cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.1594/wdcc/chelsa_v1
title: CHELSA climatologies at high resolution for the earth's land surface areas (Version 1.0)

description: Project: Climatologies at high resolution for the earth's land surface areas - CHELSA (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas currently under development in coorporation with the Department of Geography of the University of Hamburg (Prof. Dr. Jürgen Böhner, Dr. Olaf Conrad, Tobias Kawohl), the Swiss Federal Institute for Forest, Snow and Landscape Research WSL (Prof. Dr. Niklaus Zimmermann), the University of Zurich (Dr. Dirk N. Karger, Dr. Michael Kessler), and the University of Göttingen (Prof. Dr. Holger Kreft). Version 1.0 is a first release. CHELSA is an ongoing research project and we are updating it continuously. Swiss National Funds (SNF 147630, SNF 146906) Summary: CHELSA_v1.0 (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas. Version 1.0 is a first release. It includes monthly and annual mean temperature and precipitation patterns for the time period 1979-2013. CHELSA_v1 is based on a quasi-mechanistical statistical downscaling of the ERA interim global circulation model (http://www.ecmwf.int/en/research/climate-reanalysis/era-interim) with a GPCC (https://www.dwd.de/EN/ourservices/gpcc/gpcc.html) and GHCN (https://www.ncdc.noaa.gov/ghcnm/) bias correction. Specifications: High resolution (30 arcsec, ~1 km) Precipitation & Temperature Monthly coverage 1979 - 2013 Incorporation of topoclimate (e.g. orographic rainfall & wind fields). Downscaled ERA-interim model. Allows calculation of derived parameters based on monthly values such as length of dry periods etc.
DOI: 10.17632/h9y4hb8sf8.1
title: AQ-CNEA-CAC Air quality dataset (2019-2020):  "A machine learning approach to address air quality changes during the COVID-19 lockdown in Buenos Aires, Argentina"

description: This dataset provides hourly air quality data collected from the National Atomic Energy Commission of Argentina (CNEA-CAC: 34° 24' S, 58° 31' W). The data spans from February 2019  to May 2020. The pollutants included are carbon monoxide (CO), nitrogen oxide (NO), nitrogen dioxide (NO₂),  sulfur dioxide (SO₂) and ozone (O₃). Meteorological Variables of the Observatory Buenos Aires (OBS: 34° 35' S , 58° 29' W) used to run the Random Forest simulations (Wind speed, wind direction, 2 m temperature, 2 m relative humidity, sea level pressure and calm condition) are also provided. 

A full list of variables is shown below.
"date"  Datetime; "NO" nitrogen oxide concentration [ppm]; "NO2"  nitrogen dioxide concentration [ppm]; "NOx"  nitrogen oxides concentration [ppm]; "CO"  carbon monoxide concentration [ppm]; "O3"  ozone concentration [ppm]; "SO2"  sulfur dioxide concentration [ppm]; "aqsite" air quality site; "t2"  2 m temperature (°C); "rh2"  2 m relative humidity [%]; "slp" sea level pressure [hPa]; "wspd"  wind speed [degrees]; "wdir"  wind direction [km/hr]; "calm"  calm condition; "meteosite"  meteorological site; "gas_emcycle" gas emission cycle; "aer_emcycle" aerosol emission cycle; "month"  month; "U" U wind component; "V"  V wind component; "daynight"  [morning, day or night]; "datatype" [training: Data used for training and testing, evaluation: Data used to evaluate model skills (BLD) , lockdown: Data used to estimate relative changes during the lockdown phases (LD and PLD)].

An introductory code to use the data is also provided. The code will be run from the R script AQ_RandomForestCNEA.R. AQ_RF_functions are the auxiliary functions used and RF_parameters.csv is the file you need to provide in order to set the predictors and other parameters needed.
DOI: 10.17632/k2gj5ykrzj.1
title: Boermans and Burger JIE 2023

description: Code and data supporting Boermans and Burger (2023) “Fickle Emerging Market Flows, Stable Euros, and the Dollar Risk Factor,” Journal of International Economics. Data includes Euro Area holdings of (and flows to) emerging market bonds.
DOI: 10.17632/p4w45rcfgv.1
title: The dataset of recycled and virgin plastic materials in Japan

description: These datasets are the dataset for "The Recycled Content of Plastic Products: Estimating the Impact of a Recycling Law on the Input Mix"
We can use these datasets in Stata 17.
DOI: 10.25326/243
title: Calibrated stable water isotope data in precipitation from the R/V Maria Sybilla Merian during EUREC4A 

