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1,118 results for “Time series”
Load and generation time series for German federal states: Static vs. dynamic regionalization factors (data)
<p>This dataset contains regionalization factors for electricity generation and demand time series in Germany for the years 2019 - 2022. The factors can be used to distribute national generation and demand time series available from SMARD or ENTSO-E to federal state level. The methods underlying the regionalization factors are described in [1], with a focus on the year 2021. However, an extended version of the dataset covering the years 2019-2022 is also included for comprehensive analysis. Moreover, the dataset comprises the corresponding regionalized generation and demand time series at the federal state level of Germany. This time series has been generated using the provided distribution factors for the years 2019-2022 and corresponding generation and demand time series from SMARD [2]. Addtionally, the regionalization methodology for the distributed generation and demand data for the year 2021 has been supplemented with validation data, as described in [1]. This data has been cross-checked against the available SMARD Transmission System Operator (TSO) data. A description of the preprocessing required to obtain the TSO data comparison is provided in a separate .txt file. A PDF document has been prepared, which includes scatter plots that illustrate a comparison between actual and allocated generation per production type or demand data for TSOs on an hourly basis for the year 2021.</p> <p><strong>"static_regionalization_factors.2021[csv, xlsx]"</strong></p> <p>Each column corresponds to one factor per federal state and per production type or demand. Regionalization factors are based on share of generation capacity in each state (generation) or population and GDP (demand).</p> <p><strong>"dynamic_regionalization_factors_2021.[csv, xlsx]"<br> “dynamic_regionalization_factors_all.[csv, xlsx]”</strong></p> <p>Each column corresponds to one factor per federal state and per production type or demand. Each row corresponds to a specific hour of the years 2019 through 2022. Regionalization factors are based on a combination of per unit generation data and share of generation capacity in each state, simulated renewable generation data based on spatio-temporal weather data and distribution of wind and solar generation capacities, and a regionalized load dataset for 2015 [3].</p> <p><strong>“time_series_federal_states_all.[csv, xlsx]”</strong></p> <p>Each column corresponds to the allocated electricity generation or demand per federal state per production type or demand in units of MWh. Each row corresponds to a specific hour of the years 2019 through 2022. The regionalized generation and demand time series has been created by utilizing the dynamic regionalization factors provided in the dataset, in conjunction with the national electricity generation and demand data of Germany as provided by SMARD [2].</p> <p><strong>“TSO_actual.[csv, xlsx]”<br> “TSO_allocated.[csv, xlsx]”</strong></p> <p>Each column corresponds to the spatially aggregated electricity generation per type or demand per TSO in units of GWh. Each row corresponds to one hour of the year 2021. The TSOs in Germany do not hold direct responsibility for individual federal states, but rather for specific regions. In order to assess the validity of the regionalization methodology employed, it was necessary to generate data at the NUTS3 level and subsequently aggregate it to correspond with the relevant TSOs. The data is pre-processed at NUTS3 level and then undergoes the same methodology as outlined in [1]. The preprocessing steps required to map the installed capacity to the TSO level are explained in the accompanying .txt file. The allocated generation and demand data are aggregated to correspond to the TSO level using a shapefile of mapped regions in Germany that correspond to the TSOs [4]. The actual TSO data is generation and demand as published by SMARD [2]. The accompanying PDF presents scatter plots that showcase the actual vs allocated hourly generation types or demand per TSO, expanding on the information provided in the article.