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12,237 results for “2019”
Plant Atlas 2020 — British and Irish vascular plant and charophyte 10 x 10 km distribution trends for 1930–2019 (long-term) and 1987–2019 (short-term), including country-level breakdowns
<p>Plant Atlas 2020 is the most comprehensive survey of plants (flowering plants, ferns and charophytes) ever undertaken in Britain and Ireland. It is based on over 30 million records, collected mainly by volunteer recorders of the Botanical Society of Britain and Ireland (BSBI) between 2000 and 2019, as well as previous nationwide surveys undertaken in the 1950s and 1990s. This resource provides the data for the long- (1930–2019) and short- term (1987–2019) 10 x 10 km (“hectad”) distribution trends, presented in both the <em>Plant Atlas 2020</em> book (Stroh et al., 2023) and website (www.plantatlas2020.org).</p>
2001_2019_ERA5_ TotalPrecipitation_FourierProcessed_ER
<p> </p> <p><strong>Abstract:</strong></p> <p>Monthly Precipitation form the ERA5 reanalysis archive supplied by the European Centre ofr Medium Range Weather Forecasting for 2001 - 2019. The original data is at 0.25 degree resolution and was downscaled by ERA extraction algroithms to 5km.</p> <p> </p> <p>This is a set of images produced by Temporal Fourier Analysis (TFA) of ERA5 data:</p> <p>ERA5: Total Precipitation </p> <p>The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for whole world<br>This series of ERA5 data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2001 to 2019. </p> <p> </p> <p>Precipitation from the ERA5 reanalysis archive supplied by the European Centre ofr Medium Range Weather Forecasting for 2001 - 2019. Abstract: Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium Range Weather Forecasting . for 2001 - 2019 . The original data is at 0.25 degree resolution and wasdownscaled by ERA extraction algroithms. The daily data have been aggregated to dekadal, monthly, and annual datasets to match the outputs produced by NASA from the MODIS imagery temperature and vegetation Index datasets. The resolution was also chosen to match these MODIS datasets.</p> <h4>Process:</h4> <p>Image values were extracted from ERA5 ( Total precipitation) 5 km imagery from 2001 to 2019. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (<a href="https://doi.org/10.1371/journal.pone.0001408">https://doi.org/10.1371/journal.pone.0001408</a>) <br>Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic (WGS84). The E4Warning study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.</p> <p> </p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <h4>File names:</h4> <p><br>The wd at the start of each file name indicates that the image covers Globally and ER refers to Europe, North Africa, Eurasia in the E4warning and is in geographic projection. 19 refers to the year timeline of 2001-2019.<br><br>The next two characters identify the channel:<br>20 - Monthly Total Precipitation<br><br><br>The last two characters of each file name denote the output from Fourier processing:<br>a0 - mean<br>mn - minimum<br>mx - maximum<br>a1 - amplitude of annual cycle<br>a2 - amplitude of bi-annual cycle<br>a3 - amplitude of tri-annual cycle<br>p1 - phase of annual cycle<br>p2 - phase of bi-annual cycle<br>p3 - phase of tri-annual cycle<br>d1 - variance in annual cycle<br>d2 - variance in bi-annual cycle<br>d3 - variance in tri-annual cycle<br>da - combined variance in annual, bi-annual, and tri-annual cycles<br>vr - variance in raw data<br><br>Parameter Fourier Variable Image values are<br>ERA5 A0, A1, A2, A3, Min, Max, Vr Reflectance values monthly total precipitation in mm<br>ALL D1,D2,D3,Da Percentages<br>ALL E1,E2,E3 Percentages<br>ALL P1,P2.P3 Months*100. (Jan=100)</p>
Datasets for paper 'Cabello, V., Renner, A., Giampietro, M. 2019. Relational analysis of the resource nexus in arid land crop production. Advances in Water Resources 130:258-629'
<p>Datasets produced for the paper Cabello, V., Renner, A., Giampietro, M. 2019.<em> </em>Relational analysis of the resource nexus in arid land crop production. <em>Advances in Water Resources </em>130:258-269</p>
Data related to the article: Y. Norman et al., Science 365, eaax1030 (2019)
