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Data and Software Archive for "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada"
<p>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. <em>F1000Research</em> 2021, <strong>10</strong>: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 <em>Geostatistical Epidemiology Toolbox </em>(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)..</p> <p>We also provide a guide which provides a general description of the contents of the four sections in this archive (<em>Documentation_for_Sections_of_Zenodo_Archive.docx</em>). 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.</p> <p><strong>Data Files:</strong></p> <p><strong>Section 1. "Section_1.Tables_S1_S7.Figures_S1_S11.zip"</strong></p> <p>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.</p> <p><strong>Section 2. "Section_2.Localized_Hotspot_Lists.zip"</strong></p> <p>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.</p> <p><strong>Section 3. "Section_3.All-Data_Files.Kriging_GiStar_Local_and_GlobalMorans.2020_2021"</strong></p> <p>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”).</p> <p><strong>Section 4. "Section_4.All_Map_Images_of_Geostat_Analyses.zip"</strong></p> <p>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). </p> <p><strong>Software:</strong></p> <p>This Zenodo archive also provides all program files pertaining to the <em>Geostatistical Epidemiology Toolbox </em>(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. </p> <p>This distribution of the <em>Geostatistical Epidemiology Toolbox </em>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).</p> <p><strong>Software File 1. "Software.GeostatisticalEpidemiologyToolbox.zip"</strong></p> <p>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. </p> <p>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. </p> <p>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).</p> <p><strong>Software File 2: “Software.Additional_Programs_for_Cluster_Outlier_Streak_Idendification_and_Pairing.zip"</strong></p> <p>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:</p> <p><em>“Ontario_City_Closest_Postal_Code_Identification.pl”</em></p> <p>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.</p> <p>The output of this program (for the same municipal region being evaluated) is required for the following two Perl scripts:</p> <p><em>“Local_Morans_Analysis.Recurrent_Clustered_PC_Identifier.pl”</em></p> <p>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 <em>“Ontario_City_Closest_Postal_Code_Identification.pl” </em>(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.</p> <p><em>“Local_Morans_Analysis.Clustered_Streak_Pairing_Program.pl”</em></p> <p>This program uses the output from “<em>Local_Morans_Analysis.Recurrent_Clustered_PC_Identifier.pl</em>” to pair streaks that were identified in two closely situated postal codes spatially (requires output from <em>“Ontario_City_Closest_Postal_Code_Identification.pl”)</em>. 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]).</p> <p>"<em>Streak_Analysis_using_Multinomial_Logistic_Regression_Models.Rmd</em>"</p> <p>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.</p> <p>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. </p>
Data archive for the peer-reviewed journal article "Information content and aerosol property retrieval potential for different types of in situ polar nephelometer data"
<p>Data archive accompanying the peer-reviewed journal article "Information content and aerosol property retrieval potential for different types of in situ polar nephelometer data". This article was accepted for publication in the journal <em>Atmospheric Measurement Techniques</em> in 2022. The original contributions presented in the study are included in the article and its supplementary information. The GRASP-OPEN model was used to perform forward calculations: this model is publicly available on the official GRASP website (https://www.grasp-open.com/; last access: 14 September, 2022). The specific GRASP-OPEN model outputs that were used for the study are contained in this data archive. </p>
Hash data files for "Hashes are not suitable to verify fixity of the public archived web"
<p>This work investigates the fixity of a set of archived webpages, or mementos. We conducted a study on 16,627 mementos from 17 public web archives. We replayed and downloaded the mementos 39 times using a headless browser over a period of 442 days and generated a hash for each memento after each download, resulting in 39 hashes per memento. The hashes were generated by creating Merkle trees to represent hashes at each level of the memento. A hash was generated for each resource used to construct the full webpage and then the hashes were combined to generate an overall hash for the composite memento.</p> <p>There are 39 data files, one for each download. </p> <p>The mementos downloaded come from the dataset at <a href="https://github.com/oduwsdl/mementos-fixity/">https://github.com/oduwsdl/mementos-fixity/</a></p>