</p> <p>[1] M. Sundblad, T. Fürmann, A. Weidlich and M. Schäfer, "<a href="https://arxiv.org/abs/2304.02951">Load and generation time series for German federal states: Static vs. dynamic regionalization factors</a>," <em>2023 Open Source Modelling and Simulation of Energy Systems (OSMSES)</em>, Aachen, Germany, 2023, pp. 1-6, doi: 10.1109/OSMSES58477.2023.10089686.</p> <p>[2] Bundesnetzagentur | <a href="https://www.smard.de/home">SMARD.de</a></p> <p>[3] Matthias Kühnbach, Anke Bekk, and Anke Weidlich (2021). <a href="https://www.forecast-model.eu/forecast-en/content/publications.php">Prepared for regional self-supply? On the regional fit of electricity demand and supply in Germany</a>. Energy Strategy Reviews, 34:100609, 20</p> <p>[4] Frysztacki, Martha Maria. (2023). Mapping of districts to control zones of German Transmission System Operators (TSOs) (v0.1) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7530196">https://doi.org/10.5281/zenodo.7530196</a></p>
Transition and Drivers of Elastic to Inelastic Deformation in the Abarkuh Plain from InSAR Multi-Sensor Time Series and Hydrogeological Data
<p>This repository contains the datasets used in <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JB026430">Mirzadeh et al., 2023</a>. It includes three InSAR time-series datasets from the Envisat descending orbit, ALOS-1 ascending orbit, and Sentinel-1A in ascending and descending orbits, acquired over the Abarkuh Plain, Iran, as well as the geological map of the study area and the GNSS and hydrogeological data used in this research.</p> <p>Dataset 1: Envisat descending track 292</p> <ul> <li>Date: 06 Oct 2003 - 05 Sep 2005 (12 acquisitions)</li> <li>Processor: ISCE/stripmapStack + MintPy</li> <li>Displacement time-series (in HDF-EOS5 format): timeseries_LOD_tropHgt_ramp_demErr.h5</li> <li>Mean LOS Velocity (in HDF-EOS5 format): velocity.h5</li> <li>Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5</li> <li>Geometry (in HDF-EOS5 format): geometryRadar.h5</li> </ul> <p>Dataset 2: ALOS-1 ascending track 569</p> <ul> <li>Date: 06 Dec 2006 - 17 Dec 2010 (14 acquisitions)</li> <li>Processor: ISCE/stripmapStack + MintPy</li> <li>Displacement time-series (in HDF-EOS5 format): timeseries_ERA5_ramp_demErr.h5</li> <li>Mean LOS Velocity (in HDF-EOS5 format): velocity.h5</li> <li>Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5</li> <li>Geometry (in HDF-EOS5 format): geometryRadar.h5</li> </ul> <p>Dataset 2: Sentinel-1 ascending track 130 and descending track 137</p> <ul> <li>Date: 14 Oct 2014 - 28 Mar 2020 (129 ascending acquisitions) + 27 Oct 2014 - 29 Mar 2020 (114 descending acquisitions)</li> <li>Processor: ISCE/topsStack + MintPy</li> <li>Displacement time-series (in HDF-EOS5 format): timeseries_ERA5_ramp_demErr.h5</li> <li>Mean LOS Velocity (in HDF-EOS5 format): velocity.h5</li> <li>Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5</li> <li>Geometry (in HDF-EOS5 format): geometryRadar.h5</li> </ul> <p>The time series and Mean LOS Velocity (MVL) products can be georeferenced and resampled using the makTempCoh and geometryRadar products and the MintPy commands/functions.</p>
In-situ time series collected in the Patagonian fjords.
<p>The dataset included the time series of Conservative temperature, Absolute Salinity, Dissolved Oxygen, and Marine Current published in this manuscript.</p>
Exploratory Analysis of Top 50 Companies in Indian Stock Market: A Time Series Analysis of Historical Stock Prices
<p>The dataset consists of 'open, close, high, low, close, adj close and volume' columns for the top 50 Indian Companies and the dataset been fetched from YahooFinance for over 20 years. </p>
Data and code example for the article: "Massively parallel hybrid quantum-classical machine learning for kernelized time-series classification"
<p>Data needed to reproduce the figures of <a href="https://arxiv.org/abs/2305.05881">https://arxiv.org/abs/2305.05881</a> and a simple code example of a quantum-convex-classical neural network used to train a sine versus cosine classification problem.</p>
