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opencc-by-4.0Aug 2022View details →

Population dynamics of small mammals in the Jura massif, Franche-Comté, France (1979-2000)

Small mammal populations were monitored seasonally from August 1979 to July 2000 according to a stratified sampling plan and using standard trapping, mostly in the area of Septfontaines – Le Souillot, Doubs, France (6.18°E, 46.97°N). The dataset includes 2120 trap lines (90% n = 1912 in the Septfontaines – Le Souillot (LS) area), and 22848 captures (92% n = 20937 in the LS area). Methods Small mammals were captured using INRA trap lines. INRA live traps (15 × 5 × 5 cm) are suitable for species of body mass less than 50 g. In a standard way generally applied here,each trap line consisted of 34 live traps spaced 3 meters apart. Trap lines were set up for three nights and checked every morning. Animals were euthanized by cervical dislocation, weighed and dissected for sex, reproductive status and age determination. Liver was examined macroscopically for parasites. Relative age was estimated based on the  dry weight of crystalline eye lenses. Caveats: in a very little number of cases (beginning of the study and circumstantial occasions) trap lines were not standard (150 m length, 51 traps, or set up for one or two nights only, etc.). See <a href="https://zenodo.org/record/6997317/files/Codes_Trapline.docx?download=1">Codes_Trapline.docx</a> and fields "remarques" (table <a href="http://zenodo.org/record/6997317/files/traplines.txt?download=1">traplines.txt</a>) and 'observation' (table <a href="http://zenodo.org/record/6997317/files/captures.txt?download=1">captures.txt</a>). Sometimes field not informed have been coded "", 99 or NA. Although we did our best to harmonize this in the files uploaded, some inconsistencies might remain. Those issues should be considered carefully before data analysis. Trapping and animal handling was carried out in full accordance with the relevant European guidelines (Directive 86/609/EEC) and national regulations. INRA (Institut National de la Recherche Agronomique), the umbrella organization under which the field work was carried out, created its first ethical committee in 1998. It was therefore impossible to get formal ethical approval prior to the major part of the study. A similar research protocol used from 2014 to 2017 received full approval from the Comité d’Éthique Bisontin en Expérimentation Animale (CEBEA No. 58). FILE DESCRIPTION Files <a href="https://zenodo.org/record/6997317/files/traplines.txt?download=1">traplines.txt</a>, <a href="https://zenodo.org/record/6997317/files/Ccaptures.txt?download=1">captures.txt</a> and <a href="https://zenodo.org/record/6997317/files/rates.txt?download=1">rates.txt</a> are tables of a relational database. They can be linked using the index field 'codeligne' between <a href="https://zenodo.org/record/6997317/files/traplines.txt?download=1">traplines.txt</a> and <a href="https://zenodo.org/record/6997317/files/captures.txt?download=1">captures.txt</a>, and 'codeind' between <a href="https://zenodo.org/record/6997317/files/captures.txt?download=1">captures.txt</a> and <a href="https://zenodo.org/record/6997317/files/rates.txt?download=1">rates.txt</a>. Main files <a href="https://zenodo.org/record/6997317/files/captures.txt?download=1">captures.txt</a> <ul> <li>codeligne, trapline ID</li> <li>numind, specimen ID for the trap line</li> <li>numcontr, control number (traps were controlled every morning; for 3 successive nights for standard trap lines). E.g. 1 for a specimen captured on the 1st control.</li> <li>numpiege, trap ID in the trap line (1,2,....n). E.g. 34 is the 34th trap in the trap line counted from the beginning.</li> <li>espece, species (see <a href="http://zenodo.org/record/6997317/files/Codes_Sp_Parasites.docx?download=1">Codes_Sp_Parasites.docx</a> for the codes)</li> <li>poids, wet weight in g</li> <li>sexe, sex (1 male, 2 female)</li> <li>cristallin, dry weight of the two crystalline lens, in 1/10 of mg</li> <li>uterus, uterus diameter</li> <li>foetusd, number of foetuses in the uterus right horn</li> <li>foetusg, number of foetuses in the uterus left horn</li> <li>cicplacd, number of placental scares in the uterus right horn</li> <li>cicplacg, number of placental scares in the uterus left horn</li> <li>corpsjd, number of corpus luteum in the right ovary</li> <li>corpsjg, number of corpus luteum in the left ovary</li> <li>allaitante, milking (1 yes, 0 no)</li> <li>corpsblancd, number of corpus albicans in the right ovary</li> <li>corpsblancg, number of corpus albicans in the left ovary</li> <li>testd, length of the right testicle</li> <li>testg, length of the left testicle</li> <li>vesd, length of the right seminal vesicle</li> <li>vesg, length of the left seminal vesicle</li> <li>diammax, parasite mass great diameter</li> <li>diammin, parasite mass small diameter</li> <li>nombrekyste, parasite cyst number</li> <li>nomlu, parasite species as identified in the field</li> <li>nomanaly, parasite species as identified in the lab</li> <li>observation, remark</li> <li>codeind, specimen ID (codeligne+numind)</li> </ul> <a href="https://zenodo.org/record/6997317/files/rates.txt?download=1">rates.txt </a> <ul> <li>codeind, specimen ID</li> <li>ratepds, spleen weight (1/100 g)</li> <li>remarque, remark</li> </ul> <a href="https://zenodo.org/record/6997317/files/traplines.txt?download=1">traplines.txt</a> <ul> <li>codeligne, 8 digits trap line ID. LS891001 = location LS, 89 year, 10 month, 01, trap line ID for this place, year and month. See <a href="https://zenodo.org/record/6997317/files/Codes_Trapline.docx?download=1">Codes_Trapline.docx</a> for more details.</li> <li>dept, administrative department (INSEE code)</li> <li>date, date at which the trapline has been set up</li> <li>descripteur1, habitat description, <a href="https://zenodo.org/record/6997317/files/Codes_Trapline.docx?download=1">Codes_Trapline.docx</a></li> <li>descripteur2, habitat description, see <a href="https://zenodo.org/record/6997317/files/Codes_Trapline.docx?download=1">Codes_Trapline.docx</a></li> <li>facies, habitat description, see <a href="https://zenodo.org/record/6997317/files/Codes_Trapline.docx?download=1">Codes_Trapline.docx</a></li> <li>remarque, remark,</li> <li>lati, latitude of the northwest corner of the sampling grid (CRS NTF (Paris) / Lambert zone II, EPSG: 27572), see <a href="https://zenodo.org/record/6997317/files/Codes_Trapline.docx?download=1">Codes_Trapline.docx</a></li> <li>longi, longitude of the northwest corner of the sampling grid (CRS NTF (Paris) / Lambert zone II, EPSG: 27572), see <a href="https://zenodo.org/record/6997317/files/Codes_Trapline.docx?download=1">Codes_Trapline.docx</a></li> <li>precis, precision, the number of Lambert II squares (1km x 1km) composing the square side of the grid, see <a href="https://zenodo.org/record/6997317/files/Codes_Trapline.docx?download=1">Codes_Trapline.docx</a></li> </ul> Supplementary files <a href="https://zenodo.org/record/6997317/files/bboxlarge.kml?download=1">bboxlarge.kml</a>, the bounding box including all the area with trap lines that could be geographically located. It takes the precision of the location into account (hence, includes the southeastern extremes of the grid squares (see Codes_Trapline.docx). <a href="https://zenodo.org/record/6997317/files/bboxLS.kml?download=1">bboxLS.kml</a>, the bounding box of the study area "LS" (Le Souillot) including all the trap lines that could be geographically located. It takes the precision of the location into account (hence, includes the southeastern extremes of the grid squares, see <a href="https://zenodo.org/record/6997317/files/Codes_trapline.docx?download=1">Codes_Trapline.docx</a>). Those trap lines are the core (90% of the total number of the traplines set) of the research carried out in the area. <a href="https://zenodo.org/record/6997317/files/lineloc.kml?download=1">lineloc.kml</a>, the geographical coordinates of the northwestern corner of the square including each trap line with trapline ID and  precision (1, square of 1 x 1 km; 2, square of 2 x 2 km, etc.). <a href="https://zenodo.org/record/6997317/files/Code_Sp_Parasites.docx?download=1">Codes_Sp_Parasites.docx</a>, codes of small mammal species and parasite names. <a href="https://zenodo.org/record/6997317/files/Codes_Trapline.docx?download=1">Codes_Trapline.docx</a>, codes of trap lines.

