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174 results for “Dynamic Assessment”
Assessing the role of soil microbes in the dynamics of P release from poorly soluble P forms
<p>Dataset and script used for the publication <em>Assessing the role of soil microbes in the dynamics of P release from poorly soluble P forms, </em>doi (to be determined).</p>
Life cycle inventories for the article: Circular Battery Production in the EU: Insights from integrating Life Cycle Assessment into System Dynamics Modeling on Recycled Content and Environmental Impacts
<p>This repository provides the unregionalized life cycle inventories to the paper "<span>Ginster, R.</span>, <span>Blömeke, S.</span>, <span>Popien, J. L.</span>, <span>Scheller, C.</span>, <span>Cerdas, F.</span>, <span>Herrmann, C.</span>, & <span>Spengler, T. S.</span> (<span>2024</span>). <span>Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts</span>. <em>Journal of Industrial Ecology</em>, <span>1</span>–<span>18</span>. <a href="https://doi.org/10.1111/jiec.13527">https://doi.org/10.1111/jiec.13527</a>".</p> <h2>Contents</h2> <p>The repository is split into 2 parts and comprises the following files:</p> <p><strong>01_production: </strong>contains the necessary life cycle inventories for battery production.</p> <ul> <li><strong>01_primary</strong>: contains the life cycle inventories for battery production from primary materials.</li> <li><strong>02_secondary</strong>: contains the life cycle inventories for battery production from secondary materials.</li> <li><strong>03_active_material</strong>: contains the life cycle inventories for the active battery materials from primary materials.</li> <li><strong>04_active_material</strong>: contains the life cycle inventories for the active battery materials from secondary materials.</li> </ul> <p> </p> <p><strong>02_recycling: </strong>contains the necessary inventories for battery recycling.</p> <ul> <li><strong>01_process</strong>: contains the life cycle inventories for battery recycling.</li> <li><strong>02_intermediate</strong>: contains the life cycle inventories for the intermediate system for battery recycling.</li> <li><strong>03_output</strong>: contains the life cycle inventories for the resulting substances from battery recycling.</li> </ul> <h2>Summary</h2> <p>These files allow to reproduce the results of our study. Each file contains the life cycle inventory of one distinct battery capacity (20, 45, 68, 85, 95, 100 kWh) with a specific cell chemistry (LFP, NCA, NMC333, NMC532, NMC622, NMC811, NMC955) for battery production (based on Knehr et al. 2022) or for battery recycling (based on Blömeke et al. 2023).</p> <h2>Related publication</h2> <p>More details on the scientific context is provided in the publication itself:</p> <p><span>Ginster, R.</span>, <span>Blömeke, S.</span>, <span>Popien, J. L.</span>, <span>Scheller, C.</span>, <span>Cerdas, F.</span>, <span>Herrmann, C.</span>, & <span>Spengler, T. S.</span> (<span>2024</span>). <span>Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts</span>. <em>Journal of Industrial Ecology</em>, <span>1</span>–<span>18</span>. <a href="https://doi.org/10.1111/jiec.13527">https://doi.org/10.1111/jiec.13527</a></p> <h2>Funding</h2> <p>This publication (Raphael Ginster and Steffen Blömeke) was created within the Research Training Group CircularLIB, supported by the Ministry of Science and Culture of Lower Saxony with funds from the program zukunft.niedersachsen of the Volkswagen Foundation (MWK | ZN3678).</p> <p>The publication on which this dataset is based were funded by the German Federal Ministry of Education and Research within the Competence Cluster Recycling & Green Battery (greenBatt) under the grant numbers 03XP0302A (Christian Scheller) and 03XP0331A (Jan-Linus Popien). The authors are responsible for the contents of this publication.</p>
Assessment of mutation probabilities of KRAS G12 missense mutants and their long-time scale dynamics by atomistic molecular simulations and Markov state modeling: Datasets.