Simulated and real datacubes for developing and testing changepoint algorithms for spatially correlated, short and noisy time-series
<p>A set of datacubes where z-dimension is time, thus each (x,y,.) is a timeseries. The idea is to detect sudden changes in each series, assuming 1) the series can be quite short and noisy 2) the change occurs in spatial patches. The set has synthetic examples with known change-events, and a real-world dataset with unknown change-events. The data files are related to the pape</p> <p>T Rajala, P Packalen, M Myllymäki, A Kangas (2023): Improving detection of changepoints in short and noisy time-series with local correlations: Connecting the events in pixel neighbourhoods, "Journal of Agricultural, Biological and Environmental Statistics", https://doi.org/10.1007/s13253-023-00546-1</p> <p>More of the NFI data is available from Natural Resources Institute Finland, https://kartta.luke.fi/index-en.html</p> <p>See `data/00data_readme.txt` for further details.</p> <p> </p>
Simulated population time series used to build and test a model of accuracy for population-based global biodiversity indicators
<p class="MsoNormal">Global biodiversity is facing a crisis, which must be solved through effective policies and on-the-ground conservation. But governments, NGOs, and scientists need reliable indicators to guide research, conservation actions, and policy decisions. Developing reliable indicators is challenging because the data underlying those tools is incomplete and biased. For example, the Living Planet Index tracks the changing status of global vertebrate biodiversity, but taxonomic, geographic and temporal gaps and biases are present in the aggregated data used to calculate trends. But without a basis for real-world comparison, there is no way to directly assess an indicator's accuracy or reliability. Instead, a modelling approach can be used.</p> <p class="MsoNormal">We developed a model of trend reliability, using simulated datasets as stand-ins for the "real world", degraded samples as stand-ins for indicator datasets (e.g. the Living Planet Database), and a distance measure to quantify reliability by comparing sampled to unsampled trends. The model revealed that the proportion of species represented in the database is not always indicative of trend reliability. Important factors are the number and length of time series, as well as their mean growth rates and variance in their growth rates, both within and between time series. We found that many trends in the Living Planet Index need more data to be considered reliable, particularly trends across the global south. In general, bird trends are the most reliable, while reptile and amphibian trends are most in need of additional data. We simulated three different solutions for reducing data deficiency, and found that collating existing data (where available) is the most efficient way to improve trend reliability, and that revisiting previously-studied populations is a quick and efficient way to improve trend reliability until new long-term studies can be completed and made available.</p>
Meteorological variables for Agriculture: daily time series for the Italian Area (MADIA daily)
<p> </p> <p><strong>Abstract</strong></p> <p>The <strong>MADIA daily gridded dataset</strong> provides the series of the main <strong>agro-meteorological </strong>variables derived from ERA5 hourly surface data, with a spatial resolution of 0.25 degrees, across the Italian domain for the period <strong>1981-2022</strong>. The dataset contains time series of minimum, average and maximum air temperature, minimum and maximum air relative humidity, wind speed, solar radiation, precipitation and reference evapotranspiration according to the FAO Penman-Monteith method. Data is provided at daily temporal resolution and in <strong>csv </strong>format (every cell is identified by the latitude/longitude coordinates of its centre). The dataset is annotated with discovery and description metadata. A vector file is