cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/yg9h3nnpt5.2
title: Abdominal wound dehiscence and Incisional hernia prevention  a systematic review and network meta-analysis of randomized clinical trials. 

description: Background: Incisional hernia (IH) is the main complication after laparotomy. The objective of this network meta-analysis was to evaluate the effectiveness of closure technique modification (CTM) for reducing the incidence of IH and abdominal wound dehiscence (AWD) to provide objective support for its recommendation.
Methods: A network meta-analysis was performed according to the PRISMA-NMA guidelines. The primary objective was to determine the incidence of IH and AWD the secondary objective was to determine the incidence of postoperative complications. Only published clinical trials were included. The risk of bias was analyzed, and the random effects model was used to determine statistical significance.
Results: Twelve studies comparing 2,738 patients were included. The incidence of IH was significantly lower in the CTM group than in the control group, with an OR of 0.41 (95% CI 0.31-0.55). The incidence of AWD was also reduced, with an OR of 0.38 (95% CI 0.19-0.75). Associated complications, including hematoma, seroma, and postoperative pain, could not be analyzed; however, CTM did not increase the risk of surgical site infection.
Conclusion: CTM for midline laparotomy significantly reduces the incidence of IH and AWD compared to conventional closure. Limitations of the analysis included differences in follow-up, patient selection, diagnostic methods, and the reporting of postoperative complications among the studies.
Funding Information: No funding was received.
Registration: This study was prospectively registered in the PROSPERO database under registration number CRD42021231107.
DOI: 10.17632/x5h2skp4zz.1
title: Supplemental material - Curettage plus one or two cycles of cryosurgery for basal cell carcinoma with clinically nodular features - a prospective randomized controlled trial

description: The dataset presented here is the supplemental material of the letter: "Curettage plus one or two cycles of cryosurgery for basal cell carcinoma with clinically nodular features - a prospective randomized controlled trial" published in the Journal of the American Academy of Dermatology (JAAD). It contains a detailed method section, a table on times for wound healing, a figure on the non-inferiority result and the Consort checklist.
DOI: 10.17632/8psdkzpknf.1
title: Data for Nonmelanoma skin cancer and Mohs utilization in Medicaid patients. 

description: Our study highlights the treatment of Medicaid beneficiaries with nonmelanoma skin cancers at an institution that does not offer Mohs micrographic surgery for Medicaid patients. 

Figure 1. Treatment map of Medicaid patients with nonmelanoma skin cancers.

Table 1. Characteristics of nonmelanoma skin cancers in Medicaid patients (n = 212 cancers from 72 patients).
DOI: 10.17632/rx52dsdv4p.1
title: Orogenic cyclicity and episodic tectono-magmatic processes in the formation of the Paleozoic northern Yili magmatic arc, Central Asian Orogenic Belt

description: Supporting Information, including a text file (analytical methods, data filtered methods, and Figures S1 to S3) and 11 supplement tables (Table S1 to S11)
DOI: 10.17632/5gvznd2b3n.2
title: TCGA: A TROPICAL CORN GERMPLASM ASSEMBLY FOR GENOMIC PREDICTION AND HIGH-THROUGHPUT PHENOTYPING STUDIES
DOI: 10.17632/5gvznd2b3n.4
title: TCGA: A TROPICAL CORN GERMPLASM ASSEMBLY FOR GENOMIC PREDICTION AND HIGH-THROUGHPUT PHENOTYPING STUDIES

description: Genomic prediction (GS) studies using diversity panels are essential to identify genetic variations associated with traits of interest in maize. Unfortunately, most of these studies have been conducted on temperate germplasm and on a global germplasm collection in which tropical genotypes are under-represented. Nonetheless, a continuous effort has been directed to improving the accuracy of GS. While genotyping is currently a precise and efficient mechanized process, phenotyping is still laborious, low-throughput, and highly sensitive to environmental variations. Also, extreme shifts in the weather pattern due to climate change complicates the selection of superior genotypes with broad adaptability. In this context, adopting GS models that account for genotypes x environments reaction norms, crop growth models, and environmental covariates should increase the accuracy of genomic predictions. Thus, the objective of this project is to develop a diversity panel of tropical maize for genomic prediction studies that incorporate high-throughput phenotyping, plant growth models, and environmental covariables. For that, 360 tropical maize lines from ESALQ-USP, IAPAR, IAC, and CIMMYT will be genotyped and phenotyped using traditional methods and multispectral imaging in eight environments (two locations, two years, and two seasons). With this data, several GS models will be tested and compared for prediction accuracy and selection coincidence. Besides the development of novel GS models and high-throughput phenotyping protocols in tropical maize, we will also organize, characterize, and publicize a panel of tropical maize lines (data and genetic material) to the scientific community that will serve as the benchmark for new studies of this nature in tropical maize.