<p>Datasets related to the publication [1].<br> Including:</p> <ul> <li>KRAS G12X mutations derived from COSMIC v.79 [http://cancer.sanger.ac.uk/cosmic/] (KRAS_G12X_mut_COSMICv79..xlsx)</li> <li>RMSFs (300-2000ns) of GDP-systems (300_2000rmsf_GDP_systems_RAW_AVG_SE.xlsx)</li> <li>RMSFs (300-2000ns) of GTP-systems (300_2000RMSF_GTP_systems_RAW_AVG_SE.xlsx)</li> <li>PyInteraph analysis data for salt-bridges and hydrophobic clusters (.dat files for each system in the PyInteraph_data.zip-file)</li> <li>Backbone trajectories for each system (residues 4-164; frames for every 1ns). Last number (e.g. _1) refers to the replica of the simulated system.</li> <li>backbone_4-164.gro/.pdb/.tpr -files (resid 4-164) </li> </ul> <p><br> [1] Pantsar T et al. Assessment of mutation probabilities of KRAS G12 missense mutants and their long-time scale dynamics by atomistic molecular simulations and Markov state modeling. <em>PLoS Comput Biol Submitted</em> (2018)</p>
Individual work of Bachelor students monitored using a dynamic assessment dashboard
<p>This document explain how data were generated and how to interpret them. </p> <p><strong>LICENSE: CC0</strong><br> But if you want to combine data with other datasets, feel free to use them as if they were published under CC0 license. <br> Data were published in February 2017. At that time, Zenodo only provided CC BY, CC BY-SA, CC BY-NC, CC BY-ND and CC BY-NC-ND. No CC0 option was available.</p> <p> </p> <p><strong>HOW DATA WERE COLLECTED</strong><br> Data provided in this dataset were collected in Fall 2016 as part of the Bachelor course 'Formation des usagers en bibliothèques' taught at the University of applied sciences Geneva (Information Sciences Department). <br> Data were generated by the interaction of the students with DAD, the Dynamic assessment dashboard, software to be released in 2017 (https://github.com/grolimur/DAD). Only data related to individual activities are published in this set. They were then cleaned and anonymised.</p> <p>The activities.csv file is provided for information purpose.</p> <p>Data are provided in 2 formats:</p> <p>* Comma-spearated values (submissions.csv)<br> * SQLite (submissions.sqlite)</p> <p>CSV is not correctly read by Excel, it's recommended to convert it into an .xslx file before using it. <br> SQLite is provided in order to apply different sorting and filters to the data. It can be read using SQLite manager for Firefox (https://addons.mozilla.org/en-US/firefox/addon/sqlite-manager/).</p> <p> </p> <p><strong>CODEBOOK</strong><br> Here is the name, the meaning and the possible values of the columns (name - description [possible values]).</p> <p>ID - database row ID [(database unique row ID)]<br> ActivityID - unique activity ID [1, 2, 3, 4, 10, 14, 16, 21, 22, 23, 41]<br> StudentID - unique anonymous student ID [#50 to #72]<br> date - recorded by the database [timestamp]<br> year - year extracted from date [2016, 2017]<br> month - month extracted from date [01, 11, 12]<br> day - day extracted from date [1 to 31]<br> weekday - weekday calculated from date [1, 2, 3, 4, 5, 6, 7]<br> hour - hour extracted from date - 00 to 23]<br> minute - minute extracted from date [00 to 59]<br> second - second extracted from date [00 to 59]<br> gr - group activity or not [0]<br> validated - was the submission validated or not [-1 or 1]<br> week - week number [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, H1, H2]</p> <p><em>ActivityID meaning</em><br> Activities are described in activities.csv. This file conatins only individual activities description. <br> The 2 activities iwth the bonus particularity are not present in the data because students didn't need to apply for them. A bonus was automatically added when activity 1 AND 2 AND 3 AND 4 (bonus = activity 5) as well as when activities 21 AND 22 AND 23 (activity 24) were completed.</p> <p><em>Date format</em><br> The date is formatted like YYYY-MM-DD hh:mm:ss.</p> <p><em>Weekday meaning</em><br> 1 stands for Monday, 2 for Tuesday and so on until 7 for Sunday.</p> <p><em>Group meaning</em><br> 0 stands for 'individual activity' and 1 would stand for group activity'. <br> Only entries with gr="0" appears in this dataset.</p> <p><em>Validated meaning</em><br> -1 stands for 'rejected' and 1 for 'validated'.</p> <p><em>Week meaning</em><br> The course lasts 10 weeks. 1 stands for the 1st week and so on until 10 that stands for the 10th week. <br> H1 and H2 stand for the Christmas holiday weeks that took place between week 8 and 9. <br> No submission were sent in weeks 1, 2 and 10.</p> <p> </p>