A Non-parametric Discrete Fracture Network Model

Database used to build discrete fracture networks through a non-parametric approach from Gómez et al. 2023 (DOI: 10.1007/s00603-022-03194-y). The data is structured in twelve.csv files, each with an array of size n-by-3, containing the orientation of the discontinuity (dip direction and dip of the pole) and its pseudo-trace length in meters, with n being the number of fractures in each file.

Shapefiles of administrative boundaries, Subway and main rivers in Glasgow, UK, around 1910

This collection consists of ESRI shapefiles for Glasgow around 1910: <ul> <li>sanitary district boundaries in 1903 (Sanitary_Districts.shp, etc.)</li> <li>municipal ward boundaries in 1912 (Wards_1912.shp, etc.)</li> <li>registration district boundaries within the area of the City of Glasgow in 1913 (Registration_Districts.shp, etc.)</li> <li>routes of main rivers (River Clyde and River Kelvin) around 1915 (Rivers.shp, etc.)</li> <li>route of the Glasgow Subway around 1915 (Subway.shp, etc.)</li> </ul> For details of shapefile construction, please see the descriptions in the following article: Angelopoulos, K., Stewart, G. and Mancy, R. Local infectious disease experience influences vaccine refusal rates: a natural experiment. Proceedings of the Royal Society B: Biological Sciences. DOI: 10.1098/rspb.2022.1986. Details of construction and references to original map sources are provided in the second paragraph of the section "Geographic conversion" in the online supplementary materials of the above reference. Further information about the boundaries is provided in the caption of Figure S1 of the supplementary materials. Additional contextual information is provided in both the main text and supplementary materials.

Bacteremia

The data set consists of 14,691 observations from different patients with the clinical suspicion to suffer from bacteremia, for whom a a blood culture analysis was performed at the Vienna General Hospital, Austria, between January 2006 and December 2010. It contains the results of the blood culture analysis for bacteremia and the values of 51 potential predictors of bacteremia. To protect data privacy our version of this data was slightly modified compared to the original version, and this modified version was cleared by the Medical University of Vienna for public use (DC 2019-0054).  Details on the meaning of the variables can be found in the data dictionary. The original version of the data set was used by Ratzinger et al (2014) to develop a model for screening bacteremic patients based on highly standardizable laboratory variables. This public version has been used by Gregorich et al (2021).

opencc-by-4.0Dec 2020View details →

Sub-National COVID-19 Incidence and Determinants Dataset

The Sub-National COVID-19 Incidence and Determinants Dataset contains rich sub-national data on COVID-19 cases and deaths combined with data on factors associated with the spread and severity of COVID-19 outbreaks in 2020. The data covers 503 sub-national areas (NUTS-2 level and equivalents) of 46 countries in five continents (Europe, Asia, North America, South America and Oceania). Indicators were mostly gathered weekly, with the exception of some variables that are monthly and yearly. The dataset was compiled to study the determinants of COVID-19 outbreaks with a focus on the effects of international airline travel. However, the data are useful to investigate also other questions on the sub-national diffusion of COVID-19. The information used to build this dataset was drawn from a variety of sources in order to cover four major areas of interest: health outcomes of the pandemic (COVID-19 cases and deaths), international air travel (number of incoming air passengers, centrality of local airports in the global airline network and air travel limitation policies), population mixing and government policy responses, and pre-pandemic area characteristics (socioeconomic, demographic, public health and co-morbidity). A complete list of sources can be found in the “Data sources” Pdf document attached. Please cite as: Recchi, E., A. Ferrara, A. Rodríguez Sánchez, E. Deutschmann, L. Gabrielli, S. Iacus, L. Bastiani, S. Spyratos & M. Vespe. 2022. The Impact of Air Travel on the Precocity and Severity of Covid-19 Deaths in Sub-National Areas across 45 Countries. Scientific Reports 12: 16522. https://doi.org/10.1038/s41598-022-20263-y