description: Water isotope in precipitation collected on the R/V Maria S. Merian, includes VSMOW-SLAP normalisation.
DOI: 10.25326/262
title: MUSICA IASI water isotopologue pair product (a posteriori processing version 2), customized subset for EUREC4A-iso"

description: This is the MUSICA IASI dataset of {H2O, dD} pairs customized for the EUREC4A-iso campaign (https://eurec4a.eu/overview/eurec4a-iso). It includes the MUSICA IASI {H2O, dD} pair results for a 10°x10° box over Barbados for the period 10 January to 20 February 2020. This data package comprises the full data output as described in Diekmann et al. (2021, https://doi.org/10.5194/essd-2021-87), i.e. all results for the given period and region for all retrieval grid levels between the surface and 56 km, full averaging kernel information and flag variables indicating the quality of the individual observations. Additionally, data are provided for an extended box around Barbados (21° to 54° N, 110° W to 22° E). The dataset for the extended box includes only quality-filtered {H2O, dD} pair data for the altitudes 2.9, 4.2 and 6.4 km and it consists only of the main variables describing the H2O and dD state. Information about the MUSICA IASI dataset together with a data user guide can be found in Diekmann et al. (2021, https://doi.org/10.5194/essd-2021-87) and the full MUSICA IASI {H2O, dD} pair dataset is found at https://dx.doi.org/10.35097/415.
DOI: 10.25326/309
title: BCO LGR Continuous In-Situ Water Vapor Isotopic Composition

description: During the EUREC4A campaign, a Los Gatos Research (LGR) water-vapor isotope analyzer, using off-axis integrated cavity output spectroscopy (OA-ICOS) was operated at the Barbados Cloud Observatory (BCO) at 13.16°N, 59.43°W from 18 January to 16 February 2020. The instrument was installed alongside a Picarro Water Vapor Isotopic Analyzer in a temperature-regulated container (24±2°C). Both analyzers sampled ambient air from the same protected inlet and heated inlet line. Water vapor isotope data from the LGR were normalized to the VSMOW-SLAP scale using three liquid standards; two were measured once, while a third was introduced during daily checks. The daily standard was interpolated to the raw 1 Hz time resolution and the IAEA procedure (IAEA, 2017) was used to calibrate and account for instrument drift. No humidity-dependence correction was applied to the data. Uncertainty estimates are ±0.41‰ and ±0.94‰ for δ18O and δD respectively. Masked (missing) data represent periods when daily calibration checks or instrument maintenance were performed. All variables have been averaged to 1-minute intervals.
DOI: 10.5281/zenodo.4660553
title: Serverless Literature Dataset

description: <strong>Serverless computing literature dataset</strong> <strong>Topic</strong>:<strong> </strong>Serverless,<strong> </strong>Function as a Service(FaaS), Applications, Benefit, Challenges, Issues, tools, and platforms. <strong>Data source:</strong> ACM Digital Library, IEEE Xplore, ScienceDirect, Scopus, and SpringerLink. <strong>Search string:</strong> (serverless OR FaaS OR "function as a service" OR "function-as-a-service") AND (computing OR paradigm OR architecture OR model OR application OR function OR service OR platform OR programming) <strong>Duration:</strong> (2016 - 2020)
DOI: 10.5281/zenodo.6587510
title: Reference Datasets for: SHAFTS (v2022.3): a deep-learning-based Python package for Simultaneous extraction of building Height And FootprinT from Sentinel Imagery

description: These are reference building height and footprint datasets which consist of 46 cities worldwide and support the development of SHAFTS (https://github.com/LllC-mmd/3DBuildingInfoMap). The snapshot of original reference datasets from 46 cities and related GitHub repository has been created as a zipped file named <strong><em>SHAFTS_220527_snapshot.zip</em></strong>. On 2022.5.27, we added 8 additional cities from ArcGIS Hub when compared with the previous version (https://doi.org/10.5281/zenodo.6370003).
DOI: 10.5281/zenodo.7570133
title: Dataset: The aftermath of a trophic cascade: Increased anoxia following invasive species introduction of a eutrophic lake

description: This repository includes the setup and output from the analysis ran on Lake Mendota to explore the trophic cascade caused by invasion of spiny water flea in 2010. Scripts to run the model are located under /src, and the processed results for the discussion of the paper are located under /data_processed.
DOI: 10.6084/m9.figshare.19248635.v2
title: The social isolation enforced by the COVID-19 pandemic reduces the Health-Related Quality of Life score in the adult population of Metropolitan Lima