Method to assess the functional role of noisy brain signals by mining envelope dynamics
<p>Preprocessed envelope EEG features based on a spatial filter approach. The features were computed across multiple within-trial SVIPT events for a large hyperparameter space on data of an exemplary subject.</p> <p>The file "components.bsv" contains the preprocessed envelope features of all investigated configurations and provides underlying parameters as well as a relative path for the key ``record_dir'' to additional component information. Specifically, for each configuration the spatial filter, spatial activity pattern and the time-resolved within-trial envelope signal is provided under "records/". </p>
14-day smartphone ambulatory assessment of depression symptoms and mood dynamics in a general population sample: comparison with the PHQ-9 depression screening
<p>This dataset contains 14 days of ambulatory assessment (AA) depression symptoms and mood ratings with timestamps, a retrospective Patient Health Questionnaire (PHQ-9) assessment and the demographic variables age and gender.</p> <p>The AA was conducted with users of the mobile mental health / depression screening app "Moodpath". ICD-10 depression symptoms were assessed with the following questions:</p> <p>ICD-10 symptom 1: "depressed mood"<br> q1 = Are you feeling depressed?<br> q2 = Are you feeling hopeless?</p> <p>ICD-10 symptom 2: "loss of interest and enjoyment"<br> q3 = Do you feel like you are not interested in anything right now?<br> q4 = Do you have less pleasure in doing things you usually enjoy?</p> <p>ICD-10 symptom 3: "increased fatigability"<br> q5 = Do you currently have considerably less energy? <br> q6 = Are your everyday tasks making you very tired currently?</p> <p>ICD-10 symptom 4: "reduced concentration and attention"<br> q11 = Is it hard for you to make decisions currently? <br> q12 = Is it hard for you to concentrate currently?</p> <p>ICD-10 symptom 5: "reduced self-esteem and self-confidence"<br> q7 = Is your self-confidence clearly lower than usual?<br> q8 = Are you feeling up to your tasks? </p> <p>ICD-10 symptom 6: "ideas of guilt and unworthiness"<br> q9 = Are you blaming yourself currently? <br> q10 = Do you think you are worth less than others right now?</p> <p>ICD-10 symptom 7: "bleak and pessimistic views of the future"<br> q46 = Are you thinking that you will be doing well in the future? <br> q47 = Are you looking hopefully into the future?</p> <p>ICD-10 symptom 8: "ideas or acts of self-harm or suicide"<br> q16 = Are you thinking about death more often than usual? </p> <p>ICD-10 symptom 9: "disturbed sleep"<br> q13 = Did you sleep badly last night? </p> <p>ICD-10 symptom 10: "diminished appetite"<br> q14 = Do you have less or no appetite today? </p>
Assessing Nonadiabatic Dynamics Methods in Long Timescales
<p>In this study, we employ the multiconfiguration time-dependent Hartree (MCTDH) and its multi-layer variants (ML-MCTDH), ab initio multiple spawning (AIMS), and fewest-switches surface hopping (FSSH) methodologies to simulate the excited-state dynamics of a weakly-coupled multi-dimensional (10 dimensional) Spin-Boson model Hamiltonian designed for a long timescale decay behavior. </p> <p>The pre-print of the article can be found at <a href="https://chemrxiv.org/engage/chemrxiv/article-details/66f68454cec5d6c142647b0a">https://chemrxiv.org/engage/chemrxiv/article-details/66f68454cec5d6c142647b0a</a></p> <p>MUKHERJEE S, Lassmann Y, Mattos RS, Demoulin B, Curchod BFE, Barbatti M. Assessing Nonadiabatic Dynamics Methods in Long Timescales. ChemRxiv. 2024; doi:10.26434/chemrxiv-2024-j7xxl </p> <p> </p> <p><strong>The dataset contains</strong></p> <ul> <li>MCTDH.tar.bz2 - 10 sets of text files for MCTDH simulations </li> <li>ML-MCTDH.tar.vz2 - 10 sets of text files for ML-MCTDH simulations</li> <li>DC-FSSH.tar.bz2 - HDF5 output files for 2000 independent decoherence-corrected FSSH trajectories and a Python script for Wigner Sampling initial conditions </li> <li>CSS-AIMS.tar.gz - output text files for 44 independent cannibalistic stochastic selection approach of AIMS trajectories</li> </ul>