Syrian Migration to Europe, 2011-21: Data Inventory

This inventory includes metadata on various quantitative and qualitative sources of information on Syrian migration to Europe in 2011-21 that can be used for agent-based modelling purposes, with each source accompanied by data quality assessment. The files are available in a TSV and MS Excel format. The judgement-based quality ratings provided are specific to the requirements of agent-based modelling, as detailed in the <a href="https://www.baps-project.eu/inventory/project_outputs/data_sources/Background%20paper%20Data%20and%20knowledge.pdf">background paper.</a> A queryable version of the inventory is available on the website of the project Bayesian Agent-Based Population Studies (BAPS), funded by the European Research Council (725232): <a href="https://baps-project.eu/inventory/data_inventory">https://baps-project.eu/inventory/data_inventory</a>. The methodology behind assembling this dataset and assessing the individual data sources according to pre-defined quality criteria is detailed in: Nurse S and Bijak J (2022) Building a Knowledge Base for the Model. In: J Bijak et al., Towards Bayesian Model-Based Demography. Agency, Complexity and Uncertainty in Migration Studies. Methodos Series, vol 17. Springer, Cham. <a href="https://doi.org/10.1007/978-3-030-83039-7_4">https://doi.org/10.1007/978-3-030-83039-7_4</a>

Reference data and documentation for Skills4EOSC Deliverable D6.1 Mapping of existing professional networks

This record presents the data underlying Skills4EOSC Deliverable D6.1 Mapping of existing professional networks and relevant documentation of the search string.

Predicted occurrence probability for ticks in Great Britain (2014 to 2021) at 1 km spatial resolution

The dataset contains predictions of occurrence probability for ticks in Great Britain (2014 to 2021) at 1 km spatial resolution + all covariate layers used for modeling. Over seven million electronic health records (EHRs), among which 11,741 EHRs reported tick attachment, were used to evaluate climate, environmental and animal host factors affecting the risk of tick attachment in cats and dogs in Great Britain (GB). The tick presence/absence EHRs for dogs and cats were further overlaid with spatiotemporal time-series of climatic, vegetation, human influence, hydrological and terrain variables (slope, wetness index) to produce a spatiotemporal regression matrix; an Ensemble Machine Learning framework was used to fine-tune hyperparameters for Random Forest (classif.ranger), Gradient boosting (classif.xgboost) and GLM-net (classif.glmnet) algorithms, which were then used to produce a final ensemble meta-learner that predicts the probability of occurrence of ticks across GB with monthly intervals. <ul> <li>gb1km_covariates.zip contains ALL covariate layers as GeoTIFFs (time-series) used for modeling ticks dynamics;</li> <li>data_1km_2014_M01.rds = contains all covariates for January 2014 prepared as SpatialGridDataFrame (R data object);</li> </ul> Codes of files indicate e.g.: <ul> <li>"monthly.tick.prob_savsnet.mar_p_1km_s_2014_2021" = monthly occurrence probability for January based on the training data from 2014 to 2021;</li> <li>"monthly.tick.prob_savsnet.oct_md_1km_s_20211001_20211031" = monthly prediction (model) error derived as the standard deviation from multiple base learners;</li> </ul> The dataset is described in detail in the following publication: <ul> <li>Arsevska, E., Hengl, T., Singelton, D. et al. (2023?) Risk factors for tick attachment in companion animals in Great Britain: a spatiotemporal analysis covering 2014–2021. Submitted to Parasites & Vectors (in review).</li> </ul> The model summary shows: <pre><code>Call: stats::glm(formula = f, family = "binomial", data = getTaskData(.task, .subset), weights = .weights, model = FALSE) Deviance Residuals: Min 1Q Median 3Q Max -1.4749 -0.0557 -0.0471 -0.0430 3.7611 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -7.64495 0.02095 -364.957 < 2e-16 *** classif.ranger 4.95061 0.63615 7.782 7.13e-15 *** classif.xgboost 189.75543 5.53109 34.307 < 2e-16 *** classif.glmnet 140.24208 5.05375 27.750 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 170604 on 7303013 degrees of freedom Residual deviance: 162571 on 7303010 degrees of freedom AIC: 162579 Number of Fisher Scoring iterations: 9</code></pre> Acknowledgements: We are grateful to data providers in veterinary practice (VetSolutions, Teleos, CVS, and other practitioners). We are grateful to the INRAE MIGALE bioinformatics facility (MIGALE, INRAE, 2020. Migale Bioinformatics Facility, doi: <a href="https://entrepot.recherche.data.gouv.fr/dataverse/migale">10.15454/1.5572390655343293E12</a>) for providing computing resources. We are also grateful for the help and support provided by <a href="https://www.liverpool.ac.uk/savsnet/">SAVSNET team members</a> Bethaney Brant, Susan Bolan and Steven Smyth. This study was funded mainly by a grant from the Biotechnology and Biological Sciences Research Council, BB/NO19547/1 and British Small Animal Veterinary Association (BSAVA). The research was partly funded by the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Emerging and Zoonotic Infections at the University of Liverpool in partnership with Public Health England (PHE) and Liverpool School of Tropical Medicine (LSTM). This work has been partially funded by the “Monitoring outbreak events for disease surveillance in a data science context" (MOOD) project from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 874850 (<a href="https://mood-h2020.eu/">https://mood-h2020.eu/</a>). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, the Department of Health or Public Health England.

