), based on IPCC projections, predicting that northern and coastal populations (Dromiciops bozinovici) will decline because the minimum number of cold days needed to survive the winter will not be attained. These populations are also the most affected by habitat fragmentation and change in land use. Conversely, Andean and other highland populations at cooler environments, are predicted to persist and thrive. Given the widespread presence of hibernating mammals around the world, models based on simple physiological parameters such as this one, are becoming essential for predicting species responses to warming in the short term.

opencc-by-4.0Apr 2018View details →

Research data for: On the persistent shape and coherence of pulsating auroral patches

Data for the research article: On the persistent shape and coherence of pulsating auroral patches in Journal of Geophysical Research: Space physics. For example all-sky imager movies of 557.7 nm pulsating/fluctuating aurora on 1 March 2012.