included with the <strong>ERA5 cell polygons </strong>covering the Italian country for visualizing and mapping csv data. In order to facilitate the data reuse for computing statistics at Italian NUTS 2 and 3 levels, a complementary vector file which reports the cell weight in terms of fraction covered of each administrative unit considered, as well as its altitude, is provided in:</p> <ul> <li>Parisse Barbara, Alilla Roberta, Pepe Antonio Gerardo, & De Natale Flora. (2022). <em>Meteorological variables for Agriculture: a dataset for the Italian Area (MADIA)</em> [Data set]. Zenodo. <a href="http://10.5281/zenodo.6868944">https://doi.org/10.5281/zenodo.6868944</a> </li> </ul> <p>Further details on methods applied for data processing are available in:</p> <ul> <li>Parisse B., Alilla R., Pepe A.G., De Natale F., <em>MADIA - Meteorological variables for Agriculture: a Dataset for the Italian Area</em>, Data in Brief, 46 (2023), 108843, <a href="https://doi.org/10.1016/j.dib.2022.108843">10.1016/j.dib.2022.108843</a>, (<a href="https://www.sciencedirect.com/science/article/pii/S2352340922010460">https://www.sciencedirect.com/science/article/pii/S2352340922010460</a>)</li> </ul> <p>The MADIA daily dataset will be periodically updated.</p> <p><strong>Attached content</strong></p> <p>A ZIP archive composed by the following folders:</p> <ol> <li>csv_data: daily time series for each year from 1981 to 2022 in csv format</li> <li>metadata: discovery and description metadata</li> <li>shp_data: a complementary vector layer with the ERA5 cell polygons for Italy</li> </ol> <p><strong>Acknowledgments</strong></p> <p>This work was supported by the Italian Ministry of Agricultural, Food and Forestry Policies (AgriDigit-Agromodelli, DM n. 36502 of 20/12/2018)</p>
Apulian Aqueduct demo site: daily time series of estimated inflows (natural springs and reservoirs) for climate projections
<p>This dataset contains the daily time series of net estimated inflows (natural springs and main reservoirs of Apulian aqueduct - demo site 1) computed with a lumped rainfall-runoff model starting from climate projections of precipitation and temperature.</p> <p>This dataset considers two representative concentration pathways (RCP 4.5 and 8.5) and two decades, in the medium (2050-2059) and long-term future (2090-2099).</p> <ul> <li>Temporal coverage: 2050-2059; 2090-2099</li> <li>Spatial coverage: Springs: Sele, Calore; Reservoirs: Conza, Locone, Monte Cotugno, Occhito, Pertusillo</li> <li>Unit of measure: m3/s</li> </ul> <p>More information and details on the content of this dataset can be found in Project Ô <a href="https://zenodo.org/record/7576611">Deliverable D4.4</a>. </p>
Eccentricity signal in the nannofossil time-series across the Middle Pleistocene Transition in the Northwestern Pacific Ocean
<p>Supplementary data to the paper "Eccentricity signal in the nannofossil time-series across the Middle Pleistocene Transition in the Northwestern Pacific Ocean" of Bordiga et al 2023 in Quaternary Science Reviews.</p>
300 K time-resolved photoluminescence on sample set- Buffer temperature series-Sample C3206
<p>Raw data of time-resolved photoluminescence measurements on the sample C3206 (buffer grown at 870 °C) in the buffer temperature sample set</p>
UK Electricity consumption time-series from Elexon data portal (Actual Total Load Per Bidding Zone)
<p>Data from 2015-01-01 to 2023-08-10. Downloaded using ElexonDataPortal for Python.</p> <p>Dataset B0610 – Actual Total Load per Bidding Zone: <a href="https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=0CDYQw7AJahcKEwjw1P2089yAAxUAAAAAHQAAAAAQAw&url=https%3A%2F%2Fwww.elexon.co.uk%2Fdocuments%2Fbmrs-api-and-data-push-guide-for-p408%2F&psig=AOvVaw3JTwF_pxNDLFZp3HSDJa_s&ust=1692128319855369&opi=89978449">https://www.elexon.co.uk/documents/bmrs-api-and-data-push-guide-for-p408/</a></p>
Time series of flow measurement and water quality monitoring at a large combined sewer overflow in Berlin