Data and scripts belonging to "Time lags of nitrate, chloride, and tritium in streams assessed by dynamic groundwater flow tracking in a lowland landscape"
<p>Data and scripts belonging to Kaandorp et al., 2021 "Time lags of nitrate, chloride, and tritium in streams assessed by dynamic groundwater flow tracking in a lowland landscape". Hydrology and Earth System Sciences. </p>
Dynamic light scattering datasets used to assess the Raynals software
<p>Updated - 23/04/2023<br> This folder contains the associated data from Burastero et al., 'Raynals, an online tool for the analysis of dynamic light scattering'</p> <p>The experimental datasets can be found at ./experimentalData/<br> The artificially generated datasets at ./SimulatedDataDLS_case1 and ./SimulatedDataDLS_case2</p> <p>To produce the experimental datasets, we performed measurements on Carbonic Anhydrase, Bovine Serum Albumin, Gold Nanoparticles, and three in-house samples: A protein with a beta-Propeller domain, a coiled-coil like protein, and an intrinsically disordered protein.</p> <p>Additionally, you'll find R scripts to generate Fig. 3, Fig. 5, and Fig. S1 to S4.<br> </p>
Data and code: Assessing fish-fishery dynamics from a spatially explicit metapopulation perspective reveals winners and losers in fisheries management
<ol> <li><span>Sustainable management of living resources must reconcile biodiversity conservation and socioeconomic viability of human activities. In the case of fisheries, sustainable management design is made challenging by the complex spatiotemporal interactions between fish and fisheries.</span></li> <li><span>We develop a comprehensive metapopulation framework integrating data on species life-history traits, connectivity and habitat distribution to identify priority areas for fishing regulation and assess how management impacts are spatially distributed. We trial this approach on European hake fisheries in the north-western Mediterranean, where we assess area-based management scenarios in terms of stock status and fishery productivity to prioritize areas for protection. </span></li> <li><span>Model simulations show that local fishery closures have the potential to enhance both spawning stock biomass and landings on a regional scale compared to a status quo scenario, but that improving protection is easier than increasing productivity. Moreover, the interaction between metapopulation dynamics and the redistribution of fishing effort following local closures implies that benefits and drawbacks are heterogeneously distributed in space, the former being concentrated in the proximity of the protected site. </span></li> <li><span>A network analysis shows that priority areas for protection are those with the highest connectivity (as expressed by network metrics) if the objective is to improve the spawning stock, while no significant relationship emerges between connectivity and potential for increased landings.</span></li> <li> <span><em>Synthesis and applications</em> – </span><span>Our framework provides a tool for 1) assessing area-based management measures aimed at improving fisheries outcomes in terms of both conservation and socioeconomic viability and 2) describing the spatial distribution of costs and benefits, which can help guide effective management and gain stakeholder support. Adult dispersal remains the main source of uncertainty that needs to be investigated to effectively apply our model to fisheries regulation.</span> </li> </ol>
A Global Study to Assess the Drug Dynamics, Efficacy, and Safety of Venglustat (GZ/SAR402671) in Parkinson's Disease Patients Carrying a Glucocerebrosidase (GBA) Gene Mutation
ClinicalTrials.gov study NCT02906020. IPD Sharing: YES. Countries: 16. Publications: 3.
Data and code: Assessing fish-fishery dynamics from a spatially explicit metapopulation perspective reveals winners and losers in fisheries management
Open the record for dataset details and reuse information.