FAPESP (2017/24327-0)
DOI: 10.6084/m9.figshare.23274413
title: Annotated GSM3618014 dataset (10Xgenomics) from Tian et al. paper Nat Methods 2019 Jun;16(6):479-4872019 (https://pubmed.ncbi.nlm.nih.gov/31133762/).

description: The raw count table were provided without the association of the cell lines to each cell. We have assigned the cell line name to each cell via the similarity between single cell clusters and cell lines bulk data from CCLE database. The annotated dataset is GSM3618014_gene_count_w_celllines_in_header.csv.zip The full annotation procedure is available in tian2019_annotation.zip <br>
DOI: 10.6084/m9.figshare.23274413.v1
title: Annotated GSM3618014 dataset (10Xgenomics) from Tian et al. paper Nat Methods 2019 Jun;16(6):479-4872019 (https://pubmed.ncbi.nlm.nih.gov/31133762/).

description: The raw count table were provided without the association of the cell lines to each cell. We have assigned the cell line name to each cell via the similarity between single cell clusters and cell lines bulk data from CCLE database. The annotated dataset is GSM3618014_gene_count_w_celllines_in_header.csv.zip The full annotation procedure is available in tian2019_annotation.zip <br>
DOI: 10.20387/bonares-5j08-7067
title: 2019 Litter Bag Experiment UKiel Temperature data

description: In 2018/19 three different potential soil amendments (faba bean straw, winter wheat straw and spruce sawdust) grinded to 1-5 mm have been buried in nylon tea bags 10 cm deep in 20L pots. The soil used was extracted shortly before from a nearby field under agricultural use (1 month after harvest of faba beans). The bags were buried on 2018-08-17 and retrieved on 6 occasions until 2019-03-07. After cleaning and drying, dry matter of the remaining amendment in the bag was determined. Temperature was logged inside a pot, inside the facility (concrete soil) and outside in a tree (grassy soil).
DOI: 10.5281/zenodo.7985388
title: A software for automatic identification of oyster species

description: The files includes all data and final analysis of the work done in CS8 - oysters, task 8.2, CSTP8.2.2_A new software for automatic identification of oyster species. This includes the data management descriptor document (DataSheet_oyster_image_classification.docx), images used (oyster_classification_images.zip), the code developed (oyster_classification_code_package.zip) and different models evaluated (oyster_classification_models.zip), the genetics data produced (oyster_classification_biometrics and PCR.xlsx) and the project report (C639_ostronklassificering.pdf). The content of the files is described briefely below. The data is used in deliverables D1.2, D1.4, D1.5 and D1.6 in the AquaVitae project. oyster_classification_images.zip The data set contains the images used for training the classification models that are capable of classifying images of oysters as either Ostrea edulis or Magallana gigas. The images are sorted in folders named “train” (training data) and “validation” (validation data) with both folders containing sub-folders called “mg” (images of Magallana gigas) and “oe” (images of Ostrea edulis). oyster_classification_code_package.zip The data set contains the code for training a neural network for classifying oyster species based on images. The code also includes localization of oyster within an image and inference of the classification along with the trained models. oyster_classification_models.zip The data set contains the trained classification models that are capable of classifying images of oysters as either Ostrea edulis or Magallana gigas. oyster_classification_biometrics and PCR.xlsx The data set contains biometric information for a subset of 240 Ostrea edulis, 240 Magallana gigas and 204 oysters of unsure species denotation sampled as a start pool for the image analysis project and for genetic evaluation of species belonging.
DOI: 10.5281/zenodo.7985389
title: A software for automatic identification of oyster species