<p>The table contains 3 years CSO monitoring time series, already split in 22 separated CSO events. All details about the monitoring set up in the papers and reports indicated below.</p> <p>Fields:</p> <ul> <li>evtID: event ID</li> <li>myDateTime; date/time</li> <li>v: m/s velocity</li> <li>Q: m³/s flow</li> <li>TSS: mg/l TSS concentration</li> <li>COD: mg/l COD concentration</li> <li>CODf: mg/l dissolved COD concentration</li> <li>EC: µS/cm electric conductivity</li> <li>NH4_N: mg/l NH4_N concentration</li> </ul> <p>TSS and COD have been measured with a spectrometer using a linear local calibration as presented in Lepot et al., 2016.</p> <p>ore information about the monitoring set up in </p> <p>Sandoval, S., Torres, A., Pawlowsky-Reusing, E., Riechel, M., Caradot, N. (2013): The evaluation of rainfall influence on CSO characteristics: the Berlin case study. Water Science & Technology Vol. 68 (12): 2683-2690 10.2166/wst.2013.524</p> <p>Caradot, N. (2012): Continuous Monitoring of Combined Sewer Overflows in the Sewer and the Receiving River: Return on Experience. Kompetenzzentrum Wasser Berlin gGmbH; MIA-CSO Project Report</p> <p>Riechel, M., Matzinger, A., Pawlowsky-Reusing, E., Sonnenberg, H., Uldack, M., Heinzmann, B., Caradot, N., von Seggern, D., Rouault, P. (2016): Impacts of combined sewer overflows on a large urban river - Understanding the effect of different management strategies. Water Research 105: 264-273 10.1016/j.watres.2016.08.017</p> <p>Caradot, N., Sonnenberg, H., Riechel, M., Matzinger, A., Rouault, P. (2013): The influence of local calibration on the quality of UV-VIS spectrometer measurements in urban stormwater monitoring. Water Practice & Technology Vol 8 (No 3-4): 417-425 10.2166/wpt.2013.042</p> <p>Lepot, M., Torres, A., Hofer, T., Caradot, N., Gruber, G., Aubin, J.-B., Bertrand-Krajewski, J.-L. (2016): Calibration of UV/Vis spectrophotometers: A review and comparison of different methods to estimate TSS and total and dissolved COD concentrations in sewers, WWTPs and rivers. Water Research 101 (15 September 2016): 519-534 10.1016/j.watres.2016.05.070</p>
One year time series of relative electric (onshore and offshore) wind turbine power datasets
<p>The datasets (in dat file format) contain ordered time series (in unit of hours with 15 minutes time resolution) of relative electric wind turbine (WT) power (expressed in percentage) of one randomly selected year (05 August 2022 to 04 August 2023) and four of its constituting weeks (01 to 07 SEP 2022, 02 to 08 JAN 2023, 13 to 19 MAR 2023 and 16 to 22 JUL 2023) with their associated graphs (in PNG file format). The original data stem from the electricity grid of Flanders (onshore) and Belgium (offshore) as provided by Elia ( <a href="https://priv-lu-myremote.tech.ec.europa.eu/en/grid-data/power-generation/,DanaInfo=.awxyCiqohHko,SSL+solar-pv-power-generation-data">https://www.elia.be/en/grid-data/power-generation/solar-pv-power-generation-data</a> ) under CC BY 4.0 license (<a href="https://priv-lu-myremote.tech.ec.europa.eu/en/grid-data/,DanaInfo=.awxyCiqohHko,SSL+elia-open-data-license?csrt=16568311101247852187">https://www.elia.be/en/grid-data/elia-open-data-license?csrt=16568311101247852187</a>). The relative electric WT power was derived by dividing the measured electric WT power by the monitored peak electric WT power multiplied by 100 %.</p>
One year time series of relative electric photovoltaic power dataset
<p>The datasets (in dat file format) contain ordered time series (in unit of hours with 15 minutes time resolution) of relative electric photovoltaic (PV) power (expressed in percentage) of one randomly selected year (05 August 2022 to 04 August 2023) and four of its constituting weeks (01 to 07 SEP 2022, 02 to 08 JAN 2023, 13 to 19 MAR 2023 and 16 to 22 JUL 2023) with their associated graphs (in PNG file format). The original data stem from the electricity grid of Brussels as provided by Elia ( <a href="https://priv-lu-myremote.tech.ec.europa.eu/en/grid-data/power-generation/,DanaInfo=.awxyCiqohHko,SSL+solar-pv-power-generation-data">https://www.elia.be/en/grid-data/power-generation/solar-pv-power-generation-data</a> ) under CC BY 4.0 license (<a href="https://priv-lu-myremote.tech.ec.europa.eu/en/grid-data/,DanaInfo=.awxyCiqohHko,SSL+elia-open-data-license?csrt=16568311101247852187">https://www.elia.be/en/grid-data/elia-open-data-license?csrt=16568311101247852187</a>). The relative electric PV power was derived by dividing the measured electric PV power by the monitored peak electric PV power multiplied by 100 %.</p>