<p>This data set contains intracranial EEG data, analysis code and results associated with the manuscript, "<strong>Hippocampal Sharp-wave Ripples Linked to Visual Episodic Recollection in Humans</strong>". [DOI: 10.1126/science.aax1030]</p> <p>Data files (.mat) and associated scripts (.m) are divided into folders according to the subject of the analysis (e.g. ripple detection, ripple-triggered averages, multivariate pattern analysis etc.) and are all contained in the .zip file: “Norman_et_al_2019_data_and_code_zenodo.zip".</p> <p>The code is written in Matlab R2018b and run on a desktop computer with a 3.4Ghz Intel Core i7-6700 CPU with 64GB RAM.</p> <p>Matlab's Signal Processing Toolbox is required.</p> <p><strong>General notes:</strong></p> <p>1) The data does not contain identifying details about the patients, nor voice recordings.</p> <p>2) Before running the analyses, make sure you set the correct paths in the "startup_script.m" located in the main folder where the zip file was extracted.</p> <p>3) To run the code, the following open-source toolboxes are required:</p> <ul> <li><strong>EEGLAB</strong> (<a href="https://sccn.ucsd.edu/eeglab/download.php">https://sccn.ucsd.edu/eeglab/download.php</a>), version: "eeglab14_1_2b". <ul> <li>A. Delorme, S. Makeig, EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. <em>J. Neurosci. Methods</em>. <strong>134</strong>, 9–21 (2004).</li> </ul> </li> <li><strong>Mass Univariate ERP Toolbox</strong> (<a href="https://openwetware.org/wiki/Mass_Univariate_ERP_Toolbox">https://openwetware.org/wiki/Mass_Univariate_ERP_Toolbox</a>), version: "dmgroppe-Mass_Univariate_ERP_Toolbox-d1e60d4". <ul> <li>D. M. Groppe, T. P. Urbach, M. Kutas, Mass univariate analysis of event-related brain potentials/fields I: A critical tutorial review. <em>Psychophysiology</em>. <strong>48</strong>, 1711–1725 (2011).</li> </ul> </li> </ul> <p>*** Make sure you download the relevant toolboxes and save them in the "path_to_toolboxes" before running the analysis scripts (see "startup_script.m")</p> <p>4) Code developed by other authors (redistributed here as part of the analysis code):</p> <ul> <li>DRtoolbox (https://lvdmaaten.github.io/drtoolbox/), version: 0.8.1b. <ul> <li>L.J.P. van der Maaten, E.O. Postma, and H.J. van den Herik. <strong>Dimensionality Reduction: A Comparative Review</strong>. Tilburg University Technical Report, TiCC-TR 2009-005, 2009.</li> </ul> </li> <li>Scott Lowe / superbar (<a href="https://github.com/scottclowe/superbar">https://github.com/scottclowe/superbar</a>), version: 1.5.0.</li> <li>Oliver J. Woodford, Yair M. Altman / export_fig (<a href="https://github.com/altmany/export_fig">https://github.com/altmany/export_fig</a>).</li> </ul>
MetFrag Local CSV: CompTox (7 March 2019 release) Smoking MetaData File
<p>This is the CSV file that can be used as a local database in MetFrag (https://msbi.ipb-halle.de/MetFrag/), for those who wish to integrate this into the command line version.</p> <p>Note that this file is TOO LARGE to be uploaded via the web interface, this is already integrated in the web interface.</p> <p>This file is based off the "SelectMetaData" CompTox MetFrag file from the 7 March 2019 release, available from:</p> <p><a>ftp://newftp.epa.gov/COMPTOX/Sustainable_Chemistry_Data/Chemistry_Dashboard/MetFrag_metadata_files</a></p> <p>The Smoking MetaData file contains the following fields, in addition to the regular (basic) CompTox data fields:</p> <p>- PubMedNeuro: the <a href="https://comptox.epa.gov/dashboard/chemical_lists/LITMINEDNEURO">LITMINEDNEURO</a> list with total PubMed reference counts in the column</p> <p>- <a href="https://comptox.epa.gov/dashboard/chemical_lists/CIGARETTES">CIGARETTES</a>, <a href="https://comptox.epa.gov/dashboard/chemical_lists/INDOORCT16">INDOORCT16</a>, <a href="https://comptox.epa.gov/dashboard/chemical_lists/SRM2585DUST">SRM2585DUST</a>, <a href="https://comptox.epa.gov/dashboard/chemical_lists/SLTCHEMDB">SLTCHEMDB</a>, <a href="https://comptox.epa.gov/dashboard/chemical_lists/THSMOKE">THSMOKE</a> as suspect lists.</p> <p>First release (July 2019, not archived) contained a smaller subset of the SRM2585DUST list.</p>
Data for manuscript Marmet, Studer, Lemoine, Grazioli, Bertholet & Gmel (2019). Reconsidering the associations between self-reported alcohol use disorder and mental health problems in the light of co-occurring addictions in young Swiss men. Plos One. DOI: 10.1371/journal.pone.0222806.