description: The files includes all data and final analysis of the work done in CS8 - oysters, task 8.2, CSTP8.2.2_A new software for automatic identification of oyster species. This includes the data management descriptor document (DataSheet_oyster_image_classification.docx), images used (oyster_classification_images.zip), the code developed (oyster_classification_code_package.zip) and different models evaluated (oyster_classification_models.zip), the genetics data produced (oyster_classification_biometrics and PCR.xlsx) and the project report (C639_ostronklassificering.pdf). The content of the files is described briefely below. The data is used in deliverables D1.2, D1.4, D1.5 and D1.6 in the AquaVitae project. oyster_classification_images.zip The data set contains the images used for training the classification models that are capable of classifying images of oysters as either Ostrea edulis or Magallana gigas. The images are sorted in folders named “train” (training data) and “validation” (validation data) with both folders containing sub-folders called “mg” (images of Magallana gigas) and “oe” (images of Ostrea edulis). oyster_classification_code_package.zip The data set contains the code for training a neural network for classifying oyster species based on images. The code also includes localization of oyster within an image and inference of the classification along with the trained models. oyster_classification_models.zip The data set contains the trained classification models that are capable of classifying images of oysters as either Ostrea edulis or Magallana gigas. oyster_classification_biometrics and PCR.xlsx The data set contains biometric information for a subset of 240 Ostrea edulis, 240 Magallana gigas and 204 oysters of unsure species denotation sampled as a start pool for the image analysis project and for genetic evaluation of species belonging.
DOI: 10.57745/5fsus2
title: Membrane properties of L. salivarius CECT5713 sensitive and resistant populations.txt

description: Membrane fatty acid composition, membrane fluidity and membrane phospholipid organization of L. salivarius CECT5713 sensitive and resistant populations
DOI: 10.57745/7qejty
title: Biological activity of L. salivarius CECT5713 senitive and resistant populations.txt

description: Culturability and storage stability by plate counts. Viable, injured and dead cells by flow cytometry
DOI: 10.57745/fbbyuh
title: Physical properties of L. salivarius CECT5713 sensitive and resistant concentrates -matrix.txt

description: Glass transition of freeze-thawed sensitive and resistant cell concentrates. Glass transition and water activity of freeze-dried sensitive and resistant cell concentrates, before and after 4 weeks storage.
DOI: 10.57745/jj6fsh
title: Biochemical characterization of L. salivarius CECT5713 sensitive and resistant cells by FTIR micro-spectroscopy.txt

description: FTIR micro-spectroscopy in aqueous environment of sensitive and resistant Ligilactobacillus salivarius CECT5713 cells
DOI: 10.57745/zudlmx
title: Physical properties of L. salivarius CECT5713 sensitive and resistant concentrates -matrix_v2.txt

description: Glass transition temperature of freeze-thawed sensitive and resistant cell concentrates. Glass transition temperature and water activity of freeze-dried sensitive and resistant cell concentrates, before and after 4 weeks storage. DSC raw data included: heat flow (and first derivative of heat flow) vs temperature.
DOI: 10.5281/zenodo.6793500
title: Reconstruction of flood probabilities in France, 1705-2015