SOMLIT-Astan time-series (2009-2016) rDNA 18S V4 ASV table (dada2)
<p>This repository contains a rDNA 18S V4 ASV table (astan-18sv4_dada2_v1.0.filtered.table.with.taxo.lulu.tsv.gz) for SOMLIT-Astan time-series (2009-2016). Each ASV, one per row, is described by the following fields: <strong>amplicon</strong> = ASV identifier; <strong>taxonomy</strong> = taxonomic path assigned to the ASV using IDTAXA; <strong>confidence</strong> = IDTAXA confidence scores for each taxonomic rank; <strong>sequence</strong> = ASV nucleic acid sequence; <strong>total</strong> = total number of reads for the entire dataset; <strong>spread</strong> = number of samples in which the ASV is detected; <strong>RAXXXXXX-X</strong> = number of reads in each of the 375 SOMLIT-Astan time-series samples. Sample ids contain information about the sampling date and the size fraction. The six digits after RA indicate the date (year, month and day), and the value after - indicate the size fraction, 02 for 0.2 to 3 µm and 3 for superior to 3 µm.</p> <p>How this table has been generated:</p> <p>The procedures used for DNA extraction and amplification of the 18S V4 region of the ribosomal operon are described in <a href="https://doi.org/10.1111/mec.16539">https://doi.org/10.1111/mec.16539</a>. The eukaryote-specific primers used were TAReuk454FWD1 (5’-CCAGCASCYGCGGTAATTCC-3’, Saccharomyces cerevisiae position 565‐584) and TAReukREV3 (5’-ACTTTCGTTCTTGATYRA-3’, Saccharomyces cerevisiae position 964‐981) (Stoeck et al., 2010). Raw sequences are available at the European Nucleotide Archive (ENA) under the project id PRJEB48571.</p> <p>The paired-end fastq files obtained from sequencing were demultiplexed and primers were removed using Cutadapt v2.8, filtering out untrimmed reads. Then, forward and reverse reads were trimmed at position 210 and reads with ambiguous nucleotides or with a maximum number of expected errors (maxEE) superior to 2 were filtered out using the function filterAndTrim() from the R package dada2 version 1.22 with R version 4.1.1 . For each run, error rates were defined using the function learnErrors(), reads were dereplicated using the function derepFastq() function and denoised using the dada() function with default options before being merged. Remaining chimaeras were removed using the function removeBimeraDenovo(). Only amplicon sequence variants (ASVs) with at least three reads in two samples were retained. ASVs were taxonomically assigned using IDTAXA with default parameters with the PR2 database version 4.14. Finally, the LULU curation approach was applied to the ASV table to remove remaining erroneous amplicons. For more details relative to the bioinformatic pipeline used to generate the ASV tables, see <a href="https://gitlab.sb-roscoff.fr/nhenry/rosko-naples-bioinfo">https://gitlab.sb-roscoff.fr/nhenry/rosko-naples-bioinfo</a>.</p>
Lake Vansjø-Vanemfjorden long time-series data for nutrients_colour and cyanobacteria
<p>Data from lake Vansjø-Vanemfjorden basin from 1996-2020 for total phosphorus, total nitrogen, water colour and maximum biovolume of cyanobacteria.</p>
Monthly aggregated GLASS FAPAR V6 (250 m): 50th percentile monthly time-series (2005)