Data archive: Trophic structure of cold-water coral communities revealed from the analysis of tissue isotopes and fatty acid composition
<p>Data belonging to the paper: </p> <p>Dick van Oevelen, Gerard C. A. Duineveld, Marc S. S. Lavaleye, Tina Kutti and Karline Soetaert (2017) Trophic structure of cold-water coral communities revealed from the analysis of 55 tissue isotopes and fatty acid composition. Marine Biology Research, DOI: https://doi.org/10.1080/17451000.2017.1398404</p> <p>Abstract:</p> <p>The trophic structure of cold-water coral reef communities at two contrasting locations, the 800-<br> m deep Belgica Mounds (Irish margin) and 300-m deep Træna reefs (Norwegian Shelf), was<br> investigated using stable isotope (δ13C and δ15N) and fatty-acid composition analysis. A<br> broad range of specimens, with emphasis on (commercial) fish species, and organic matter<br> sources were sampled using a variety of tools. Irrespective of the environmental and<br> geographical setting, the δ15N values indicated that the food web encompasses roughly 1.5<br> to 3 trophic levels. Mobile echinoderms, i.e. sea urchins and sea stars, had highest δ15N<br> values, indicative of a high trophic position in the food web. The fraction of bacterial fatty<br> acids in reef fauna was generally low (<5%), indicating that enhanced bacterial production in<br> the water column through seafloor seepage of nutrients (‘hydraulic theory’) does not form a<br> significant energy pathway into the food web. The high fraction of algal and essential fatty<br> acids in reef fauna and fish at both locations indicates a close coupling with surface<br> productivity, but the transport mechanism depends on the hydrographic setting. At Træna,<br> Calanus copepods and euphausiids form an additional link between primary production and<br> fish, which is largely absent at Belgica Mounds. At Belgica Mounds, the reef community is<br> primarily supported by phytodetritus, as evidenced by the high contribution of algal fatty<br> acids in faunal tissue and seasonal chlorophyll a deposition and marine snow at the reef. The<br> environmental setting of cold-water coral reefs influences the structure of the associated<br> food web.</p>
Data Archive for "Speckle Noise Reduction via Linewidth Broadening for Planetary Laser Reflectance Spectrometers"
<p>This archive contains the raw speckle images and experimental notes for the data contained in the journal article: "Speckle Noise Reduction via Linewidth Broadening for Planetary Laser Reflectance Spectrometers".</p> <p>The data available are:</p> <p>Raw speckle images for each of 5 illumination sources:</p> <ol> <li>Single-Frequence diode (files are named "IPS")</li> <li>Dual Mode pump diode (files are named "II-IV")</li> <li>Fabry Perot didoe (files are names "FP")</li> <li>Superluminescent Diode (files are named "SLD")</li> <li>Whilte light halogen source (files are named "WL")</li> </ol> <p>For each of theses source there are 5 images for each reflectance target, four speckle patterns and one background image with the laser source off. The naming convention is:</p> <p>LaserName_TargetReflectance_Target Rotation State or BKG.tiff</p> <p>For example, IPS_50_2 is the single-frequency laser using the 50% reflectance target and second rotation state of the target.</p> <p>The integration time for each image is give in the file "Experiment Paramters.csv" and was manually change to keep the maximum intensity at roughly 75% saturation.</p> <p>The spectra of the three laser sources presented in the manuscript are also included as .csv files</p>
Data archive: Niche overlap between a cold-water coral and an associated sponge for isotopically-enriched particulate food sources
<p>Data belonging to the paper: </p> <p>Dick van Oevelen, Christina E. Mueller, Tomas Lundälv, Fleur C. van Duyl, Jasper M. de Goeij, Jack J. Middelburg<span> </span>(In press) <strong>Niche overlap between a cold-water coral and an associated sponge for isotopically-enriched particulate food sources</strong>. PLOS ONE</p>
Data archive for Allen and Nettle, 'Hunger and socioeconomic background additively predict impulsivity in humans'
<p>This archive contains the raw data from Allen and Nettle, 'Hunger and socioeconomic background additively predict impulsivity in humans', plus R code for the data analyses. The impulsivity measure (HMDT) used in studies 2 and 3 is also included here. </p> <p>The R script 'IndividualAnalysis.R' performs the analysis of each study individually. 'MetaAnalysis.R' performs the meta-analysis. The three .csv files are the raw data from the three studies respectively. </p>
Data archive for Gott et al. 'Chronological age, biological age, and individual variation in the stress response in the European starling: A follow-up study'