Raw data : Population Dynamics and Yield Loss Assessment for Pea Aphid (Homoptera: Aphididae) on Lentil in Morocco
<p>Raw data to describes the population fluctuation of pea aphid over different seasons and their effects on yield loss in Morocco.</p> <p> </p>
SI Movies for "Dynamics of dominance: maneuvers, contests, and assessment in the posture-scale movements of interacting zebrafish"
<p>This is a repository of the Supplementary Movies from the paper "Dynamics of dominance: maneuvers, contests, and assessment in the posture-scale movements of interacting zebrafish".</p><h3>SM1</h3><p><br>This movie is a visualization of the tracking pipeline output, namely the 3D bodypoint positions of the two zebrafish over time. In this animation, we show the 3D locations of the head (circle), pec (square) and tail (cross) bodypoints of the winner (red) and loser (blue) throughout an entire experiment, the same experiment shown in Fig3(A) of the paper. The movie is downsampled from the 100fps recordings to 25fps to reduce file size. (1hr 23min 29sec)</p><h3>Notes on SM2-6</h3><p>In these movies we show two panels. The left panel shows an animation of the contestant 3D bodypoint positions. The right panel shows the XZ camera view of the tank with the XZ image coordinates of bodypoints drawn over the fish. The movies run at 100fps and are all 2 minutes long. The movies are each centered at a different time during the FishTank20200130_153857 recording, with one minute before, and one minute after, the chosen centered point.</p><h3>SM2</h3><p>This video is centred at the frame identified as the start of the first fight bout.</p><h3>SM3</h3><p>This video is centered at the frame identified as the middle of the first fight bout.</p><h3>SM4</h3><p>This video is centered at the frame identified as the end of the first fight bout.</p><h3>SM5</h3><p>This video is centered at the frame identified as the start of the second fight bout.</p><h3>SM6</h3><p>This video is centered at the frame identified as the end of the second fight bout.</p>
Data from: Temporal dynamics in psychological assessments: a novel dataset with scales and response times
<p>This dataset is collected from February 27 to March 17, 2021, it includes responses from 24,292 students to four recognized psychological scales: PHQ-9, GAD-7, ISI, and PSS. A unique aspect of this dataset is the inclusion of response time data, which reflects the duration students took to answer each question. This temporal information offers a new dimension in understanding respondent behavior and enhances the reliability of these scales.</p>
Data and code from: Accounting for unobserved population dynamics and aging error in close-kin mark-recapture assessments
<p>Obtaining robust estimates of population abundance is a central challenge hindering the conservation and management of many threatened and exploited species. Close-kin mark-recapture (CKMR) is a genetics-based approach that has strong potential to improve monitoring of data-limited species by enabling estimates of abundance, survival, and other parameters for populations that are challenging to assess. However, CKMR models have received limited sensitivity testing under realistic population dynamics and sampling scenarios, impeding application of the method in population monitoring programs and stock assessments. Here, we use individual-based simulation to examine how unmodeled population dynamics and aging uncertainty affect the accuracy and precision of CKMR parameter estimates under different sampling strategies. We then present adapted models that correct the biases that arise from model misspecification. Our results demonstrate that a simple base-case CKMR model produces robust estimates of population abundance with stable populations that breed annually; however, if a population trend or non-annual breeding dynamics are present, or if year-specific estimates of abundance are desired, a more complex CKMR model must be constructed. In addition, we show that CKMR can generate reliable abundance estimates for adults from a variety of sampling strategies, including juvenile-focused sampling where adults are never directly observed (and aging error is minimal). Finally, we apply a CKMR model that has been adapted for population growth and intermittent breeding to two decades of genetic data from juvenile lemon sharks (<em>Negaprion brevirostris</em>) in Bimini, Bahamas, to demonstrate how application of CKMR to samples drawn solely from juveniles can contribute to monitoring efforts for highly mobile populations. Overall, this study expands our understanding of the biological factors and sampling decisions that cause bias in CKMR models, identifies key areas for future inquiry, and provides recommendations that can aid biologists in planning and implementing an effective CKMR study, particularly for long-lived data-limited species.</p>