description: As part of the HEGS project, an attempt was made at reconstructing flood probabilities for 207 stations in France during the period 1705-2015. This reconstruction is based on the historical flood marks database Repères De Crues. Details can be found in this publication. This repository contains the data underlying this reconstruction (historical flood marks at sites and flood peaks at hydrometric stations), and the reconstruction itself. A sonified animation of these data is available at https://vimeo.com/815008124. <strong>data/sites.txt</strong> Description of 327 flood mark sites, with the following columns: "index": site index. "ID": site ID. "lon": longitude. "lat": latitude. "town": town (in French: commune). "INSEEcode": INSEE code, an ID for the town. "river": name of the river whose floods created the marks. "zone": geographical area. "description": site description (in French). "firstMark": year of the first available flood mark. "lastMark": year of the last available flood mark. "nMark": number of available flood marks during the period 1705-2015. <strong>data/marksAtSites.txt</strong> Flood marks recorded at sites during the period 1705-2015, with the following columns: "ID": site ID. "year": year of the mark. "month": month of the mark. "day": day of the mark. "hydroYear": hydrological year of the mark (hydroYear e.g. 1998 goes from September 1998 to August 1999). "isFloodPeak": does the mark correspond to the flood peak? "elevation": elevation of the mark (in m). <strong>data/stations.txt</strong> Description of 207 hydrometric stations, with the following columns: "index": station index. "ID": station ID. "lon": longitude. "lat": latitude. "area": area of the catchment monitored by the station. "description": station description (in French). "nMonth": number of months with available data. <strong>data/peaksAtStations.txt</strong> Flood peaks recorded at stations, with the following columns: "ID": site ID. "hydroYear": hydrological year. "amax": annual maximum value of daily discharge (in m<sup>3</sup>.s<sup>-1</sup>). "mm": same as above but converted in mm. <strong>reconstructions.txt</strong> Reconstructed distribution of annual maxima, estimated at all stations and for the period 1705-2015, with the following columns: "year": hydrological year. "stationID": ID of the station. "lon": longitude. "lat": latitude. "median": median of the estimated GEV distribution for this year and this station (in mm). "low": 0.05-quantile of the estimated GEV distribution for this year and this station (in mm). "high": 0.95-quantile of the estimated GEV distribution for this year and this station (in mm). "prob": Estimated probability of exceeding a 10-year event for this year and this station.
description: This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 835496
DOI: 10.5281/zenodo.6773480
title: Global reconstruction of flood and heavy precipitation probabilities, 1836-2015

description: As part of the HEGS project, an attempt was made at reconstructing flood and heavy precipitation probabilities for thousands of stations worldwide and for the period 1836-2015. This repository contains the precipitation/streamflow data underlying this reconstruction (https://vimeo.com/802751683), and the reconstruction itself. Details can be found in this publication. <strong>Data</strong> Seasonal maxima of daily precipitation or streamflow. One file for each season, with the following columns: "var": variable 'Rx1day' (heavy precipitation) or 'Qx' (flood). "year": year. For the DJF season, year e.g. 1998 spans from December 1998 to February 1999. "siteID": ID of the site, as used in the original HadEX2/HadEX3 and GSIM datasets. "lon": longitude. "lat": latitude. "value": seasonal maximum value, in mm (precipitation) or m<sup>3</sup>.s<sup>-1</sup> (streamflow). "returnPeriod": return period T associated with the value above. "nonExceedanceProb": non-exceedance probability associated with the return period (p=1-1/T). <strong>Reconstructions</strong> Probability of exceeding T-year events and predictive quantiles, estimated at all stations and for the period 1836-2015. One file for each season, with the following columns: "var": variable 'Rx1day' (heavy precipitation) or 'Qx' (flood). "year": year. For the DJF season, year e.g. 1998 spans from December 1998 to February 1999. "siteID": ID of the site, as used in the original HadEX2/HadEX3 and GSIM datasets. "lon": longitude. "lat": latitude. "Pr[exceeding the 2-year event]": estimated probability of exceeding the 2-year event at this site during this season. "Pr[exceeding the 10-year event]": estimated probability of exceeding the 10-year event at this site during this season. "Pr[exceeding the 100-year event]": estimated probability of exceeding the 100-year event at this site during this season. "q5": 5%-quantile of the predictive distribution at this site during this season. "q10": 10%-quantile of the predictive distribution at this site during this season. "q25": 25%-quantile of the predictive distribution at this site during this season. "q50": 50%-quantile of the predictive distribution at this site during this season. "q75": 75%-quantile of the predictive distribution at this site during this season. "q90": 90%-quantile of the predictive distribution at this site during this season. "q95": 95%-quantile of the predictive distribution at this site during this season.
description: This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 835496
DOI: 10.5281/zenodo.6773481
title: Global reconstruction of flood and heavy precipitation probabilities, 1836-2015