<p><strong>List of Subdatasets:</strong></p> <ul> <li>Long-term data: <a href="https://doi.org/10.5281/zenodo.8381409">2000-2021</a></li> <li>5th percentile (p05) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408654">2000</a>, <a href="https://doi.org/10.5281/zenodo.8411611">2001</a>, <a href="https://doi.org/10.5281/zenodo.8412712">2002</a>, <a href="https://doi.org/10.5281/zenodo.8413021">2003</a>, <a href="https://doi.org/10.5281/zenodo.8413689">2004</a>, <a href="https://doi.org/10.5281/zenodo.8414639">2005</a>, <a href="https://doi.org/10.5281/zenodo.8411609">2006</a>, <a href="https://doi.org/10.5281/zenodo.8414085">2007</a>, <a href="https://doi.org/10.5281/zenodo.8414960">2008</a>, <a href="https://doi.org/10.5281/zenodo.8415476">2009</a>, <a href="https://doi.org/10.5281/zenodo.8415686">2010</a>, <a href="https://doi.org/10.5281/zenodo.8412154">2011</a>, <a href="https://doi.org/10.5281/zenodo.8414082">2012</a>, <a href="https://doi.org/10.5281/zenodo.8411364">2013</a>, <a href="https://doi.org/10.5281/zenodo.8414933">2014</a>, <a href="https://doi.org/10.5281/zenodo.8415414">2015</a>, <a href="https://doi.org/10.5281/zenodo.8412246">2016</a>, <a href="https://doi.org/10.5281/zenodo.8414083">2017</a>, <a href="https://doi.org/10.5281/zenodo.8411366">2018</a>, <a href="https://doi.org/10.5281/zenodo.8415203">2019</a>, <a href="https://doi.org/10.5281/zenodo.8415549">2020</a>, <a href="https://doi.org/10.5281/zenodo.8387608">2021</a></li> <li>50th percentile (p50) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408710">2000</a>, <a href="https://doi.org/10.5281/zenodo.8408798">2001</a>, <a href="https://doi.org/10.5281/zenodo.8408866">2002</a>, <a href="https://doi.org/10.5281/zenodo.8415319">2003</a>, <a href="https://doi.org/10.5281/zenodo.8415619">2004</a>, <a href="https://doi.org/10.5281/zenodo.8415878">2005</a>, <a href="https://doi.org/10.5281/zenodo.8416080">2006</a>, <a href="https://doi.org/10.5281/zenodo.8416619">2007</a>, <a href="https://doi.org/10.5281/zenodo.8417164">2008</a>, <a href="https://doi.org/10.5281/zenodo.8417513">2009</a>, <a href="https://doi.org/10.5281/zenodo.8417708">2010</a>, <a href="https://doi.org/10.5281/zenodo.8415669">2011</a>, <a href="https://doi.org/10.5281/zenodo.8416000">2012</a>, <a href="https://doi.org/10.5281/zenodo.8416542">2013</a>, <a href="https://doi.org/10.5281/zenodo.8417055">2014</a>, <a href="https://doi.org/10.5281/zenodo.8417467">2015</a>, <a href="https://doi.org/10.5281/zenodo.8415747">2016</a>, <a href="https://doi.org/10.5281/zenodo.8416333">2017</a>, <a href="https://doi.org/10.5281/zenodo.8416835">2018</a>, <a href="https://doi.org/10.5281/zenodo.8417326">2019</a>, <a href="https://doi.org/10.5281/zenodo.8417589">2020</a>, <a href="https://doi.org/10.5281/zenodo.8388078">2021</a></li> <li>95th percentile (p95) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408949">2000</a>, <a href="https://doi.org/10.5281/zenodo.8409059">2001</a>, <a href="https://doi.org/10.5281/zenodo.8409154">2002</a>, <a href="https://doi.org/10.5281/zenodo.8409362">2003</a>, <a href="https://doi.org/10.5281/zenodo.8416487">2004</a>, <a href="https://doi.org/10.5281/zenodo.8417029">2005</a>, <a href="https://doi.org/10.5281/zenodo.8417833">2006</a>, <a href="https://doi.org/10.5281/zenodo.8417996">2007</a>, <a href="https://doi.org/10.5281/zenodo.8418308">2008</a>, <a href="https://doi.org/10.5281/zenodo.8418669">2009</a>, <a href="https://doi.org/10.5281/zenodo.8418986">2010</a>, <a href="https://doi.org/10.5281/zenodo.8417649">2011</a>, <a href="https://doi.org/10.5281/zenodo.8417816">2012</a>, <a href="https://doi.org/10.5281/zenodo.8417959">2013</a>, <a href="https://doi.org/10.5281/zenodo.8418253">2014</a>, <a href="https://doi.org/10.5281/zenodo.8418625">2015</a>, <a href="https://doi.org/10.5281/zenodo.8417759">2016</a>, <a href="https://doi.org/10.5281/zenodo.8417898">2017</a>, <a href="https://doi.org/10.5281/zenodo.8418076">2018</a>, <a href="https://doi.org/10.5281/zenodo.8418442">2019</a>, <a href="https://doi.org/10.5281/zenodo.8418751">2020</a>, <a href="https://doi.org/10.5281/zenodo.8392976">2021</a></li> </ul> <p><strong>General Description</strong></p> <p>The <i>monthly aggregated Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</i> dataset is derived from <abbr title="glass.umd.edu/FAPAR/MODIS/250m/">250m 8d GLASS V6 FAPAR</abbr>. The data set is derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and LAI data using several other FAPAR products (MODIS Collection 6, GLASS FAPAR V5, and PROBA-V1 FAPAR) to generate a bidirectional long-short-term memory (Bi-LSTM) model to estimate FAPAR. The dataset time spans from March 2000 to December 2021 and provides data that covers the entire globe. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping. The dataset includes:</p> <ul> <li><strong>Long-term:</strong></li> </ul> <p>Derived from monthly time-series. This dataset provides linear trend model for the p95 variable: (1) slope beta mean (p95.beta_m), p-value for beta (p95.beta_pv), intercept alpha mean (p95.alpha_m), p-value for alpha (p95.alpha_pv), and coefficient of determination R<sup>2</sup> (p95.r2_m).</p> <ul> <li><strong>Monthly time-series:</strong></li> </ul> <p>Monthly aggregation with three standard statistics: (1) 5th percentile (p05), median (p50), and 95th percentile (p95). For each month, we aggregate all composites within that month plus one composite each before and after, ending up with 5 to 6 composites for a single month depending on the number of images within that month.</p> <p><strong>Data Details</strong></p> <ul> <li><strong>Time period:</strong> March 2000 – December 2021</li> <li><strong>Type of data:</strong> Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</li> <li><strong>How the data was collected or derived:</strong> Derived from 250m 8 d GLASS V6 FAPAR using Python running in a local HPC. The time-series analysis were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li> <li><strong>Statistical methods used:</strong> for the long-term, Ordinary Least Square (OLS) of p95 monthly variable; for the monthly time-series, percentiles 05, 50, and 95.</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Antarctica.</li> <li><strong>Coordinate reference system:</strong> EPSG:4326</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -62.0008094, 179.9999424, 87.37000)</li> <li><strong>Spatial resolution:</strong> 1/480 d.d. = 0.00208333 (250m)</li> <li><strong>Image size:</strong> 172,800 x 71,698</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <p><strong>Support</strong></p> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: <a href="https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues">https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues</a></p> <p><strong>Reference</strong></p> <p>Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Herold, M., Jung, M., Duveiller, G., Weynants, M., Wheeler, I., (2023?) "Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution", submitted to PeerJ, preprint available at: <a href="https://doi.org/10.21203/rs.3.rs-3415685/v1">https://doi.org/10.21203/rs.3.rs-3415685/v1</a></p> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> fapar = Fraction of Absorbed Photosynthetically Active Radiation</li> <li><strong>variable procedure combination:</strong> essd.lstm = Earth System Science Data with bidirectional long short-term memory (Bi–LSTM)</li> <li><strong>Position in the probability distribution / variable type:</strong> p05/p50/p95 = 5th/50th/95th percentile</li> <li><strong>Spatial support:</strong> 250m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20000301 = 2000-03-01</li> <li><strong>Time reference end time:</strong> 20211231 = 2022-12-31</li> <li><strong>Bounding box:</strong> go = global (without Antarctica)</li> <li><strong>EPSG code:</strong> epsg.4326 = EPSG:4326</li> <li><strong>Version code:</strong> v20230628 = 2023-06-28 (creation date)</li> </ol>
Bermuda Atlantic Time-Series Study (BATS) Pigment Data
<p>This dataset is published on Zenodo by the Simons CMAP curators for long-term care. All credits go to the data producers at the Bermuda Atlantic Time-series Study (BATS): https://bats.bios.asu.edu/bats-data/ </p><p>The BATS (Bermuda Atlantic Time-series Study) Pigment dataset is time-series spanning from 1988 to 2022. The dataset contains the 21 separate in-situ pigment measurements along with sampling depth and the BATS Cruise ID.</p><p>This description has been reproduced using https://www.dropbox.com/s/8kk760972lpj5sa/bats_pigments.txt?dl=0</p>
Data from: Evaluation of a pharmacist-led actionable audit and feedback intervention for improving medication safety in primary care: an interrupted time series analysis
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