CFMDG: a Coastal Flood Modelling Dataset in Gâvres (France) to support risk prevention and metamodels development

Along most of the coastal areas, detailed coastal flood observations (e.g. inland water depths) are scarce, and when they are available, this for a limited number of events. Given recent scientific advances, coastal flooding events can be properly modelled, even in complex environments and under the action of wave overtopping, and thus provide detailed information. However, such models are computationally expensive, which prevents their use for instance for forecasting and warning. At the same time, metamodelling techniques have been explored for coastal hydrodynamics and have shown promising results. Metamodels are functions that aim to reproduce the behaviour of a “true” model (e.g., a numerical hydrodynamic model) for given input variables (for instance, offshore conditions). Within the RISCOPE research project (<a href="http://perso.math.univ-toulouse.fr/riscope">https://perso.math.univ-toulouse.fr/riscope</a>/) aiming at exploring to which extent such metamodelling techniques may allow to forecast coastal floods with a good accuracy, a simulated flood database has been built for the site of Gâvres (France), characterised by a significant effect of wave overtopping processes. The CFMDG dataset compiles a set of post-processed coastal flood simulations on the site of Gâvres. The dataset includes 250 scenarios. Each scenarios is defined by 6h time series centered on high tide, with one time series per forcing variables. The forcing variables (called X) are: local relative mean sea-level, tide, atmospheric storm surge, the offshore wave characteristics and the offshore wind. These scenarios combine past real (flood and no flood) events in the 1900-2021 time span with extreme statistics based events, and some complementary fictive events. The post-processed outputs (called Y) includes, for each scenario, the maximal flooded area (m²) and the maximal water depth (m) in each of the 64 618 inland model grid points. The modelling chain that allowed building this dataset relies on the joint use of a spectral wave model (WW3) to propagate the waves to the coast, and a non-hydrostatic wave-flow model (SWASH) to simulate the nearshore hydrodynamics and the flooding. The spatial and temporal resolution of the SWASH configuration validated on the Gâvres site are respectively 3 m and more than 10Hz. All the results are obtained for a Digital Elevation Model corresponding to the 2018 configuration of the site.    Such type of dataset is of use for local knowledge, risk prevention, metamodel testing/training, and local coastal flood forecast.  Part of this dataset has already been used in (<a href="http://www.mdpi.com/2077-1312/9/11/1191">Idier et al., 2021</a>; <a href="http://www.sciencedirect.com/science/article/pii/S0951832021006293?via%3Dihub">López-Lopera et al., 2021</a>; <a href="https://hal.science/hal-02536624">Betancourt et al., 2022</a>), to develop metamodels and set up a coastal flood forecast and early warning prototype. We hope and expect that making this dataset accessible will trigger further developments/investigations for improving risk knowledge on the considered site as well as methodological developments on machine-learning/metamodel-based techniques to support flood forecast. The table below summarizes the variables contained in the dataset, for each scenario. <table> <tbody> <tr> <td> Variable name </td> <td> Description and unit </td> <td> Comment </td> </tr> <tr> <td> Scenario n° </td> <td> Number of the scenario. </td> <td>   </td> </tr> <tr> <td> INPUTS (X) </td> </tr> <tr> <td> NM </td> <td> Relative mean sea level, referenced to the French vertical datum (m, IGN69) </td> <td> Time series over 6h </td> </tr> <tr> <td> T </td> <td> Tidal water level (m), referenced to the relative mean sea level </td> <td> Time series over 6h </td> </tr> <tr> <td> S </td> <td> Atmospheric storm surge (m) </td> <td> Time series over 6h </td> </tr> <tr> <td> Hs </td> <td> Significant wave height (m) </td> <td> Time series over 6h </td> </tr> <tr> <td> Tp </td> <td> Wave peak period (s) </td> <td> Time series over 6h </td> </tr> <tr> <td> Dp </td> <td> Wave peak direction (° in nautical convention) </td> <td> Time series over 6h </td> </tr> <tr> <td> U </td> <td> Wind speed (m/s) </td> <td> Time series over 6h </td> </tr> <tr> <td> DU </td> <td> Wind direction (° in nautical convention) </td> <td> Time series over 6h </td> </tr> <tr> <td> t </td> <td> Relative time centered on the high tide of each event (min) </td> <td> Not Concerned </td> </tr> <tr> <td> High Tide date </td> <td> UTC date for scenarios corresponding to past real events </td> <td> Not Concerned </td> </tr> <tr> <td> OUTPUTS (Y) </td> </tr> <tr> <td> Smax </td> <td> Maximum flooded area during the event (m²) </td> <td> Post-processed scalar output </td> </tr> <tr> <td> Hmax </td> <td> Maximum water depth reached during the event (m), provided for each inland location </td> <td> Post-processed functional (map) output </td> </tr> <tr> <td> longitude </td> <td> Longitude (°, WGS84) </td> <td> For each inland location point </td> </tr> <tr> <td> latitude </td> <td> Latitude (°, WGS84) </td> <td> For each inland location point </td> </tr> <tr> <td> XL93 </td> <td> Longitude (m, Lambert 93) </td> <td> For each inland location point </td> </tr> <tr> <td> YL93 </td> <td> Latitude (m, Lambert 93) </td> <td> For each inland location point </td> </tr> </tbody> </table>

opencc-by-2.5Dec 2022View details →

BioASQ-QA: A manually curated corpus for Biomedical Question Answering

The BioASQ question answering (QA) benchmark dataset contains questions in English, along with golden standard (reference) answers and related material. The dataset has been designed to reflect real information needs of biomedical experts and is therefore more realistic and challenging than most existing datasets. Furthermore, unlike most previous QA benchmarks that contain only exact answers, the BioASQ-QA dataset also includes ideal answers (in effect summaries), which are particularly useful for research on multi-document summarization. The dataset combines structured and unstructured data. The material linked with each question comprise documents and snippets, which are useful for Information Retrieval and Passage Retrieval experiments, as well as concepts that are useful in concept-to-text Natural Language Generation. Researchers working on paraphrasing and textual entailment can also measure the degree to which their methods improve the performance of biomedical QA systems. Last but not least, the dataset is continuously extended, as the BioASQ challenge is running and new data are generated.