description: As part of the HEGS project, an attempt was made at reconstructing flood and heavy precipitation probabilities for thousands of stations worldwide and for the period 1836-2015. This repository contains the precipitation/streamflow data underlying this reconstruction, and the reconstruction itself. <strong>Data</strong> Seasonal maxima of daily precipitation or streamflow. One file for each season, with the following columns: "var": variable 'Rx1day' (heavy precipitation) or 'Qx' (flood). "year": year. For the DJF season, year e.g. 1998 spans from December 1998 to February 1999. "siteID": ID of the site, as used in the original HadEX2/HadEX3 and GSIM datasets. "lon": longitude. "lat": latitude. "value": seasonal maximum value, in mm (precipitation) or m<sup>3</sup>.s<sup>-1</sup> (streamflow). "returnPeriod": return period T associated with the value above. "nonExceedanceProb": non-exceedance probability associated with the return period (p=1-1/T). <strong>Reconstructions</strong> Probability of exceeding a T-year event, estimated at all stations and for the period 1836-2015. One file for each season, with the following columns: "var": variable 'Rx1day' (heavy precipitation) or 'Qx' (flood). "year": year. For the DJF season, year e.g. 1998 spans from December 1998 to February 1999. "siteID": ID of the site, as used in the original HadEX2/HadEX3 and GSIM datasets. "lon": longitude. "lat": latitude. "eventLabel": definition of the event whose probability of exceedance is sought. "prob": Estimated probability of exceeding the event defined above at this site during this season.
description: This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 835496
DOI: 10.5281/zenodo.7853032
title: Codes and data related to the article: Renard (2023). Use of a National Flood Mark Database to Estimate Flood Hazard in the Distant Past. Hydrological Sciences Journal.

description: This package contains R codes and data related to the article: B. Renard. 2023. Use of a National Flood Mark Database to Estimate Flood Hazard in the Distant Past<em>. Hydrological Sciences Journal</em>. DOI: 10.1080/02626667.2023.2212165 <strong>CONTENTS</strong> R scripts used to set up models, analyse results and prepare figures. France207_MAX.RData contain monthly maxima at 207 stations. See references below for the original sources. Folder data_raw/ contain exports from the flood marks database. See references below for the original sources. funk.R is a library of functions used by other scripts. 0_setUp.R prepares the runs to estimate the model for one component. Estimation is performed using the RSTooDs package (https://zenodo.org/record/5075760). This script extracts the data, specifies the probabilitic models and write STooDs configuration files. 1_margin.R analyses the runs that have been performed and estimates marginal distributions at each station. Note that the runs set up in the previous point need to have been performed before using this script. 2_probMap.R computes estimated flood probabilities over the whole 1705-2015 period. The previous script needs to have been run before. 3_sensitivityAnalysis.R prepares configuration files for sensitivity analysis experiments. 4_ZenodoRelease.R prepares the released data and probability maps. The previous scripts needs to have been run before. Scripts named fig_XXX.R create the figures shown in the article. <strong>REFERENCES</strong><br> Streamflow series at stations: https://www.hydro.eaufrance.fr/<br> developed by the "Service central d’hydrométéorologie et d’appui à la prévision des inondations" (Schapi) from the Ministry of the Ecological Transition Flood marks database: https://www.reperesdecrues.developpement-durable.gouv.fr/<br> developed by the flood forecasting network "Vigicrues" from the Ministry of the Ecological Transition
DOI: 10.5281/zenodo.7993172
title: National Taiwan Museum Rational Validity Assessment, May 2023

description: This is the data gathered in order to perform a rational validity assessment of the National Taipei Museum, checking its displayed collection against its mission statement.
DOI: 10.5281/zenodo.7993171
title: National Taiwan Museum Rational Validity Assessment, May 2023

description: This is the data gathered in order to perform a rational validity assessment of the National Taipei Museum, checking its displayed collection against its mission statement.
DOI: 10.5519/qd.k3i8wvdk
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (189 records)
DOI: 10.5281/zenodo.7680595
title: Codes and data related to the article: Renard et al. Floods and Heavy Precipitation at the Global Scale: 100-year Analysis and 180-year Reconstruction. Journal of Geophysical Research - Atmospheres.

description: This package contains R codes and data related to the article: B. Renard, D. McInerney, S. Westra, M. Leonard, D. Kavetski, M. Thyer and J.-P. Vidal. Floods and Heavy Precipitation at the Global Scale: 100-year Analysis and 180-year Reconstruction. <em>Journal of Geophysical Research - Atmospheres</em>. DOI: 10.1029/2022JD037908 <strong>Analyses</strong> This folder contains the R scripts used to set up models, analyse results and prepare figures. See README file for details. <strong>ShinyApp</strong> This folder contains an interactive Shiny App to explore the data and the results from the article. An online version can be found at https://hydroapps.recover.inrae.fr/HEGS-paper