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2,516 results for “Seri”
Single pixel s*t Landsat time series training data for CNN
<p>Single pixel s*t Landsat time series classification using 1D CNN</p> <p>Sep 22, 2022 update (version 2): <br> The 1D CNN classification codes are available at https://github.com/hankui/cnn_Landsat_time_series_classification_v2-Python</p> <p>The NLCD training data is available at 10.5281/zenodo.7106054</p> <p>The NLCD training data is derived from Landsat 5/7 analysis ready data (ARD) in year 2011 (as x predictor variable) and National Land Cover Database (NLCD) 2011 (as y response variable)</p> <p>The NLCD training data is distributed across Continental United States (CONUS) with 3,314,439 30m pixel locations</p> <p>The NLCD training data include (i) NLCD label with 15 classes, i.e., all NLCD classes except ice (https://www.mrlc.gov/data/legends/national-land-cover-database-class-legend-and-description)<br> (ii) year 2011 growing season Landsat ARD percentiles for Landsat 5/7 bands 2, 3, 4, 5 and 7 and for 8 band ratios derived from the five bands <br> (iii) percentiles include 10th, 20th, 25th, 30th, 35th, 40th, 50th (median), 60th, 65th, 70th, 75th, 80th, 90th so that <br> one can use 5 percentiles (10th, 25th, 50th, 75th, and 90th)<br> 7 percentiles (10th, 20th, 35th, 50th, 65th, 80th, and 90th)<br> 9 percentiles (10th, 20th, 30th, 40th, 50th, 60th, 70th, 80th, and 90th)<br> (iv) the pixel location represented in Landsat ARD tile h and v no. and the pixel i and j locations in the tile<br> (v) the no. of the cloud free observations in 2011 growing season derived for the pixel location<br> </p> <p>#*************************************************************************************************************#</p> <p>A munuscript describing how the data were derived and how the 1D CNN was adapted to the data is in review </p> <p><br> #*************************************************************************************************************#</p> <p>The codes were written in python (v3.7) and tensorflow (v2.6). </p> <p>The parameters are:</p> <p>(1) learning rate: cnn training initial learning rate 0.01 used in the paper </p> <p>(2) epoch: cnn training epochs 70 used in the paper </p> <p>(3) method: cnn training optimizer method 1: Adam method 2: dynamic learning rate used in the paper</p> <p>(4) L2: L2 regularization value; 0.001 used in paper </p> <p>(5) layer: no. of CNN layers (can be 4, 5 and 8) and 5 and 8 used in the paper</p> <p>(6) perc: training data percentages (can be 0.1, 0.5 and 0.9) tested in the paper; the evaluation is used the left 10% </p> <p>(7) gpui: which gpu process it will use (only applicable with multi-gpus) </p> <p>(8) IMG_HEIGHT: the no. of percentiles (can be 3, 5, 7 and 9) and 5, 7 and 9 used in the paper </p> <p>An example would be: </p> <p>version=7_4 </p> <p>layer=5; perc=0.1; gpui=0;IMG_HEIGHT=5</p> <p>method=0; learning_rate=0.01; epoch=10; iter=1; L2=0.001; sleep ${SLEEP}; ## Hank layer=5; perc=0.1; </p> <p>echo "python Pro_2d1d_CNN_v${version}.py ${learning_rate} ${epoch} ${method} ${L2} ${layer} ${perc} ${gpui} ${IMG_HEIGHT} "</p> <p>python Pro_2d1d_CNN_v${version}.py ${learning_rate} ${epoch} ${method} ${L2} ${layer} ${perc} ${gpui} ${IMG_HEIGHT} > layer${layer}.p${perc}.d${IMG_HEIGHT}.rate${learning_rate}.e${epoch}.L${L2}.v${version} & </p> <p><br> #*************************************************************************************************************#</p> <p>Aug 29, 2021 (version 1): <br> Training data: There are 2 input text files (csv) storing the 3,314,439 NLCD and 484,476 CDL land cover training samples:<br> NLCD training: ./NLCD/metric.ard.nlcd.Mar01.18.40.txt<br> CDL training: ./CDL/metric.ard.nlcd.Mar01.18.40.txt</p> <p>The codes and their usages are at: <br> https://github.com/hankui/cnn_Landsat_time_series_classification_v1-R<br> </p>
Library of multivariate time series
<p>A database of many different types of multivariate time series, each with between 5-25 processes and between 100-2500 observations.</p> <p>The database contains a serialized Python dictionary of 1053 datasets, where the key for the dictionary is the dataset name, and each value is another dictionary of: "data", an MxT numpy array of processes-by-observations; and "labels", a list of descriptive labels for the dataset.</p>