Molecular Dynamics Assessment of Fusion Relevant Elements Figures
<p>Figures for "Molecular Dynamics Assessment of Primary Damage Efficiency for Fusion Relevant Elements"</p>
Data from high throughput SNP-chip as cost effective new monitoring tool for assessing invasion dynamics in the comb jelly Mnemiopsis leidyi
<p class="MsoNormal"><span>High throughput low-density SNP arrays provide a cost-effective solution for population genetic studies and monitoring of genetic diversity as well as population structure commonly implemented in real time stock assessment of fish species. However, the application of high throughput SNP arrays for monitoring of invasive species has so far not been implemented. We developed a species-specific SNP array for the invasive comb jelly <em>Mnemiopsis leidyi</em> based on whole genome resequencing data. Initially, </span><span>a total of</span><span> </span><span>1,</span><span>395</span><span> </span><span>high quality </span><span>SNPs</span><span> were identified</span><span> </span><span>u</span><span>sing stri</span><span>ngent</span><span> filtering criteria</span><span>. From those, 192 assays were designed and validated, resulting in the final panel of 116 SNPs. Markers were diagnostic between the northern and southern <em>M. leidyi</em> lineages and highly polymorphic to distinguish populations. Despite using a reduced representation of the genome, our SNP panel yielded comparable results to using a whole genome resequencing approach (832,323 SNPs), recovering similar values of genetic differentiation between samples and detecting the same clustering groups when performing Structure analyses. The resource presented here provides a cost-effective, high throughput solution for population genetic studies, allowing to routinely genotype large number of individuals. Monitoring of genetic diversity and effective population size estimations in this highly invasive species will allow for the early detection of new introductions from distant source regions or hybridization events. Thereby, this SNP chip represents an important management tool in order to understand invasion dynamics and </span><span>opens the door for implementing such methods for a wider range of alien invasive species.</span></p> <div></div>
An Assessment of Nonhydrostatic and Hydrostatic Dynamical Cores at Seasonal Time Scales in the Energy Exascale Earth System Model (E3SMv1)
<p>This is the companion data for the manuscript of the same title, submitted to Journal of Advances in Modeling Earth Systems on 09/03/2021. </p> <p><strong>summer: </strong>this folder contains part of the model outputs I ran on NERSC Cori in 2020-2021 corresponding to the summer simulations in the manuscript.</p> <p><strong>winter: </strong>this folder contains part of the outputs I ran on NERSC Cori in 2020-2021 corresponding to the winter simulations in the manuscript.</p> <p><strong>ne256: </strong>this folder contains part of the outputs I ran on NERSC Cori in 2021 corresponding to the ne256 simulations in the manuscript.</p> <p><strong>bubble</strong>: this folder includes the namelists used in the rising bubble experiments.</p> <p><strong>script: </strong>this folder includes an example of a script to generate the realistic SCREAM simulation </p> <p> </p> <p>Unfortunately, model outputs are too large (~ 30 TB). Therefore, I only provide mean data, used directly to generate figures, in this repository. All model output are archived on tape at NERSC. </p> <p>For more details, refer to the manuscript, or contact me (wrliu@ucdavis.edu). </p>
Spatio-temporal dynamics of attacks around deaths of wolves: A statistical assessment of lethal control efficiency in France