<p>Dataset for the manuscript Marmet, Studer, Lemoine, Grazioli, Bertholet & Gmel (2019). Reconsidering the associations between self-reported alcohol use disorder and mental health problems in the light of co-occurring addictions in young Swiss men. Plos One. DOI: 10.1371/journal.pone.0222806.</p> <p>The dataset contains all data needed to reproduce the results in the above cited manuscript. Variable description and labels can be found in the codebook. For further information on the instruments used please refer to the manuscript.</p> <p>The data was collected between April 2016 and March 2018 in Switzerland by the C-SURF study (<a href="http://www.c-surf.ch">www.c-surf.ch</a>). Participants were on average 25 years old when they answered the questionnaires. The final sample size used in the manuscript is 5516. Please note that the dataset contains 25 datasets created with multiple imputation, therefore there are no missing values in the dataset.</p> <p>The research protocol for this study was approved by the Human Research Ethics Committee of the Canton Vaud (Protocol No. 15/07). Data collection was funded by the Swiss National Science Foundation (FN 33CSC0-122679, FN 33CS30_139467, FN 33CS30_148493)</p>
MetFrag Local CSV: CompTox (7 March 2019 release) Wastewater MetaData File
<p>This is the CSV file that can be used as a local database in MetFrag (<a href="https://msbi.ipb-halle.de/MetFrag/">https://msbi.ipb-halle.de/MetFrag/</a>), for those who wish to integrate this into the command line version.</p> <p>Note that this file is TOO LARGE to be uploaded via the web interface, this is already integrated in the web interface.</p> <p>This file is based off the "SelectMetaData" CompTox MetFrag file from the 7 March 2019 release, available from:</p> <p>ftp://newftp.epa.gov/COMPTOX/Sustainable_Chemistry_Data/Chemistry_Dashboard/MetFrag_metadata_files</p> <p>The Wastewater MetaData file contains the following fields, in addition to the regular (basic) CompTox data fields:</p> <p>Suspect Lists (1=presence, 0=absence):</p> <p>- ITNANTIBIOTIC, STOFFIDENT, REACH2017, ZINC15PHARMA and PFASMASTER</p> <p>Suspect Lists with scores from KEMI (see details on <a href="https://www.norman-network.com/nds/SLE/">NORMAN-SLE</a> and hyperlinks below):</p> <p>- <a href="https://zenodo.org/record/2628787">KEMIMARKET_EXPO</a>, <a href="https://zenodo.org/record/2628787">KEMIMARKET_HAZ</a>, <a href="https://zenodo.org/record/2653567">KEMIWW_WDUIndex</a>, <a href="https://zenodo.org/record/2653567">KEMIWW_StpSE</a>, <a href="https://zenodo.org/record/2653567">KEMIWW_SEHitsOverDL</a></p>
ERT Datasets for the paper of Nickschick et al. (2019) in Solid Earth
<p>Underlying data for the paper</p> <p>Nickschick, T., Flechsig, C., Mrlina, J., Oppermann, F., Löbig, F. & Günther, T. (2019): Large-scale electrical resistivity tomography in the Cheb Basin (Eger Rift) at an ICDP monitoring drill site to image fluid-related structures. Solid Earth. https://doi.org/10.5194/se-2019-38.</p> <p>The paper contains two types of data:</p> <p>DC resistivity (geoelectrics) ERT data in four profiles<br> -------------------------------------------------------<br> P1, P2 and P3 represent classical multi-electrode ERT data with a unit electrode spacing of a=5m using the Wenner array. They are a representative selection of in total 8 profiles measured in the frame of the (German) MSc work of F. Loebig (there called P2, P5 and P7) where also the lithological section (Fig. 2) was developed:<br> 1. P1 between Lesinka and Hnevin, 620m long<br> 2. P2 around the Hartousov mofette, 700m long<br> 3. P3 between Hartousov and Kacerov, 700m long</p> <p>The inversion results are shown in Fig. 6a,b,c.</p> <p>The large-scale dataset represents data from a dipole-dipole experiment that is in detail described in the paper (Figs. 3-5) with the inversion result given in Fig. 7a along with borehole data.<br> For location see Fig. 1.</p> <p>For all four profiles we provide<br> - measured data with electrode positions on top and the electrode array (abmn) along with the resistance below, topography at the bottom<br> - the configuration file for the inversion software BERT (see https://gitlab.com/resistivity-net/bert), we used version 2.2.9 from January 2019<br> - kml/gpx files denoting the positions of the electrode chains (P1-P3) or an Excel file containing the positions in UTM33N</p> <p>Note that for P2 and the large-scale profile the topography needs to be taken into account whereas it is not necessary <br> Users should be able to reproduce the results by calling<br> bert cfgfile all show</p> <p>Gravity data<br> ------------<br> The data represent a two-column file:<br> 1. position along the ERT profile (projected) in metres<br> 2. Bouguer anomaly in mGal</p> <p>See also special README file in the gravity folder.</p>