description: Data on the health status of Lima residents
DOI: 10.1285/i22390359v42p123
title: Framing argument for specialised knowledge: interactional metadiscourse markers in economics and law research articles

description: This chapter examines the patterns of interactional metadiscourse use in the disciplines of Economics and Law, and draws upon Hyland's (2005a) analytical framework of metadiscourse markers along with other integrative frameworks in an approximately 160,000-word corpus of social science empirical research articles in these fields. Both distributional and functional analyses of metadiscourse resources show that there are similarities as well as differences between the two disciplines in terms of how writers structure their argumentative texts for their readers, and how they draw on their understandings of these resources to report the results of their original study to their readers. It is argued that metadiscursive use may be accounted for by the epistemologies behind the existing qualitative and quantitative methods of empirical research alongside a range of experiential, social and identity-shaping variables of the writers involved in this kind of argumentative genre. By contributing additional evidence to current published research, this study aims to provide a greater understanding of metadiscourse in the argumentative writing practices of the research article.
DOI: 10.21227/8rpg-qt98
title: NSL-KDD

description: The train set and test set of NSL-KDD
DOI: 10.23721/100/1504338
title: The BoT-IoT Dataset

description: The dataset has captured pcap files that are 69.3 GB in size, with more than 72.000.000 records. The extracted flow traffic, in csv format is 16.7 GB in size. The dataset includes DDoS, DoS, OS and Service Scan, Keylogging and Data exfiltration attacks, with the DDoS and DoS attacks further organized, based on the protocol used.
DOI: 10.25326/299
title: GOES movie animations for ATR flights during EUREC4A 

description: Satellite animations are made to visualize the path followed by the ATR aircraft for each of its 19 flights during the EUREC4A field campaign, as well as dropsondes and radiosondes that have been launched nearby. Geostationary images shown here are from the GOES-16 visible channel in daytime (channel 2) and infrared channel in nighttime (channel 13) and are retrieved at 1mn time increment and 500m resolution in daytime, 2km resolution in nighttime. The code developed can also be used to generate movies with any combination of platforms making measurements in the region captured by the mesoscans (50.1323W:61.3010W, 8.2457W:17.9343N), and can include the trajectory of radiosondes and dropsondes launched. Here, movies are restricted to the ATR track and HALO circle in the domain 59.85W:56.1W, 11.9N:14.7N.
DOI: 10.25326/304
title: Calibrated stable water vapour isotope measurements on board RV Atalante during EUREC4A-OA

description: Water vapour isotope data measured on board French RV Atalante during EUREC4A-OA cruise. It includes VSMOW-SLAP normalisation, water vapour mixing ratio correction, procedure follows Benetti et al., 2014. Data gaps are possible due to calibration or instrument failure. Rainfall events are flagged.
DOI: 10.25326/305
title: Calibrated stable water isotope in precipitation from the RV Atalante during EUREC4A-OA

description: Water isotope in precipitation collected on board French RV Atalante during EUREC4A-OA cruise. It includes VSMOW-SLAP normalisation and rain rate derived from the weight of the collected rain amount. Rain collectors placed on railing in front of deck under bridge provided by ETH (Zürich, Switzerland)
DOI: 10.5281/zenodo.4472402
title: Sample Records. A systematic review in Circular Economy and Bioenergy addressed by Education and Communication

description: Sample Records. A systematic review in Circular Economy and Bioenergy addressed by Education and Communication
DOI: 10.5281/zenodo.5075996
title: MED-GOLD Indicators for the Wine pilot service over Iberian Peninsula from ERA5 Reanalysis 1979-2020