Early EASE-GRID Sea Ice Age, 1978-1983

Early spin-up period Arctic sea ice age data for 1978 through 1983. This product augments the NSIDC sea ice age product: "EASE-Grid Sea Ice Age, Version 4.1" (Tschudi et al., 2019a), which begins in January 1984. See the main product website for complete documentation. The age is estimated via Lagrangrian tracking based on the NSIDC sea ice motion product (Tschudi et al., 2019b), whose source data is primarily passive microwave brightness temperatures and drifting buoys. Age is estimated weekly as annual age categories. Values are: 1 for "first-year ice", ice that is 0-1 years old, and so on for older ice. The ice is "aged" once each year during the week of the annual sea ice minimum extent, generally sometime in September.  In this product, the initialization of the field begins with the first available data in late-October 1978. For the existing ice at that time, age is initialized at the start of the product with age=1. The first week of the data, because it is after the minimum, the age of existing ice is augmented to age=2 and new ice is given age=1. So, the first field in 1978 has only two age categories of 1 (0-1 years old) or 2 (1-2 years old) and this continues through 1978. This means that the age of the ice that formed between the minimum in September and the beginning of the data in late-October 1978 is overestimated by one year. In subsequent years, the oldest ice category will continue to overestimate some of the ice pack until that initial ice either: (1) melts, (2) is transported out of the Arctic, or (3) reaches the maximum age in the product (16 years). Much of the the existing ice in 1978 may be older than 1-2 years old as ice may stay in the Arctic for 5 or more years, but the data availability and the Lagrangian methodology cannot give a specific until the product is fully "spun up". For each subsequent year, a one-year older age category is added in the week of each year's extent minimum. Note that due to the assumption made at the beginning of the product in 1978, the oldest ice category may overestimate the true age of some parcels by one year.  Tschudi, M., W. N. Meier, J. S. Stewart, C. Fowler, and J. Maslanik. (2019a). EASE-Grid Sea Ice Age, Version 4 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/UTAV7490FEPB. Date Accessed 02-20-2023. Tschudi, M., W. N. Meier, J. S. Stewart, C. Fowler, and J. Maslanik. (2019b). Polar Pathfinder Daily 25 km EASE-Grid Sea Ice Motion Vectors, Version 4 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/INAWUWO7QH7B.

Rainfall data from WRF simulations for the Atacama Desert for present and mid-Pliocene climate

We provide model output for rainfall from WRF experiments for the present-day and mid-Pliocene climate. These are netCDF files that contain processed data shown in figures of Reyers et al. (accepted). Details on the files and content are listed in the primary data information Reyers_et_al_primary_data_information.pdf Refer to Reyers et al. (2022) for the full information on the data production and interpretation. This work used resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under project ID bb1198. The research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Projektnummer 268236062 – SFB1211 "Earth-evolution at the dry limit" (https://sfb1211.uni-koeln.de/). Reference Reyers, M., Fiedler, S., Ludwig, P., Böhm, C., Wennrich, V., and Shao, Y.: On the importance of moisture conveyor belts from the tropical East Pacific for wetter conditions in the Atacama Desert during the Mid-Pliocene, Clim. Past Discuss. [preprint], https://doi.org/10.5194/cp-2022-72, 2022, accepted.

Dataset for paper "Target selection for Near-Earth Asteroids in-orbit sample collection missions"

This dataset can be used to reproduce the results of the paper titled "Target selection for Near-Earth Asteroids in-orbit sample collection missions." The "results" folder contains the data to reproduce the maps and the rankings of the target asteroids. The "trajectories" folder contains the propagation of the sample trajectories used to obtain the grids.

Look-Up Table of A Prototype of Reconfigurable Intelligent Surface with Continuous Control of the Reflection Phase

Tabulated values (look-up table) of the magnitude (dB) and phase (degres) of the unit-cell versus voltage, experimentally characterized, for a Reconfigurable Intelligent Surface prototype based on varactors described in : R. Fara, P. Ratajczak, D. -T. Phan-Huy, A. Ourir, M. Di Renzo and J. de Rosny, "A Prototype of Reconfigurable Intelligent Surface with Continuous Control of the Reflection Phase," in IEEE Wireless Communications, vol. 29, no. 1, pp. 70-77, February 2022, doi: 10.1109/MWC.007.00345. also accessible here: https://arxiv.org/ftp/arxiv/papers/2105/2105.11862.pdf

Dataset for paper "Ejecta cloud distributions for the statistical analysis of impact cratering events onto asteroids' surfaces: a sensitivity analysis"

Dataset for the paper "Ejecta cloud distributions for the statistical analysis of impact cratering events onto asteroids' surfaces: a sensitivity analysis" published in Icarus.

opencc-zeroFeb 2023View details →

Samenvattende tabel bij het PrIUS-rapport

Deze tabellen verzamelen alle basisgegevens uit het PrIUS-rapport, voor externe raadpleging. <blockquote> D'hondt, B., Hillaert, J., Devisscher, S. & Adriaens, T. (2022) Een kader voor de aanpak van invasieve uitheemse soorten in Vlaanderen: een prioritering voor het natuurbeleid (PrIUS). Instituut voor Natuur- en Bosonderzoek. 165 blz. (Rapporten van het Instituut voor Natuur- en Bosonderzoek; no. 36) <a href="https://doi.org/10.21436/inbor.88096226">https://doi.org/10.21436/inbor.88096226</a> </blockquote> De tabellen zijn aangeleverd in xlsx- en csv-formaat. Beiden geven 38 kernvariabelen weer die in het rapport zijn uitgewerkt. Voor alle details wordt naar het rapport verwezen. xlsx (tabblad 'PrIUS') <ul> <li>Variabelen met betrekking tot Soort (rapport 3.1, 3.2; Tabel 1, 2): nr., groep, soort, species, lijst, GBIF-codes;</li> <li>m.b.t. Milieu (rapport 5.1.1; Tabel 3; fiches): marien, brakwater, zoetwater, terrestrisch;</li> <li>m.b.t. Klimaat (rapport 5.1.2; Tabel 4; fiches): Cfa, Cfb, ∑(Cfa,Cfb);</li> <li>m.b.t. Impact (rapport 5.2; Tabel 6, 7; fiches): "natuur", "volksgezondheid", "veiligheid", "infrastructuur", "(socio)economisch";</li> <li>m.b.t. Aanwezigheid (rapport 5.3; Tabel 8; Figuur 6, 7, 8; fiches): aan_Vlaanderen, hokken_VL, hokken_N2000, %_in_N2000, %_in_HRL, %_in_VRL, &permil;_van_N2000, &permil;_van_HRL, &permil;_van_VRL;</li> <li>m.b.t. Status van toename (rapport 5.4; Tabel 9; fiches): svt_Vlaanderen, svt_N2000;</li> <li>m.b.t. Beheerbaarheid (rapport 5.5; Figuur 9): indamming_min., indamming_gem., indamming_st.afw., indamming_max., uitroeiing_min., uitroeiing_gem., uitroeiing_st.afw., uitroeiing_max.;</li> <li>m.b.t. Integratie (rapport 6.2): int_groep.</li> </ul> csv De csv-tabel geeft dezelfde data weer als de xlsx-tabel (tabblad 'PrIUS'). In functie van een vlotte dataverwerking (met name in de programmeeromgeving R) zijn ten opzichte van dit bestand de volgende wijzigingen doorgevoerd: <ol> <li>inleidende rijen zijn verwijderd;</li> <li>variabelenamen zijn licht gewijzigd (vereenvoudigd);</li> <li>voor de numerieke variabelen is "nvt" (niet van toepassing) omgezet naar "NA". Noot: dit betekent een verlies van informatie, voor die variabelen waar er een reëel onderscheid is tussen velden die niet van toepassing zijn (gegeven de PrIUS-workflow) en velden waarvoor geen waarde bekomen is (bv. vanwege databeperkingen). Zie de xlsx-tabel voor dit onderscheid.</li> </ol>