Air temperature measurements from Automatic Weather Station (AWS) at Freiburg – Chemiehochhaus (FRCHEM) from 2019-01-01 to 2019-12-31 [L2]
<p>Quality controlled and gap-filled continuous air temperature data from the urban rooftop weather station at Freiburg-Chemiehochhaus (FRCHEM, 7.8486ºE, 48.0011ºN, 323.5 m) using an actively ventillated and shielded psychrometer operated 2m above roof level.</p> <ul> <li>Quality controlled air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2019.</li> <li>Average, minimum and maximum air temperatures are provided on hourly, daily, monthly and annual scales.</li> <li>Characteristic hours and days are reported on daily, monthly and annual scales (e.g. summer days with T_max > 25ºC, hot days with T_max > 30º, desert days with T_max > 35ºC, tropical nights with T_min > 20°, frost days with T_min < 0ºC and ice days with T_max < 0ºC, all based on 00:00 - 24:00 UTC).</li> <li>Detailed information on gap-filled data is provided.</li> <li>Note: All times are provided in UTC, not local time.</li> </ul> <p>For more details read `FRCHEM_2019_AirTemperature_MetaData.txt`.</p> <p>Version 1.1.0 contains additionally air temperature data aggregated at 10min and 30min.</p>
Air temperature measurements from Automatic Weather Station (AWS) at Freiburg – Werthmannstrasse (FRWRTM) from 2019-01-01 to 2019-12-31 [L2]
<p>Quality controlled and gap-filled continuous air temperature data from the urban weather station at Freiburg-Werthmannstrasse (FRWRTM, 7.8447ºE, 47.9928, 277 m) using a passively ventilated and shielded temperature and humidity probe (Campbell Scientific Inc., CS 215) operated in a Stevenson Screen 2m above ground level in the vegetated backyard of Werthmannstrasse 10.</p> <ul> <li>Quality controlled in-canopy air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2019.</li> <li>Average, minimum and maximum in-canopy air temperatures are provided on hourly, daily, monthly and annual scales.</li> <li>Characteristic hours and days are reported on daily, monthly and annual scales (e.g. summer days with T_max > 25ºC, hot days with T_max > 30º, desert days with T_max > 35ºC, tropical nights with T_min > 20°, frost days with T_min < 0ºC and ice days with T_max < 0ºC, all based on 00:00 - 24:00 UTC).</li> <li>Detailed information on gap-filled data is provided.</li> <li>Note: All times are provided in UTC, not local time.</li> </ul> <p>For more details read `FRWRTM_2019_AirTemperature_MetaData.txt`.</p> <p>Version 1.1.0 contains additionally air temperature data aggregated at 10min and 30min.</p>
SolarSMART Engineering Perceptions 2019 Study
<p>SolarSMART Engineering Perceptions 2019 Study dataset provides results from a perception analysis with 42 Engineering students from an advanced Energy Technologies course at the University of Georgia regarding their perceptions of consumer adoption behaviors of multiple clean and renewable energy technologies. The dataset includes the following: demographic information for each participant, draw-a-map responses for their perceptions of where consumers adopt and do not adopt renewable and clean energy technologies (e.g., bioenergy, geothermal, solar, wave, and wind). Respondents were also asked questions about their intended plans after graduation, as well as what coursework they took as part of their studies that was not STEM in nature. </p>
Japanese "Abe" Tweets from 2019-02-10 to 2020-10-07 (15,407,811 tweets and 114,231,250 retweets)