Text-fig. 9. Wataria kvacekii sp. nov., UF 279-24556. a: Wood ring-porous, earlywood with 2–3 rows of wide pores, vessels solitary and in radial multiples of 2, axial parenchyma scanty vasicentric and some apotracheal diffuse-in-aggregates, TS. b: Series of vessel elements with simple perforations, axial parenchyma strands adjacent to vessels, RLS. c: Alternate intervessel pitting, vessel element end walls horizontal, RLS. d: Vessel-axial parenchyma pitting similar to intervessel pitting, RLS. e, f: Rays with tile cells, storied axial parenchyma, some strands chambered crystalliferous, TLS. g: Detail of ray, TLS. h: Storied imperforate elements. Scale bars: 200 µm in a; 100 µm in b, e; 50 µm in c, d, f, h; 20 µm in g. in A Diverse Assemblage Of Late Eocene Woods From Oregon, Western Usa
Text-fig. 9. Wataria kvacekii sp. nov., UF 279-24556. a: Wood ring-porous, earlywood with 2–3 rows of wide pores, vessels solitary and in radial multiples of 2, axial parenchyma scanty vasicentric and some apotracheal diffuse-in-aggregates, TS. b: Series of vessel elements with simple perforations, axial parenchyma strands adjacent to vessels, RLS. c: Alternate intervessel pitting, vessel element end walls horizontal, RLS. d: Vessel-axial parenchyma pitting similar to intervessel pitting, RLS. e, f: Rays with tile cells, storied axial parenchyma, some strands chambered crystalliferous, TLS. g: Detail of ray, TLS. h: Storied imperforate elements. Scale bars: 200 µm in a; 100 µm in b, e; 50 µm in c, d, f, h; 20 µm in g.
Apulian Aqueduct demo site: daily time series of reservoirs inflows 2010-2019
<p>This dataset contains the time series of net inflows to the main reservoirs of Apulian aqueduct (demo site 1), computed with a mass-balance equation at a daily time step.</p> <ul> <li>Temporal coverage: 2010-2019</li> <li>Spatial coverage: Reservoirs: Conza, Locone, Monte Cotugno, Occhito, Pertusillo</li> <li>Unit of measure: m<sup>3</sup>/sec</li> </ul> <p>More information and details on the content of this dataset can be found in Project Ô Deliverable D4.1.</p>
FrenchPiezo: the French mainland groundwater level multivariate time series
<p><strong>FrenchPiezo </strong>is the French mainland multivariate time series dataset of groundwater levels (a.k.a piezometric levels) across the French mainland. The dataset contains 1026 multivariate time series, each made of 3 dimensions which are:</p> <ul> <li>the piezometric level (<strong>p</strong>),</li> <li>the precipitation (<strong>t</strong><strong>p</strong>),</li> <li>the evapotranspiration (<strong>e</strong>).</li> </ul> <p>Each time series is identified by a code (<strong>bss</strong>) which is the identifier of the piezometer used to measure the piezometric level. The measurements are sampled daily from January 2015 to January 2021, corresponding to 2221 days. The piezometric levels are collected from <a href="https://hubeau.eaufrance.fr/">Hub'Eau</a>, the French free API for accessing water data. The climate data <strong>tp </strong>and <strong>e </strong>are collected from the Copernicus ERA5 archive using their free API. These data are in the file <em>dataset_2015_2021.csv</em><strong> </strong>in which time series with more than 50 missing values have been dropped. The file <em>dataset_2015_2021_nomissing_linear.csv</em> is the same dataset in which missing values have been imputed using linear interpolation.</p> <p>In addition to the climate data, information about the nature of the soil where each time series is collected are downloaded from <a href="https://bdlisa.eaufrance.fr/decouvrir-la-bdlisa"><em>BDLISA</em></a> and are given in the file <em>dataset_stations.csv </em>. BDLISA classifies soils as hydrologic entities (EH), characterized by a set of attributes described <a href="https://www.sandre.eaufrance.fr/ftp/documents/fr/ddd/saq/2.1/sandre_dictionnaire_SAQ__2.1.pdf">here</a>: </p> <p>More information on how we created the dataset, the scripts used to collect the data, and some experiments on forecasting the future values of groundwater levels using global and local foresting methods can be found on our <a href="https://github.com/frankl1/piezoforecast">GitHub page</a>.</p>
Scaling in the Immune System and Computational Immunology: Lecture Series