QPR: Datasets version 2

Se publican dos archivos.   <ol> <li> QPR_datacurada_feb23.xls: Dataset generado con la plataforma de Ciencia Ciudadana Social ¿Qué Pasa, Riachuelo? (QPR) versión alfa. Publicamos aquellos datos que pudieron ser curados en instancia de taller con co-investigadores.   </li> </ol> <ol> <li> QPR_metadata_feb23.csv: Metadatos completos del set de datos que se genera en los siete formularios presentes en ¿Qué Pasa, Riachuelo? (QPR)  </li> </ol>   Proyecto CoAct:   CoAct es un proyecto global financiado por la Unión Europea que aborda problemas complejos asociados al empleo juvenil, la salud mental y la justicia ambiental mediante herramientas de ciencia ciudadana social.  La ciencia ciudadana social se entiende en este contexto como investigación participativa co-diseñada e impulsada por grupos de personas que comparten una preocupación social. Esta metodología busca generar instancias que les permita a las comunidades tener una participación activa en la investigación, desde el diseño hasta la interpretación de los resultados y su transformación en acciones concretas. Las personas actúan así como co-investigadoras reconociendo sus competencias en el campo de aplicación.  Proyecto CoAct Justicia Ambiental  En la Cuenca Matanza-Riachuelo, un grupo de 56 co-investigadores trabajando en conjunto con el Centro de Investigaciones para la Transformación (CENIT) de la Universidad Nacional de San Martín (UNSAM) y la Fundación Ambiente y Recursos Naturales (FARN) y con la ayuda de muchas otras personas que en distintos momentos contribuyeron con el proyecto, se co-diseñó la plataforma ¿Qué pasa, Riachuelo?. Los co-investigadores son personas que tienen un conocimiento cercano de los problemas socio-ambientales de la cuenca porque viven o trabajan allí y están interesadas en su transformación. Durante talleres realizados durante los años 2020 y 2021 se definieron los temas, se co-diseñaron los formularios para generar datos y las principales funcionalidades de la plataforma. Se puede conocer más información sobre este proceso en <a href="https://farn.org.ar/coact-justicia-ambiental/">https://farn.org.ar/coact-justicia-ambiental/</a> o en <a href="https://coactproject.eu/">https://coactproject.eu/</a>  La plataforma QPR recoge información sobre tres temas: calidad de agua, áreas naturales y relocalización y reurbanización. Describimos a grandes rasgos el contenido asociado a cada tema  Calidad del agua: Información sobre la calidad observable del agua (e.g  turbidez, color, olor, presencia de objetos flotantes, vegetación en la ribera y cauce, fauna, proximidad a posibles fuentes de contaminación y otros) y situaciones hidrometeorológicas (e.g. niveles del agua y situación climática en los días previos al reporte).   Áreas naturales: Información sobre visitas a las áreas naturales Cuenca Matanza Riachuelo. Se informan usos, actividades u observaciones del patrimonio natural y cultural del área. Asimismo, se recoge información sobre valoraciones y amenazas que existen en las áreas naturales de la cuenca Matanza-Riachuelo y sobre la experiencia relacionada con su protección  Relocalización y reurbanización: Información sobre los avances en el proceso de relocalización y reurbanización de villas y asentamientos de la cuenca Matanza-Riachuelo a nivel de barrio o complejo habitacional. Mudanzas, obras de vivienda, obras de servicios públicos, celebración de mesas de trabajo, acceso a la información e identificación de principales problemáticas a atender en el proceso. Además, también existe la posibilidad de compartir información sobre la experiencia de participación en las mesas de trabajo barriales por la relocalización o reurbanización como: fecha, lugar, participantes, presencia de autoridades, temas tratados, registro de la mesa, posibilidades de participación, fecha de la próxima reunión, propuesta de otros temas a tratar.   Para cada uno de estos temas existen dos formularios, uno estructurado que no requiere registro previo y otro con campos abiertos donde se puede agregar relatos, imágenes y documentos que requiere registro previo. Además, existe un séptimo formulario que requiere registro previo para compartir novedades de actividades relacionadas con la justicia ambiental en la cuenca. En estos videos tutoriales se puede encontrar información sobre cada uno de los formularios de QPR.  CoAct Justicia Ambiental | ¿Cómo compartir experiencias sin estar registrado en QPR?   <a href="https://youtu.be/7zhWlEOi_Ic">https://youtu.be/7zhWlEOi_Ic</a>  CoAct Justicia Ambiental | ¿Cómo compartir experiencias registrándose en QPR?  <a href="https://youtu.be/YkjqRJvB4ZM">https://youtu.be/YkjqRJvB4ZM</a>  Set de datos y metadatos  El set de datos compartido a modo de ejemplo es sobre calidad de agua, y contiene información generada tanto por el formulario estructurado como por el abierto durante un taller con co-investigadores. Ver. QPR_datacurada_feb23.csv  Compartimos también el archivo QPR_metadata_feb23.xls que contiene la metadata completa de los siete formularios.  La primera columna del archivo de metadatos “Nombre de la variable” tiene el nombre de la variable que encabeza el set de datos. La segunda columna “Pregunta en el formulario” contiene la pregunta completa tal como está formulada en la plataforma. La tercera columna “Etiqueta en el mapa” contiene el título que se utiliza en la visualización de las respuestas en el mapa. La cuarta columna “Categorías o formato del dato” lista las categorías disponibles como respuesta en las preguntas estructuradas y en el caso que sea otro tipo de dato, por ejemplo, texto, fecha, coordenada, etc. se describe el formato. La quinta columna “Tipo de respuesta” describe el tipo de respuesta asociada a esa variable, por ejemplo, si es de respuesta única o múltiple, esta información es útil sobre todo para las variables categóricas. Y finalmente una sexta columna “Obligatoria u opcional” indica si la pregunta es de respuesta obligatoria u opcional.  Agradecimientos  El proyecto CoAct ha recibido financiamiento de la del programa Horizonte 2020 de la Unión Europea bajo el acuerdo de subvención número 873048. Expresamos nuestro agradecimiento a los co-investigadores por su conocimiento y el tiempo invertido en el co-diseño de la herramienta. En especial agradecemos a la Biblioteca Popular Sarmiento de Valentín Alsina por su rol en organizar salidas a campo para el tema calidad del agua que ayudaron a generar el presente set de datos.  -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- English Description Two files are published.    <ol> <li> QPR_datacurada_feb23.xls: Dataset generated with the Citizen Social Science platform ¿Qué Pasa, Riachuelo? (QPR) alpha version. We publish the data that were curated in a workshop with co-researchers.    </li> </ol> <ol> <li> QPR_metadata_feb23.csv: Complete metadata of the dataset that are generated with the seven forms available in ¿Qué Pasa, Riachuelo? (QPR)   </li> </ol> CoAct Project:    CoAct is a global EU-funded project that addresses complex issues associated with youth employment, mental health and environmental justice using social citizen science tools.   Citizen social science is understood in this context as participatory research co-designed and driven by groups of people who share a social concern. This methodology seeks to generate instances that allow communities to have an active participation in research, from design to interpretation of results and their transformation into concrete actions. People thus act as co-researchers, and their competences and experience in the field of application is recognised.   CoAct Environmental Justice Project   The platform ¿Qué pasa, Riachuelo? (QPR) was co-designed in the Matanza-Riachuelo Basin with a group of 56 co-researchers working together with the Research Center for Transformation (CENIT) of the National University of San Martín (UNSAM) and the Environment and Natural Resources Foundation (FARN) and with the help of many other people who contributed to the project in different moments. Co-researchers are people who have local knowledge of the socio-environmental problems of the basin because they live or work there and are interested in the transformation of these realities. During workshops held during the years 2020 and 2021, the themes, the forms to generate data and the main functionalities of the platform were co-designed. More information about this process can be found in <a href="https://farn.org.ar/coact-justicia-ambiental/">https://farn.org.ar/coact-justicia-ambiental/</a> or <a href="https://coactproject.eu/">https://coactproject.eu/</a>   The QPR platform collects information on three themes: water quality, natural areas and resettlement and redevelopment. We broadly describe the content associated with each theme   <ul> <li> Water quality: Information on observable parameters of water quality (e.g. turbidity, colour, odour, presence of floating objects, vegetation on the riverbank and riverbed, fauna, proximity to possible sources of pollution and others) and hydrometeorological situations (e.g.  water levels and weather situation in the days prior to the report).    </li> <li> Natural areas: Information on visits to the natural areas of the Matanza-Riachuelo Basin. Uses, activities, observations of natural and cultural patrimony of area are reported. Likewise, information is collected on values and threats that exist in the natural areas of the Matanza-Riachuelo Basin and on the experiences related to their protection.   </li> <li> Resettlement and redevelopment: Information on the progress in the processes of resettlement and redevelopment of slums and settlements in the Matanza-Riachuelo Basin at the neighbourhood or housing complex level. Removals, housing works, public service works, holding of work tables, access to information and identification of main problems to be addressed in the process. In addition, there is also the possibility of sharing information about the experience of participation in the neighbourhood’s work tables for resettlement or redevelopment such as: date, place, participants, presence of authorities, discussed topics, documentation of the table’s meetings, possibilities of participation, date of the next meeting, proposals of other topics to be discussed.    </li> </ul> For each of these themes there are two forms: one structured that does not require prior registration and another that requires prior registration with open fields where stories, images and documents can be added. In addition, there is a seventh form that requires prior registration that allows to share news of activities related to environmental justice in the basin. In these tutorial videos you can find information about each of the QPR forms.   <ul> <li> CoAct Environmental Justice | How to share experiences without being registered in QPR? <a href="https://youtu.be/7zhWlEOi_Ic">https://youtu.be/7zhWlEOi_Ic</a>   </li> <li> CoAct Environmental Justice | How to share experiences by registering with QPR <a href="https://youtu.be/YkjqRJvB4ZM">https://youtu.be/YkjqRJvB4ZM</a>   </li> </ul>   Data and metadata set   The dataset shared as an example is on water quality, and contains information generated by both the structured and open forms during a workshop with co-researchers. See. QPR_datacurada_feb23.csv      We also share the QPR_metadata_feb23.xls file containing the complete metadata of the seven forms.  The first column of the metadata file "Variable Name" has the name of the variable that heads the dataset. The second column "Question in the form" contains the complete question as it is formulated on the platform. The third column "Label on the map" contains the title that is used in the display of responses on the map. The fourth column "Categories or data format" lists the categories available as an answer in the structured questions and in the case that it is another type of data, for example, text, date, coordinate, etc. the format is described. The fifth column "Type of response" describes the type of response associated with that variable, for example, if there are multiple options. This information is useful especially for categorical variables. And finally a sixth column "Mandatory or optional" indicates whether the question is mandatory or optional. (Note that the metadata fields have been translated from Spanish here for description purposes)   Acknowledgements    The CoAct project has received funding from the European Union's Horizon 2020 programme under grant agreement number 873048. We especially thank the co-researchers for their knowledge and the time invested in co-designing the tool. We thank especially the  Community Library Sarmiento of Valentin Alsina for their role in organising fieldwork activities in the water quality theme which helped to create this dataset