<p>Data archive for Gott et al. 'Chronological age, biological age, and individual variation in the stress response in the European starling: A follow-up study'.</p> <p>Revised version of September 4 2018.</p> <p>Contains one data file and one R script to reproduce the analyses in the paper.</p>
Data archive for 'Cognitive bias in relation to developmental history and stress response in European starlings (Sturnus vulgaris)'
<p>Data files and R code for Gott et al. 'Cognitive bias in relation to developmental history and stress response in European starlings (Sturnus vulgaris)'.</p> <p>One .csv file gives the trial-by-trial data from the cognitive bias experiment. The other gives individual-level summary variables. Both are used by the R script.</p> <p>Uploaded 9th September 2018</p>
Data archive for 'Developmental history, energetic state and choice impulsivity in European starlings, Sturnus vulgaris'
<p>Data and code for Dunn et al. 'Developmental history, energetic state and choice impulsivity in European starlings, Sturnus vulgaris'</p> <p> </p> <p>The r script allows you to carry out all the analyses reported in the paper. Data from the experiment reported in the paper are 'databybird.csv'.</p> <p>In the paper, we also compare the findings of the present experiment with findings from a study of risk preference in the same birds (Andrews et al. 2018), and two earlier studies of impulsivity in different birds (Bateson et al. 2015, Nettle et al. 2015) . For convenience, we have included the relevant data from those earlier studies here too, though those data are all already published in association with the primary paper on those experiments.</p> <p>This version has a revised R script which tweaked some of the analyses in response to peer review.</p>
Illustrative Darwin core archive to input data on a citizen science platform from a collection management system
<p>Illustrative DwC archive to send data from a collection management system to a citizen sciences platform. This illustrative archive displays the specimens used for the trans-institutional and trans-platform pilot project held in the frame of ICEDIG.</p> <p>Further description of its content in the milestone28 document, worpackage 5.2 of the ICEDIG project.</p>
Code and data archive for Nettle et al. 'Consequences of measurement error in qPCR telomere data: A simulation study'
<p>Code and data for Nettle et al. 'Consequences of measurement error in qPCR telomere data: A simulation study'</p> <p>Updated version of March 2019</p> <p>Main simulation functions are contained in the script ‘simulation.functions.r’. When called, these functions (listed below) return datasets with requested properties containing both the ideal values of the quantities (Cqs, TS, etc.), and their post-error measured values. This allows the user to determine the differences between ideal and measured values, and perform other analyses. All simulation parameter values are user-specifiable. The script ‘paper.results.r’ reproduces all the figures and simulation results from the main paper. 'paper.results.r' also reads in the two .csv files of empirical data (dataset1 and dataset2).</p> <p>Datasets consist of observations from <em>n</em> individuals. The steps common to all of the simulation functions are as follows:</p> <ul> <li>A vector of <em>n </em>true single copy gene abundances, <em>true.dna.scg</em> is defined, drawn from a normal distribution with mean <em>b</em> and standard deviation <em>var.sample.size</em> (<em>b </em>is a constant).</li> <li>A vector of <em>n </em>relative telomere lengths, <em>true.telo.var</em> is defined, drawn from a normal distribution with mean 1 and standard deviation <em>telomere.var.</em></li> <li>Hence, the true abundance of the telomere sequence is defined, as <em>a*true.dna.scg*true.telo.var</em>. Here, <em>a</em> is a scaling constant representing how many copies of the telomeric sequence there are per single copy gene in the average sample.</li> <li>Ideal Cq values for both reactions are defined as <em>f – log<sub>2</sub>(true.dna.scg)</em> and <em>f – log<sub>2</sub>(true.dna.telo),</em> where <em>f</em> is a constant representing the chosen fluorescence threshold.</li> <li>Measurement errors in the Cqs are generated from a normal distribution with mean 0; standard deviations given by <em>error.scg</em> and <em>error.telo</em>; and a correlation between <em>error.scg</em> and <em>error.telo</em> given by <em>error.cor</em>.</li> <li>Hence, measured Cqs are generated, which can be compared to the ideal Cq values.</li> <li>TS ratios are calculated both on the measured Cqs, and the ideal ones.</li> </ul> <p>The following functions are available. Specify desired parameter values in the parenthesis, e.g. <em>generate.one.dataset(n=10000, error.telo=0.1, error.scg=0.1, error.cor=0</em>). Default values in the simulation functions are generally those given in table 1 of the main paper.