<p><strong>Abstract</strong> (our paper)</p> <p>To examine conservative–liberal differences in the extent to which partisan tweets reach less partisan moderate users in a nonwestern context, we analyzed a network of retweets about former Japanese Prime Minister Shinzo Abe. The analyses consistently demonstrated that partisan tweets originating from the conservative cluster reach a wider range of moderate users than those from the liberal cluster. Network analyses revealed that while the conservative and the liberal clusters’ internal structures were similar, the conservative cluster reciprocated the follows from moderate accounts at a higher rate than the liberal cluster. In addition, moderate accounts reciprocated the conservative cluster’s following at a higher rate than they did for the liberal cluster. The analysis of tweet content showed no difference in the frequency of hashtag use between conservatives and liberals, but there were differences in the use of emotion words and linguistic expressions. In particular, emotion words related to the propagation of messages, such as those expressing “dislike”, were used more frequently by conservatives, while the use of adjectives by conservatives was closer to that of moderate users, indicating that conservative tweets are more palatable for moderate users than liberal tweets.</p> <p><strong>Data</strong></p> <p>Abe.tsv.gz:<br> The first column is the tweet id, the second column is the tweet id of the retweet source, and the third column is the date and time (JST) when the tweet was posted.<br> This data was collected by giving the query "安倍 OR アベ" to the Twitter Search API. Therefore, most of the tweets are Japanese tweets. The second column is empty if the tweet is not a retweet.</p> <p><strong>Publication</strong></p> <p>This data set was created for our study. If you make use of this data set, please cite:<br> Mitsuo Yoshida, Takeshi Sakaki, Tetsuro Kobayashi, Fujio Toriumi. Japanese conservative messages propagate to moderate users better than their liberal counterparts on Twitter. <em>Scientific Reports</em>. vol.11, article no.19224, 2021.<br> <a href="https://doi.org/10.1038/s41598-021-98349-2">https://doi.org/10.1038/s41598-021-98349-2</a></p>
IPCC Climate Zones (from the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories)
<p><strong>Description</strong></p> <p>These data (re)create spatial data for the 2019 IPCC Climate Zones, shown in <em>Figure 3A.5.1</em> of <a href="https://www.ipcc-nggip.iges.or.jp/public/2019rf/pdf/4_Volume4/19R_V4_Ch03_Land%20Representation.pdf">Chapter 3: Consistent Representation of Lands</a> in <a href="https://www.ipcc-nggip.iges.or.jp/public/2019rf/vol4.html">Volume 4: Agriculture, Forestry and Other Land Use</a> of the <a href="https://www.ipcc-nggip.iges.or.jp/public/2019rf/index.html">2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories</a>. I recreated these data because I could not readily identify the data in a spatial format online, a problem which has previously been noted by ESDAC, who produced a <a href="https://esdac.jrc.ec.europa.eu/content/support-renewable-energy-directive#tabs-0-description=1">spatial version of <em>Figure 3A.5.1</em> from the original 2006 guidelines</a>.</p> <p>Resolution: 0.5 arc degree</p> <p>CRS: lon/lat WGS 84</p> <p><strong>If you use these data please ensure you also cite the IPCC</strong> - Calvo Buendia, E et al. (2019). 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. IPCC, Switzerland.</p> <p> </p> <p><strong>Methods</strong></p> <p>The data were derived using the classification scheme shown in <em>Figure 3A.5.2</em> based on the gridded Climate Research Unit (CRU) Time Series (TS) monthly climate data (<a href="https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.3711">Harris et al., 2014</a>) for the period from 1985 to 2015 following the methods described in <em>Annex 3A.5 Default climate and soil classifications </em>of the above Chapter. All data were processed in <em>R</em> version 4.2.1, with the packages <a href="https://cran.r-project.org/web/packages/elevatr/index.html"><em>elevatr</em></a> (v0.4.2), <a href="https://cran.r-project.org/web/packages/lubridate/index.html"><em>lubridate</em></a> (v1.8.0), <a href="https://cran.r-project.org/web/packages/magrittr/index.html"><em>magrittr</em></a> (v2.0.3), and <a