The Impact of Gulf Stream Frontal Eddies on Ecology and Biogeochemistry near Cape Hatteras

This data goes along with the manuscript "The Impact of Gulf Stream Frontal Eddies on Ecology and Biogeochemistry near Cape Hatteras" available as a preprint at https://doi.org/10.1101/2023.02.22.529409 and under review in the Journal of Geophysical Research: Oceans. The code to analyze this data and generate the figures from the paper is available at: https://github.com/patrickcgray/gs_front_analysis (https://doi.org/10.5281/zenodo.7685135).

opencc-by-4.0Jul 2021View details →

Dataset for the IntoValue 1 + 2 studies on results dissemination from clinical trials conducted at German university medical centers completed between 2009 and 2017

The IntoValue dataset contains clinical trials conducted at one of 35 German UMCs and registered on ClinicalTrials.gov or the German Clinical Trials Registry (DRKS). All trials were reported as complete between 2009 and 2017 on the trial registry at the time of data collection. The dataset also includes a results publication found via manual searches; if multiple results publications were found, the earliest was included. Trials were associated with a German UMC by searching for trials with a UMC listed as responsible party or lead sponsor, or with a principle investigator (PI) from a UMC ('lead_city'). Version 1 additionally includes trials with a UMC only as a facility (`facility_city`). A lookup table of regular expressions used to identify German UMCs is available at <a href="https://github.com/quest-bih/IntoValue2/blob/master/data/1_sample_generation/city_search_terms.csv">https://github.com/quest-bih/IntoValue2/blob/master/data/1_sample_generation/city_search_terms.csv</a>. Trials include all interventional studies and are not limited to investigational medical product trials, as regulated by the EU's Clinical Trials Directive or Germany's Arzneimittelgesetz (AMG) or Novelle des Medizinproduktegesetzes (MPG). DRKS data were searched (pre-filtered for completion years and study status as well as Germany as 'Country of recruitment') and downloaded as CSVs from the DRKS website (<a href="https://www.drks.de/">https://www.drks.de/</a>). ClinicalTrials.gov data were downloaded downloaded as pipe files from Clinical Trials Transformation Initiative (CTTI) Aggregate Content of ClinicalTrials.gov (AACT) (<a href="https://aact.ctti-clinicaltrials.org/pipe_files">https://aact.ctti-clinicaltrials.org/pipe_files</a>). DRKS and ClinicalTrials.gov use different terminology for various trial aspects, such as phase and masking; these different levels are captured in the data dictionary as `levels_drks` and `levels_ctgov`. For later analyses requiring parity across registries, levels for some variables were collapsed and a lookup table is provided in `iv_data_lookup_registries.csv`. These data were generated and used for two publications (Wieschowski et al., 2019; Riedel et al. 2021) and therefore comprises two versions (indicated as `iv_version`). For version 1, registry data was collected on April 17, 2017 from ClinicalTrials.gov and on July 27, 2017 for DRKS and was limited to trials with a completion date on DRKS and primary completion date on ClinicalTrials.gov between 2009 and 2013. Version 1 manual searches for results publications were conducted from 2017-07-01 to 2017-12-01. For version 2, registry data was collected on June 3, 2020 and was limited to trials with a completion date on DRKS and ClinicalTrials.gov between 2014 and 2017. Version 2 manual searches for results publications were conducted from 2020-07-01 to 2020-09-01. Raw registry data for versions 1 and 2 is available in `raw-registries.zip`. Publication identifiers (DOI, PMID, URL) were manually entered during the publication search and then further enhanced using the API of Internet Archive's open-source Fatcat catalog of research publications, to add PMIDs based on DOIs, and vice versa. Manual search steps differed slightly in the two versions and are indicated and described in `identification_step`. Version 1 includes trials with a German UMC as either a `lead_city` or a `facility_city`, whereas version 2 is limited to trials a German UMC as a `lead_city`. Each row indicates a single trial registration. Due to changes in completion dates, some trials are duplicated between versions as indicated in `is_dupe`. Cross-registered trials were manually deduplicated, and some cross-registered duplicates remain (e.g., DRKS00004156 and NCT00215683) and are not indicated in the dataset. All dates are provided as `yyyy-mm-dd`. Additional documentation on each variable (type, description, levels) is provided in `iv_data_dictionary.csv`. Additional information on the project and methods for generating the dataset is available in associated publications and at the project's OSF page (<a href="https://osf.io/98j7u/">https://osf.io/98j7u/</a>). Code for the project is available at <a href="https://github.com/quest-bih/IntoValue2">https://github.com/quest-bih/IntoValue2</a>. References: Wieschowski, S., Riedel, N., Wollmann, K., Kahrass, H., Müller-Ohlraun, S., Schürmann, C., Kelley, S., Kszuk, U., Siegerink, B., Dirnagl, U., Meerpohl, J., & Strech, D. (2019). Result dissemination from clinical trials conducted at German university medical centers was delayed and incomplete. Journal of Clinical Epidemiology, 115, 37–45. <a href="https://doi.org/10.1016/j.jclinepi.2019.06.002">https://doi.org/10.1016/j.jclinepi.2019.06.002</a> Riedel, N., Wieschowski, S., Bruckner, T., Holst, M. R., Kahrass, H., Nury, E., Meerpohl, J. J., Salholz-Hillel, M., & Strech, D. (2021). Results dissemination from completed clinical trials conducted at German university medical centers remained delayed and incomplete. The 2014-2017 cohort. Journal of Clinical Epidemiology, 0(0). <a href="http://doi.org/10.1016/j.jclinepi.2021.12.012">https://doi.org/10.1016/j.jclinepi.2021.12.012</a>