</p> <ul> <li><em>generate.one.dataset()</em> returns a simple dataset (one telomere measurement per individual) for chosen values of all the variables described in section 1. As well as ideal and measured Cqs, it returns ideal and measured TS ratios. It also returns the difference between the ideal and measured TS ratio, calculated two ways, computed (<em>error.computed</em>), and using equation (11) of online supplement 1 (<em>error.analytic</em>). Both methods produce the same number. This was included as an additional check of correctness of the simulation.</li> <li><em>generate.repeated.measure()</em> returns a dataset where telomere lengths from the same individuals are measured twice, via two independent biological samples, and the true telomere length of each individual is assumed not to have changed at all. The data frame it returns is as for <em>generate.one.dataset()</em>, except that there are two of each variable (e.g. <em>true.ts.1, true.ts.2, measured.ts.1, measured.ts.2</em>, etc.).</li> <li><em>calculate.repeatability()</em> calculates the repeatability of the measured T/S ratio (intra-class correlation coefficient) when <em>generate.repeated.measure()</em> is implemented using the given values for all the parameters. It requires prior installation of R package ‘irr’.</li> <li><em>compare.repeatability()</em> returns the repeatability of the T/S ratio and the repeatability calculated on the raw Cq for the telomere reaction, for the given parameter values.</li> </ul> <p> </p>
Data archive for "Femtosecond X-ray diffraction reveals a liquid-liquid phase transition in phase-change materials"
<p>This archive contains the raw experimentat data used for the data analysis in the article "Femtosecond X-ray diffraction reveals a liquid-liquid phase transition in phase-change materials". It furthermore includes the script(s) to transform raw diffraction images into structure factors and the data shown in the figures in ascii format. For the ab-initio molecular dynamics simulations, the atomic trajectories are included as well.</p>
Radio emission from a pulsar's magnetic pole revealed by general relativity: a data archive
<p>This repository contains all pulsar data presented as part of the Science paper: "Radio emission from a pulsar's magnetic pole revealed by general relativity". These data consist of pulse profiles archives of PSR J1906+0746 observed by the Nancay and Arecibo radio telescopes between 2005 and 2018, that are readable by the PSRCHIVE package, see e.g. van Straten et al., Astronomical Research and Technology 9, 237 (2012).</p>
Data archive for 'Convolutional neural networks facilitate river barrier detection and evidence severe habitat fragmentation in the Mekong River biodiversity hotspot'
<p>This repository contains the code and databases used in the paper 'Convolutional neural networks facilitate river barrier detection and evidence severe habitat fragmentation in the Mekong River biodiversity hotspot'. </p> <p>The 'Mekong River Barrier Database (MRBD)' folder contains the basin-scale barrier database developed in this study. This database contains more than 13,000 unique barriers, which were identified by using the convolutional neural networks-based object detection method from Google Earth’s satellite imagery.</p> <p>The 'FCOS' folder contains the barrier detection model (FCOS ResNext-101-FPN), trained for detecting river barriers from remotely sensed images within the MMDetection framework. The 'FCOS_x101_v2' folder contains the enhanced FCOS model.</p> <p>The 'R_script' folder contains R files used in the paper. Coordinate.R was used to extract coordinates from bounding boxes in each TIF image. CAFI.R was used to calculate the CAFI index in each sub-catchment.</p> <p>The 'Barrier image training set' folder contains over 10,000 river barrier satellite images and their associated JSON files, forming the 'training, validation, and test datasets' used during the model training process. This dataset is made available to the user community in raw, in the hope that others will contribute to its future development, thereby enhancing its use and utility.</p> <p>For more information on the MMDetection framework, refer to the following GitHub repository: <a href="https://github.com/open-mmlab/mmdetection">https://github.com/open-mmlab/mmdetection</a></p>
Archive for regression and estimation of the total freeboard of the Arctic using AMSR2 data
<p>This fileset contains data used for generating Tables and Figures in Kim et al. (2021). All file has csv format separated by a comma. In the first line of the individual file, we provide the variable name and unit of each column.</p> <p> </p> <p>The fileset has 6 sub-directories. The name of each directory indicates the figures corresponding to its contents.</p> <p>• Figure 3 (and Table 1-3)</p> <p>• Figure 4</p> <p>• Figure 5, 8</p> <p>• Figure 5_ICESat2</p> <p>• Figure 6,7</p> <p>• Figure A1</p>
A simplified palaeoceanography archiving system (PARIS) and GUI for storage and visualisation of marine sediment core proxy data vs age and depth.