description: Indicators of interest for the Wine sector over the Iberian Peninsula using ERA5 Reanalysis data for the period 1979 to 2020: Growing Season Temperature (GST) [Temp averaged between April and October] Spring Rain (SprR) [ Precip cumulated between 21 apr and 21 Jun], Harvest Rain (HarvestR) [ Precip cumulated between 21 aug and 21 Oct] (SU35) -number of days with temperature higher than 35°C [for April ot October], Warm Spell Duration Index (WSDI) [days with at least 6 consecutive days when the daily temperature maximum exceeds its 90th percentile for April to Oct] Wine risk indicators, implemented specifically for the MED-GOLD Wine pilot service by ENEA and SOGRAPE VINHOS S.A.: Sanitary Risk Index= offgts*offsp*(percentile(SprR))+offhart*percentile(HarvestR)+(100-percentile(GST); with offhart=1.; offsp=1; offgts=1.; if percentile (SprR)&gt;= 60; offsp=1.5; if percentile (GST)&lt;= 40; offhart=1.5; if percentile (GST)&gt;=70; offgts=1.5; where the percentile are here computed starting from the distribution over the 1993-2106 hindcast period to be consistent and comparable with the seasonal forecast Heat Risk Index = percentile(GST)+percentile(SU35_AMJJASO) + percentile(WSDI_AMJJASO),where the percentile are here computed srarting from the distribution over the 1993-2106 hindcast period to be consistent and comparable with the seasonal forecast Datasets computed by ENEA, in collaboration with SOGRAPE VINHOS S.A. in the framework of the European MED-GOLD project, funded from the European Union's Horizon 2020 Research and Innovation programme under Grant agreement No. 776467
description: Each File is in netcdf format (.nc)
DOI: 10.5445/ir/1000152962
title: Data supplement for the publication: A multi-disciplinary analysis of the exceptional flood event of July 2021 in central Europe. Part 1: Event description and analysis

description: Supplementary data for the publication: Mohr, S., Ehret, U., Kunz, M., Ludwig, P., Caldas-Alvarez, A., Daniell, J. E., Ehmele, F., Feldmann, H., Franca, M. J., Gattke, C., Hundhausen, M., Knippertz, P., Küpfer, K., Mühr, B., Pinto, J. G., Quinting, J., Schäfer, A. M., Scheibel, M., Seidel, F., and Wisotzky, C.: A multi-disciplinary analysis of the exceptional flood event of July 2021 in central Europe. Part 1: Event description and analysis, Nat. Hazards Earth Syst. Sci. Discuss. doi:10.5194/nhess-2022-137. 1) Inundation_Area_July2022_RiverAhr (Shape-Format) The dataset includes the inundation areas along the river Ahr, which were derived using available data (Sentinel-1, Sentinel-2) and pictures/video sequences from media and social media channels in near-real-time (within a few days after the flood). 2) BackwardTrajectories_baseonERA5_LAGRANTO (txt-Format) The dataset includes the pathway of air masses in the form of 10-day kinematic backward trajectories using the Lagrangian Analysis Tool (LAGRANTO; Sprenger and Wernli, 2015) based on ERA5 with different start dates. Further details can be found in the corresponding publication.
DOI: 10.6084/m9.figshare.13102361
title: Supplementary material from "Vegetation diversity of screes and taluses of the Pamir and south-western Tian Shan in Middle Asia"

description: This data contains the analytic table including type relevés (Tab. S1), the synoptic table (Tab. S2) and supplementary figures (Fig. S1-S2) of the scree vegetation in the Middle Asia.
DOI: 10.6084/m9.figshare.19643265.v1
title: Chesapeake Bay Phragmites management outcomes

description: The results of a five-year experiment across eight subestuaries of Chesapeake Bay, representing a gradient of watersheds with differing dominant land-use types and anthropogenic impacts, to evaluate invasive and native plant response to herbicide management. The focal invader was <em>Phragmites australis </em>(common reed), one of the most aggressive and pervasive invasive plants in North American wetlands.
DOI: 10.6084/m9.figshare.20847253
title: Efficiency of Potassium Fertilizer on Tea Yield and Quality

description: This is a dataset for tea yield and quality that with or without potassium fertilizer application
DOI: 10.6084/m9.figshare.21347844.v1
title: IBSP barrier island vegetation 1959 to 2020

description: Data from Charbonneau et al. 2022 "The persistence and conversion of coastal foredune and swale vegetation community distributions 63 years later" in Ecosphere. <br> Please reach out to Bianca Charbonneau with questions. Methods for collection explained in aforementioned publication. Dr. Charbonneau physically mapped all data. <br> FILES: <br> the 8 shaefiles need all 6 files associated with each downloaded to open <br> 1) thicketboundary2020- polyline shapefile - low and high thicket community boundary (referred to as thicket boundary) following Martin (1959). <br> 2) thicket_woody_overlap - overlap in woody habitat between 1959 and 2020 - no conversion - polygon shapefile <br> 3) veg2020_onlysouth_poly - polygon shapefile of vegetation mapped in the south mapped area in june 2020 <br> 4) woody2020- polygon shapefile, woody habitat in 2020 <br> 5)woody1959- polgyon shapefile, woody habitat in 2020 <br> 6) BeachHeather2020 - polygon shapefile, beachheather in monocultuer or mixed assemblage in june 2020 <br> 7) woodyIndividuals2020 - woody species east of thicket boundary in secondary dune or foredune as points shapefile <br> 8) martin1959 - digitized martin map from Martin, 1959, polygon shapefile <br> 9) woody distances excel - woody species east of thicket boundary distance to ocean and crest, and elevation <br>
DOI: 10.6084/m9.figshare.21799604
title: Data of Evelien Van Hoeymissen for manuscript "Gain-of-function variants in the ion channel gene TRPM3 underlie a spectrum of neurodevelopmental disorders"