href="https://cran.r-project.org/web/packages/terra/index.html"><em>terra</em></a> (v1.6-7)<em> </em>attached. The full session info is included as a <em>.txt</em> file. As these methods are not exhaustively described in the Annex, the following assumptions were made:</p> <ul> <li><a href="http://http://dx.doi.org/10.5285/c311c7948e8a47b299f8f9c7ae6cb9af">CRU TS3.25</a> was used as the most recently published data (published on 2017-09-22) that could have been incorporated into the Refinement. Other possibilities include CRU TS3.24 (which are the first data to include 2015), or CRU TS4.00 or CRU TS4.01 (both of which were published in parallel to 3.24 and 3.25). These data were all investigated, and CRU TS3.25 produced results that were the most visually similar to the published <em>Figure 3A.5.1</em> (though non-identical).</li> <li>As the methods did not mention a preferred elevation data source, the <a href="https://cran.r-project.org/web/packages/elevatr/index.html"><em>elevatr</em></a> R package was used to obtain data at zoom level 2 (approx resolution of 0.15 arc degree), that was then resampled to match the 0.5-degree resolution of the CRU data. These data originally come from the <a href="https://www.ngdc.noaa.gov/mgg/global/global.html">ETOPO1 global relief model</a>.</li> </ul> <p> </p> <p><strong>Known discrepancies</strong></p> <ul> <li>The distribution of Tropical Wet and Tropical Moist in South America does not exactly match the original data.</li> <li>There are small discrepancies in Tropical Montane classifications (likely arising from the use of a different elevation layer). These are most noticeable in, but not restricted to, Africa.</li> <li>The classification of Boreal Dry, Polar Dry, and Polar Moist in northern Russia and (to a lesser extent) in northern Canada does not exactly match the original data.</li> <li>There are a small number of Cool Temperate Dry pixels in the UK, and Warm Temperate Dry pixels around Brittany which do not occur in the original data.</li> </ul> <p> </p> <p><strong>Disclaimer</strong></p> <p><strong>I am not affiliated with the IPCC in any way</strong>, I just needed spatial data of the Climate Zones, and could not readily identify any online. This is a problem which has previously been noted by ESDAC, who produced a <a href="https://esdac.jrc.ec.europa.eu/content/support-renewable-energy-directive#tabs-0-description=1">spatial version of <em>Figure 3A.5.1</em> from the original 2006 guidelines</a>.</p> <p> </p> <p><strong>File description</strong></p> <ul> <li><em>README.html</em> - ~this description file.</li> <li><em>IPCC_Climate_Zones_ts_3.25.tif</em> - the output Climate Zones map at 0.5-arc degree resolution based on the CRU TS3.25 data.</li> <li><em>IPCC_Climate_Zones_colour_map.clr </em>- a colour map file to render the output map with the same colours as in the IPCC 2019 Refinement figure.</li> <li><em>IPCC_Climate_Zones_ts_3.25.png</em> - an image file of the output Climate Zones map.</li> <li><em>ipcc_climate_zones_2019.R</em> - the script used to produce these data.</li> <li><em>session_info.txt</em> - the R session info.</li> </ul>
Data for Marine Ecological Niche Models, for 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species
<p>Data for Ecological Niche Models: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species.</p>
Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European Marine Species, developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Native ecological niche models of 1508 European species (894 fish and 614 non fish) developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 and under RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, at 0.5° spatial resolution.</p>
PM2.5, PM10, NO2, O3 from Copernicus Air Quality Forecast March-June 2019, 2020 and 2021
<p>PM2.5, PM10, NO2, O3 Copernicus Air Quality Forecasts March-June 2019, 2020 and 2021 retrieved from the ADAM platform data cube (http://reliance.adamplatform.eu). Datasets are monthly averaged.</p> <p>The resulting extracted datasets are stored in netCDF format and cover Europe.</p>
Water stable isotope, temperature and electrical conductivity dataset (snow, ice, rain, surface water, groundwater) from a high alpine catchment (2019-2021).