<p>Scientific discovery can be aided when data is shared following the principles of findability, accessibility, interoperability, reusability (FAIR) data (Wilkinson et al., 2016). Recent discussions in the palaeoclimate literature have focussed on defining the ideal database format for storing data and associated metadata. Here, we highlight an often overlooked primary process in widespread adoption of FAIR data, namely the systematic creation of machine readable data at source (i.e. at the field and laboratory level). We detail a file naming and structuring method that was used at LSCE to store data in text file format in a way that is machine-readable, and also human-friendly to persons of all levels of computer proficiency, thus encouraging the adoption of a machine-readable ethos at the very start of a project. Thanks to the relative simplicity of downcore palaeoclimate data, we demonstrate the power of this simple but powerful file format to function as a basic database in itself: we provide a Matlab-based GUI tool that allows users to search and visualise data by sediment core location, proxy type and species type. The adoption of similarily accessible, machine-readable file formats at other laboratories will promote data sharing within projects, while also allowing for the automation of submission of data to online database repositories with particular formatting and/or metadata requirements, thus reducing post-hoc workload.</p>
Code and data archive to accompany "A derivative-free optimisation method for global ocean biogeochemical models", Oliver et. al. 2021
<p>This archive is to accompany the article:</p> <p>A derivative-free optimisation method for global ocean biogeochemical models,<br> Sophy Oliver, Coralia Cartis, Iris Kriest, Simon Tett, and Samar Khatiwala.</p> <p>The optimisation framework used in this study can be found here: https://doi.org/10.5281/zenodo.5517610</p> <p>The original source code of MOPS were from the Supplement of Kriest et al. (2017).<br> The most recent TMM source code is available at https://github.com/samarkhatiwala/tmm.</p> <p>In this archive:</p> <p>Supplement/Configurations/OxfordMOPS_Configs contains:<br> - ReadOnlyFiles (Files and Code specifically used to run the global ocean biogeochemical model MOPS model with<br> the Transport Matrix Method, which have been edited to differ from the versions downloaded from the sources above.)<br> - RunCode (runscripts to run the MOPS model with the TMM)<br> - TWIN_Configs (JSON files required by each optimisation experiment carried out).</p> <p>Supplement/OxfordMOPS_EXP contains data for each iteration of all optimisation experiments carried out.</p> <p>Supplement/OPTCLIMSO_PlottingScripts contains MATLAB plotting scripts used to create results figures of these experiments.</p>
Discover the Data Archiving Guide (DAG) - a training event for new(ish) staff members [Workshop recording]
<p>The CESSDA Data Archiving Guide (https://dag.cessda.eu/) is a new resource developed by CESSDA and is designed to provide employees at data archives and repositories with an understanding of the work a data archive performs. The information in the DAG was collected by experts from CESSDA social science data archives reflecting the procedures and policies at their local archives. While the context of these archives varies — in size, the underlying technical architecture or in the specific services provided to researchers — the DAG focuses on common ground and is a useful tool for professionals new to data archiving or those who are knowledgeable in one domain and now seek to broaden their expertise.<br> <br> The full-day workshop was targeted mainly for new employees in data archives; people generally interested in the DAG were welcome as well.<br> <br> This workshop focused specifically on the Chapters Pre-Ingest, Ingest and FAIR with an additional excursion into the glossary to deepen participant's knowledge in a playful way.</p> <p>The video is also available on the <a href="https://www.youtube.com/watch?v=yzPzVK5UZKE">CESSDA Training YouTube Channel</a>.</p>
The National Archives Accessions to Repositories Data c.2007 - 2020
<p>The Annual Accessions to Repositories survey is a UK-wide exercise conducted by the National Archives that assesses what is being collected by UK repositories. The primary purpose of this exercise is to place some of this information onto TNA’s search engine Discovery. More recently, the data has been used to communicate accessions trends to the wider archives sector including information on what is being collected and where. Each year, TNA sends out survey templates in the form of Excel spreadsheets that are sent out to repositories in each part of the UK. The returns sent to TNA include information on the size of the record, the dates it covers, the creator of the record and a description of the record. Work has been undertaken since October 2021 to to merge and standardise the accessions data held by TNA. This data repository presents the merged dataset.</p>
ScienceDex guides
Understand access before you commit
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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