<p>How different is the immune system in a human from that of a mouse? Do pathogens replicate at the same rate in different species? Answers to these questions have impact on human health since multi-host pathogens that jump from animals to humans affect millions worldwide.</p> <p>It is not known how rates of immune response and viral dynamics vary from species to species and how they depend on species body size. Metabolic scaling theory predicts that intracellular processes will be slower in larger animals since cellular metabolic rates are slower. We test how rates of pathogenesis and immune system response rates depend on species body size.</p> <p>We hypothesize that immune response rates are invariant with body size. Our work suggests how the physical architecture of the immune system and chemical signals within it may lead to nearly scale-invariant immune search and response.</p> <p>We fit mathematical models to experimental West Nile Virus (WNV, a multi-host pathogen) infection data and investigate how model parameters characterizing the pathogen and the immune response change with respect to animal mass.</p> <p>Phylogeny also affects pathogenesis and immune response. We use a hierarchical Bayesian model, that incorporates phylogeny, to test hypotheses about the role of mass and phylogeny on pathogen replication and immune response. We observe that:</p> <p><br> 1. Hierarchical models (informed by phylogeny) make more accurate predictions of experimental data and more realistic estimates of biologically relevant parameters characterizing WNV infection.</p> <p>2. Rates of WNV production decline with species body mass, modified by a phylogenetic influence.</p> <p> </p> <p>Our work is the first to systematically explore the role of host body mass in pathogenesis using mathematical models and empirical data. We investigate the complex interplay between the physical structure of the immune system and host body mass in determining immune response. The modeling strategies and tools outlined here are likely to be applicable to modeling of other multi-host pathogens. This work could also be extended to understand how drug and vaccine efficacy in humans may systematically differ from that in model organisms like mice, in which most initial experimental studies are conducted.</p> <p> </p>
DCM containing analog series
<p>The file contains 1400 analog series that consist of DCM compounds from PubChem and their bioactive analogs from ChEMBL. For all 14,796 analogs in these series, analog series ID (AS_ID), ChEMBL_ID or PubChem compound identifier (PubChem_cid) and SMILES are provided. The targets corresponding to ChEMBL compounds in the series are also provided (ChEMBL_ID_targets). In addition, DCM compounds are indicated.</p>
Collection of analog series-based (ASB) scaffolds
<p>The entire collection of 23,791 unique ASB scaffolds generated from compounds from Probes and Drugs Portal (PDP) and ChEMBL (version 23) is reported. Each ASB scaffold is provided in canonical SMILES representation, the database origin (DB_origin) is specified, and the number of analogs (#analogs) the scaffold represents reported. In addition, for each ASB scaffold from ChEMBL, unique target annotations of corresponding analogs are provided using UniProt target identifiers. For ASB scaffolds from PDP, collected compound annotations are provided. For scaffolds shared between PDP and ChEMBL the number of analogs is provided in the form 'X|Y' where 'X' and 'Y' denote the number of analogs in PDP and ChEMBL, respectively. Scaffolds are rank-ordered according to the number of analogs they represent. </p>
Collection of analog series-based (ASB) scaffolds shared between ZINC, ChEMBL, and PubChem
<p>Analog series-based (ASB) scaffolds shared between ZINC and ChEMBL (version 22), ZINC and PubChem and all the three databases are provided as three separate files. For each ASB scaffold, the SMILES representation of ZINC compounds is provided. In addition, the number of ZINC compounds, the number and the list of targets it was annotated with is reported. A README file is also given.</p>
GH7 family cellobiohydrolase MX damage series
<p>A MX damage series collected on a single crystal at 100 K of a glycoside hydrolase family 7 (GH7) cellobiohydrolase from Daphnia pulex. Data were collected at DLS beam line I02 in 2016 and have previously been deposited in the PDB with accession codes <em>5mcc, 5mcd, 5mce, 5mcf, 5mch, 5mci, 5mcj, 5mck, 5mcl, 5mcm </em>and<em> 5mcn, </em>in association with the publication: http://scripts.iucr.org/cgi-bin/paper?S1600577516016775. Please refer to the supplementary information in the linked publication for full details of the data collection and processing performed.</p> <p>The MX damage series here consists of:</p> <p>(a) a refined coordinate model for the first dose (1.11 MGy) in the series - output from REFMAC</p> <p>(b) a corresponding MTZ-format file containing calculated phase information for the refined model - output from REFMAC</p> <p>(c) a series of 11 increasing dose (1.11 - 22.7 MGy) merged MTZ-format structure amplitude files - output from CTRUNCATE</p> <p> </p>