<p>Data collected in the Otemma forefield in Switzerland (45°56’03”N,7°24’42”) from July 2019 to October 2021.<br> Data were collected by the research teams of Bettina Schaefli<sup>2</sup> and Stuart N. Lane<sup>1</sup>.</p> <p><sup>1</sup> Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland</p> <p><sup>2</sup> Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland</p> <p>For further information, please contact:</p> <ul> <li>tom.muller.1@unil.ch</li> </ul> <p><strong>Description of the dataset</strong></p> <p>This dataset contains water stable isotope (δ<sup>2</sup>H, δ<sup>17</sup>O, δ<sup>18</sup>O), water temperature and water electrical conductivity (EC) measurements collected from the Otemma glacier catchment.</p> <p>All water isotope samples were collected directly from the source and stored in 12 mL amber glass vials with an air-tight caps. River samples were first collected with an automatic ISCO 6712 portable water sampler with 1L open plastic bottles and transferred in 12 mL vials every one to two weeks. All isotope analysis were performed using a Wavelength-Scanned Cavity Ring Down Spectrometer (Picarro 2140-I, Santa Clara, California, USA) and expressed relative to the international Vienna Standard Mean Ocean Water (VSMOW) standards.</p> <p>All EC and water temperature measurements were performed with a WTW Multi 3510 IDS logger with a IDS TetraCon® 925 probe.</p> <p>The dataset contains measurements performed at various locations within the catchment. A total of approximately 1500 measurements are provided. In the dataset each point correspond to a measurement station (column "<strong>Station</strong>") which we classified in specific class of water (column "<strong>Type</strong>") as follows :</p> <ul> <li><strong>Stream </strong>: samples collected at three locations, from the glacier snout, after a small outwash plain and 2km downstream.</li> <li><strong>Tributary </strong>: 5 hillslopes tributaries originating from small seasonal overland flow or small springs at the base of the morainic hillslope. Those tributaries were monitored weekly. In addition, a few other seasonal lateral streams were sampled in various locations (Type: Other tributaries).</li> <li><strong>Bedrock </strong>: A few exfiltrations directly leaking out of the bedrock outcrop were sampled.</li> <li><strong>Ice </strong>: Ice was sampled either as surface ice (small cores 5 cm deep), as deeper cores (5 to 8m deep) or as meltwater from supraglacial gullies. All solid ice samples were melted at ambiant air temperature in air-tight plastic bags before being transferred into 12 mL vials.</li> <li><strong>Snow </strong>: The snowpack was sampled either at the surface (0 to 5cm) or at about 20 cm depth. Where possible, meltwater leaking from the snowpack was sampled. At 3 locations in 2021, we dug snowpits from which we sampled snow at different layers with depth. All solid snow samples were melted at ambiant air temperature in air-tight plastic bags before being transferred into 12 mL vials</li> <li><strong>Rain </strong>: Rainwater was mostly sampled at our camp site at 2450 m. asl. Rainwater samples represent single rain events which are identified by dry periods of at least one day long.</li> <li><strong>Groundwater </strong>: shallow (2 to 3 meters) fully-screened groundwater wells were installed in the outwash plain and water sampled monthly in the snow-free season.</li> </ul> <p>- GPS coordinates are provided with each point (Swiss coordinate system CH1903+ / LV95<strong> (EPSG: 2056)).</strong></p> <p>- Dates are provided in local timezone (GMT+1 with daylight saving time) and in UTC date format.</p> <p>- Analyitcal error from the Picarro spectrometer is reported as 1 standard deviation.</p> <p>More information can be accessed in the corresponding publication by Müller et al. (to be published in 2023).</p> <p><strong>Data files</strong></p> <ul> <li><em>Otemma_isotope_EC_T_2019_2021.csv</em> : file containing all data with GPS coordinates</li> <li> <p><em>isotope_locations_Otemma.jpg</em> : an overview of the locations of each measurement point</p> </li> <li> <p><em>Otemma_Isotopes_2019-2020.html </em>: interactive plots of all datasets (δ<sup>2</sup>H, EC, temperature), classified by Type.</p> </li> </ul>
SAMI3 data in netCDF format (2019-Mar-28)
<p>SAMI3 (Sami3 is Also a Model of the Ionosphere) is a seamless, three-dimensional, physics-based model of the ionosphere (Huba et al, 2008). It is based on SAMI2, a two-dimensional model of the ionosphere (Huba et al., 2000). <br><br>SAMI3 models the plasma and chemical evolution of seven ion species (H⁺, He⁺, N⁺, O⁺, N⁺₂, NO⁺ and O⁺₂). The temperature equation is solved for three ion species (H⁺, He⁺ and O⁺) and for the electrons. Ion inertia is included in the ion momentum equation for motion along the geomagnetic field. This is important in modeling the topside ionosphere and plasmasphere where the plasma becomes collisionless. <br><br>SAMI3 includes 21 chemical reactions and radiative recombination, and uses a nonorthogonal, nonuniform, fixed grid for the magnetic latitude range +/- 89 degrees.. <br><br><b>Drivers</b><br>Neutral