<h1>Description</h1> <p>This repository contains the supplementary materials (<em>Supplementary_information.pdf</em>, <em>Supplementary_figures.pdf</em>, <em>Supplementary_tables.pdf</em>) of the manuscript: "<strong>Spatio-temporal dynamics of attacks around deaths of wolves: A statistical assessment of lethal control efficiency in France</strong>". This repository also provides the R codes and datasets necessary to run the analyses described in the manuscript. </p> <h1>Datasets</h1> <p>We provide the spatially anonymized R datasets to respect confidentiality. Therefore, the preliminary preparation of the data is not provided in the public codes. These datasets, all geolocated and necessary to the analyses, are:</p> <ul> <li><em>Attack_sf.RData</em>: 19,302 analyzed wolf attacks on sheep <br> - ID: unique ID of the attack <br> - DATE: date of the attack <br> - PASTURE: the related pasture ID from "Pasture_sf" where the attack is located <br> - STATUS: column resulting from the preparation and the attribution of attacks to pastures (part 2.2.4 of the manuscript); not shown here to respect confidentiality </li> <li><em>Pasture_sf.RData</em>: 4987 analyzed pastures grazed by sheep <br> - ID: unique ID of the pasture <br> - CODE: Official code in the pastoral census <br> - FLOCK_SIZE: maximum annual number of sheep grazing in the pasture <br> - USED_MONTHS: months for which the pasture is grazed by sheep </li> <li><em>Removal_sf.RData</em>: 232 analyzed single or multiple wolf removals <br> - ID: unique ID of the removal <br> - OVERLAP: are they single removal ("single" in the manuscript => "NO" here), or not ("multiple" in the manuscrit, here "SIMULTANEOUS" for removals occurring during the exact same operation or "NON-SIMULTANEOUS" if not). <br> - DATE_MIN: date of the single removal or date of the first removal of a group <br> - DATE_MAX: date of the single removal or date of the last removal of a group <br> - CLASS: administrative type of the removal according to definitions from 2.1 part of the manuscript <br> - SEX: sex of the removed wolves if known <br> - AGE: class age of the removed wolves if known <br> - BREEDER: breeding status of the removed female wolves, "Yes" for female breeder, "No" for female non-breeder. Males are "No" by default, when necropsied; dead individuals with NA were not found. <br> - SEASON: season of the removal, as defined in part 2.3.4 of the manuscript <br> - MASSIF: mountain range attributed to the removal, as defined in part 2.3.4 of the manuscript </li> <li><em>Mountain_range_buff_sf.RData</em>: one row for each mountain range, corresponding to the buffered mountain ranges where removal control events could be sampled, as defined in part 2.3.3 of the manuscript </li> <li><em>Area_to_exclude_sf.RData</em>: one row for each mountain range, corresponding to the area too close from other mountain ranges, terrestrial and maritime limits, where removal control events could not be sampled, as defined in part 2.3.3 of the manuscript </li> <li><em>Overlapping_removal_sf.RData</em> corresponds to the spatial dataset necessary to run the supplementary figures about overlapping removals (S9) </li> </ul> <p>You can also find the object called <em>Subset_lt.RData</em>, which gives the ID of removals for each dataset (single/multiple removals) or subsets of single removals. </p> <p>The other RData resulted from the analysis. How to read their names: </p> <ul> <li>First part: <br> - <em>Buffer</em>: they link the attacks to removals or of control events. <br> - <em>Kernel</em>: they give the results of the kernel density estimation (z), according to the spatial (y) and temporal (x) coordinates, for each dataset.<br> - <em>Int</em>: they give, for each distance and time, the total amount of attack intensities before (INT_BEF) and after (INT_AFT) the combined locations and days of removals or control events. 1 unit of distance = 50 meters, 1 unit of time = 1 day.<br> - <em>Trend</em>: they give, for each distance and time, the trends in % of the attack intensities when comparing before and after, with their uncertainties and significance. Files with <em>spatshift</em> are designed for figures about spatial shift (unnested scales), contrary to files without <em>spatshift</em> (nested scales). </li> <li>Middle parts: (noted with an X after) <br> - <em>obs</em>: results from removals <br> - <em>jack</em>: results from jackknife samples of the removals <br> - <em>ctl</em>: results related to control sets <br> - <em>cor</em>: results corrected for livestock presence<br> - <em>raw</em>: results uncorrected for livestock presence<br> - <em>sim</em>: results for simulated attacks according to livestock presence </li> </ul> <p>Lengths of lists or sublists correspond to: </p> <p>- 20 elements: the two main datasets (single/multiple) and the 18 subsets of single removals. <br>- 100 elements: the 100 control sets. <br>- 1000 elements: the 1000 simulations of attacks (for the livestock presence correction). <br>- Other lengths and <em>jack</em> within the name: the jackknife samples. </p> <h1>Structure of the repository</h1> <h2><strong>Code 1</strong>: file <em>Buffer.R</em> </h2> <p>We keep only removals within geographic zones for the analysis (<em>Removal_analyzed_sf</em>), and sample their control events (100 simulations = control sets, <em>Removal_ctl_sf_lt</em>).