description: Raw data for figures generated by Evelien Van Hoeymissen Data of Prof Tibor Rohacs included
DOI: 10.5281/zenodo.5590053
title: PARASO ERA5 forcings

description: External forcings from ERA5, used for forcing the PARASO configuration. Due to their size, only three months are provided here (from 01-JAN-2000 to 31-MAR-2000), for testing purposes. The data should be uncompressed with the uncpr_paraso.sh script. <strong>Model description: </strong>Pelletier, C., Fichefet, T., Goosse, H., Haubner, K., Helsen, S., Huot, P.-V., Kittel, C., Klein, F., Le clec'h, S., van Lipzig, N. P. M., Marchi, S., Massonnet, F., Mathiot, P., Moravveji, E., Moreno-Chamarro, E., Ortega, P., Pattyn, F., Souverijns, N., Van Achter, G., Vanden Broucke, S., Vanhulle, A., Verfaillie, D., and Zipf, L.: PARASO, a circum-Antarctic fully coupled ice-sheet–ocean–sea-ice–atmosphere–land model involving f.ETISh1.7, NEMO3.6, LIM3.6, COSMO5.0 and CLM4.5, Geosci. Model Dev., 15, 553–594, 10.5194/gmd-15-553-2022, 2022. <strong>Source code (no COSMO)</strong>: Pelletier, Charles, Klein, François, Zipf, Lars, Haubner, Konstanze, Mathiot, Pierre, Pattyn, Frank, Moravveji, Ehsan, & Vanden Broucke, Sam. (2021). PARASO source code (no COSMO) (v1.4.3). Zenodo. 10.5281/zenodo.5576201 <strong>Other input data: </strong>Pelletier, Charles, Klein, François, Zipf, Lars, Vanden Broucke, Sam, Haubner, Konstanze, & Helsen, Samuel. (2021). Input data for PARASO, a circum-Antarctic fully-coupled 5-component model (v1.4.3) [Data set]. Zenodo. 10.5281/zenodo.5588468 The ERA5 data (Hersbach, 2018) was downloaded on 01-SEP-2019 from the Copernicus Climate Change Service (C3S) Climate Data Store. The results contain modified Copernicus Climate Change Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. <strong>Reference</strong>: Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1979 to present, https://doi.org/10.24381/cds.adbb2d47, downloaded from the Copernicus Climate Change Service (C3S); Climate Data Store (CDS) on 01-SEP-2019, 2018.
description: Developed within the framework of the PARAMOUR project, Decadal predictability and variability of polar climate: the role of atmosphere-ocean-cryosphere multiscale interactions. Fonds de la Recherche Scientifique–FNRS Grant number O0100718F (EOS ID 30454083).
DOI: 10.5281/zenodo.7448588
title: Clusters of interactions common between the Parkinson's disease map and the Ageing map

description: This set of files was generated using the script demonstrating the use of MINERVA Net repository. The script is available under: https://gitlab.lcsb.uni.lu/minerva/api-scripts/-/blob/master/R/API-minervanet.R The diagrams should be opened with the CellDesigner software (https://www.celldesigner.org/).
DOI: 10.5880/sfb806.15
title: LGM paleoenvironment of Europe - Map

description: The here documented GIS map and dataset contains a collection of Last Glacial Maximum (LGM, ~21k yBP) paleoenvironmental data. It is the first result of a project that aims to acquire, produce and publish GIS datasets from non-GIS based sources such as analogous maps, textual informations or figures of scientific publications for prehistoric time slices. In combination with modelling results and already available GIS-datasets related to the mentioned time frame, it should enable other researchers and members from other projects to use the maps in their work, properly cited and referenced. The map shows LGM land ice sheets, paleo-stream networks and inland water in Europe, a sea-level adapted (-120m) land mass and a Köppen-Geiger climate classification derived from climate model data.