composition, temperature, and winds: NRLMSISE00 (Picone et al., 2002) and HWM14 (Drob et al., 2015). <br>Solar radiation: Flare Irradiance Spectral Model version 2 (FISM v2)<br>Magnetic field: Richmond apex model [Richmond, 1995]. <br>Neutral wind dynamo electric field: Determined from the solution of a 2D potential equation [Huba et at., 2008]. <br>For the SAMI3/Weimer configuration: High latitude electric field: calculated from the empirical Weimer model for the potential. <br>For the SAMI3/AMPERE configuration: High latitude electric field: calculated using the Magnetosphere-Ionosphere Coupling solver (MIX) developed by Merkin and Lyon (2010). The inputs to MIX are SAMI3's internal conductances, plus field-aligned current observations from Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE), derived from the 66+ satellite Iridium NEXT constellation's engineering magnetometer data. This potential calculation is described in Chartier et al (2022). <br><br>For ease of use, SAMI3 output is remapped to a regular grid using the Earth System Modeling Framework by Hill et al (2004)</p>
SAMI3 data in netCDF format (2019-Mar-29)
<p>SAMI3 (Sami3 is Also a Model of the Ionosphere) is a seamless, three-dimensional, physics-based model of the ionosphere (Huba et al, 2008). It is based on SAMI2, a two-dimensional model of the ionosphere (Huba et al., 2000). <br><br>SAMI3 models the plasma and chemical evolution of seven ion species (H⁺, He⁺, N⁺, O⁺, N⁺₂, NO⁺ and O⁺₂). The temperature equation is solved for three ion species (H⁺, He⁺ and O⁺) and for the electrons. Ion inertia is included in the ion momentum equation for motion along the geomagnetic field. This is important in modeling the topside ionosphere and plasmasphere where the plasma becomes collisionless. <br><br>SAMI3 includes 21 chemical reactions and radiative recombination, and uses a nonorthogonal, nonuniform, fixed grid for the magnetic latitude range +/- 89 degrees.. <br><br><b>Drivers</b><br>Neutral composition, temperature, and winds: NRLMSISE00 (Picone et al., 2002) and HWM14 (Drob et al., 2015). <br>Solar radiation: Flare Irradiance Spectral Model version 2 (FISM v2)<br>Magnetic field: Richmond apex model [Richmond, 1995]. <br>Neutral wind dynamo electric field: Determined from the solution of a 2D potential equation [Huba et at., 2008]. <br>For the SAMI3/Weimer configuration: High latitude electric field: calculated from the empirical Weimer model for the potential. <br>For the SAMI3/AMPERE configuration: High latitude electric field: calculated using the Magnetosphere-Ionosphere Coupling solver (MIX) developed by Merkin and Lyon (2010). The inputs to MIX are SAMI3's internal conductances, plus field-aligned current observations from Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE), derived from the 66+ satellite Iridium NEXT constellation's engineering magnetometer data. This potential calculation is described in Chartier et al (2022). <br><br>For ease of use, SAMI3 output is remapped to a regular grid using the Earth System Modeling Framework by Hill et al (2004)</p>
SAMI3 data in netCDF format (2019-Mar-25)
<p>SAMI3 (Sami3 is Also a Model of the Ionosphere) is a seamless, three-dimensional, physics-based model of the ionosphere (Huba et al, 2008). It is based on SAMI2, a two-dimensional model of the ionosphere (Huba et al., 2000). <br><br>SAMI3 models the plasma and chemical evolution of seven ion species (H⁺, He⁺, N⁺, O⁺, N⁺₂, NO⁺ and O⁺₂). The temperature equation is solved for three ion species (H⁺, He⁺ and O⁺) and for the electrons. Ion inertia is included in the ion momentum equation for motion along the geomagnetic field. This is important in modeling the topside ionosphere and plasmasphere where the plasma becomes collisionless. <br><br>SAMI3 includes 21 chemical reactions and radiative recombination, and uses a nonorthogonal, nonuniform, fixed grid for the magnetic latitude range +/- 89 degrees.. <br><br><b>Drivers</b><br>Neutral composition, temperature, and winds: NRLMSISE00 (Picone et al., 2002) and HWM14 (Drob et al., 2015). <br>Solar radiation: Flare Irradiance Spectral Model version 2 (FISM v2)<br>Magnetic field: Richmond apex model [Richmond, 1995]. <br>Neutral wind dynamo electric field: Determined from the solution of a 2D potential equation [Huba et at., 2008]. <br>For the SAMI3/Weimer configuration: High latitude electric field: calculated from the empirical Weimer model for the potential. <br>For the SAMI3/AMPERE configuration: High latitude electric field: calculated using the Magnetosphere-Ionosphere Coupling solver (MIX) developed by Merkin and Lyon (2010). The inputs to MIX are SAMI3's internal conductances, plus field-aligned current observations from Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE), derived from the 66+ satellite Iridium NEXT constellation's engineering magnetometer data. This potential calculation is described in Chartier et al (2022). <br><br>For ease of use, SAMI3 output is remapped to a regular grid using the Earth System Modeling Framework by Hill et al (2004)</p>
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