</p> <p>We start by delimiting the spatio-temporal buffer for each row of the removal and control datasets. <br> - We identify the attacks from <em>Attack_sf.RData</em> within each buffer, thanks to the function <em>Buffer_fn</em>, giving the data frames <em>Buffer_X_df</em> (one row per attack) <br> - We select the pastures from <em>Pasture_sf.RData</em> within each buffer, thanks to the function <em>Buffer_pasture_fn</em>, giving the data frames <em>Buffer_X_sf</em> (one row per removal or control event) </p> <p>We calculate the spatial correction: <br> - We spatially slice each buffer into 200 rings with the function <em>Ring_fn</em>, giving the data frame <em>Ring_sf</em> (one row per ring) <br> - We add the total pastoral area of the ring of the attack ("SPATIAL_WEIGHT") with the function <em>Spatial_correction_fn</em>, for each attack of each buffer, within <em>Buffer_X_df</em> </p> <p>We calculate the pastoral correction: <br> - We create the pastoral matrix for each removal or control event with the function <em>Pastoral_matrix_fn</em>, giving a matrix of 200 rows (one for each ring) and 180 columns (one for each day, 90 days before the removal date and 90 day after the removal date), with the total pastoral area in use by sheep for each corresponding cell of the matrix (one element per removal, <em>Pastoral_X_mx_lt.RData</em>) <br> - We simulate, for each removal or control event, the random distribution of the attacks from <em>Buffer_X_df.RData</em> according to <em>Pastoral_X_mx_lt.RData</em> with the function <em>Buffer_sim_fn</em>. The process is done 1000 times (one element per simulation, <em>Buffer_X_sim_lt.RData</em>). </p> <h2>Code 2: file <em>Kernel.R</em> where we estimate the attack intensities </h2> <p>We classified the removals into 2 main datasets and 18 subsets, according to part 2.3.4 of the manuscript (<em>Subset_lt.RData</em>) (one element per set).<br>We compute the jackknife samples for each dataset or subset (<em>Removal_id_jack_lt</em>). <br>We perform the kernel estimations with the function <em>Kernel_fn</em> (<em>Kernel_X_lt</em>). <br>We sum the intensities of attacks before and after the removals or control events, with the function <em>Intensity_fn</em>, giving <em>Int_X_df_lt.RData</em>. </p> <h2>Code 3: file <em>Trend.R</em> where we calculate the trends of attack intensities after removals </h2> <p> We focus on the nested trends first: <br> - We calculate them (<em>Trend_X_df</em>) with function <em>Trend_fn </em><br> - We set the result significance test by observing the overlapping of confidence intervals of removals and control sets (<em>Trend_X_comparison_df</em>) </p> <p>We focus on the spatial shifts (trends for each specific distance):<br> - We calculate them (<em>Trend_X_spatshift_df</em>) with function <em>Trend_fn</em> <br> - We set the result significance test by observing the overlapping of confidence intervals of removals and control sets (<em>Trend_X_comparison_spatshift_df</em>) </p> <h2>- Code 4: file <em>Functions.R</em> where can be found all custom functions called in the other analysis codes </h2> <h2>- Code 5: file <em>Figures.R</em> produces part of the figures from the manuscript</h2> <p>Detailed comments are included in each code.</p> <h1>Support</h1> <p>If you have any question or request, do not hesitate to contact us at: oksana.grente@gmail.com</p> <h1>Authors and acknowledgment</h1> <p>Grente Oksana (CEFE, CNRS), Opitz Thomas (INRAE), Duchamp Christophe (OFB), Drouet-Hoguet Nolwenn (OFB), Chamaillé-Jammes Simon (CEFE, CNRS) and Gimenez Olivier (CEFE, CNRS). </p> <h1>License</h1> <p>GNU GENERAL PUBLIC LICENSE 3.0</p> <p> </p>
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Allen Brain Atlas
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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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