Multisite and multispecies live fuel moisture content (LFMC) series in the French Mediterranean since 1996
<p>Here is a dataset of live fuel moisture content (LFMC, computed as the water mass over dry mass of living shoots) time series, collected in the French Mediterranean area by the French National Forest Organization (“Office National des Forêts”) for operational fire prevention. A network of 53 sites (called the "Reseau Hydrique" network) were sampled, among which 35 are geolocalized for a maximum period extending from 1996 to 2016. For each site and year available, LFMC is measured during the summer season on shrub species (between one and three species per site) at a weekly to biweekly frequency depending on site and year.</p> <p>The dataset can be used to validate or calibrate fire danger model, assess remote sensing drought indices and understand the physiological and climatic determinants of LFMC. There are 584 site*year data (a total of 22787 individual data) for several shrub species of the French Mediterranean area.</p> <p>From the raw dataset, researchers from the Ecology of Mediterranean Forest Unit at INRA (French National Institute for Research in Agronomy) of Avignon (France) have produced an improved dataset that includes corrections, outlier identifications and error estimations. Preliminary validation assessments of the data quality were also produced.</p> <p>A data paper describing in detail the method and all the modifications, error estimations and evaluations of the raw dataset that were performed is under review in Annals of Forest Science. Both the raw and improved datasets are made available on Zenodo (Cabane et al 2017, DOI 10.5281/zenodo.162978).</p> <p>The attached dataset consists of four tables:</p> <ol> <li>The first table (<em>LFMC_final_Table.csv</em>) contains the live fuel moisture content (LFMC) on a dry weight basis (see Supplementary S1 for details). These are the robust estimates of LFMC and their associated standard errors which were both estimated from raw data with the method fully described above described in a data paper under revision (Martin-StPaul <em>et al</em>., under review in Annals of Forest Science). Each row in the table describe the LFMC at a given date, for a given species and a given site. The table has eleven columns. The first eight columns indicate the site identifier (SiteCode and SiteName), the species (Species), a unique identifier for a given species at a given site (SitexSpecies), the date (Date, Year, Month, Day of Year). The last three columns are respectively the robust LFMC (labelled RobustLFMC), the standard error <em>SE</em> (labelled RobustStandErrLFMC) and the number of valid measurements that were not identified as outliers (labelled RobustNval). RobustStandErrLFMC, and that can be used to estimate confidence limits depending on the desired confidence rate.</li> </ol> <p> </p> <ol> <li>The second table (<em>RainTable.csv</em>) contains rainfall measurements. The site identifiers are given (SiteCode and SiteName) and the rainfall amount (rainfall) corresponding to rainfall occurring between the day of year of the previous measurement (PreviousDoy) and the day of year when the measurement was performed (Doy). The last column enables to identify the doubtful measurements (RainFlag = 1), when the discharge of the gauge during the previous measurement was uncertain.</li> </ol> <p> </p> <p> </p> <ol> <li>The fourth table (<em>InfoSite_ReseauHydrique.csv</em>) contains a basic description of each site. It includes the identifier of the site (SiteCode and SiteName), the coordinates of the site in WGS84 (Longitude and Latitude), a flag indicating whether the site is still active (1= active, 0= inactive), the names of measured species (SpeciesName1 and up to SpeciesName3), the first and last year of measurement, as well as the number of measurement year available, for each species (StartYear, EndYear, NbYears).</li> </ol> <p> </p> <ol> <li>The third table contains raw data as produced by the French National Forest Organization (<em>LFMC_raw_Table.csv</em>). The first twelve columns indicate site name, species name, and date, as in the first table. The six following columns indicate individual LFMC values (LFMC1 to LFMC5), and the mean LFMC value (FFSLFMC) released by the French Forest Service. The last six columns correspond to flags identifying outliers (LFMC1Flag to LFMC5Flag). Flags were attributed either manually or automatically (see Martin-StPaul <em>et al</em>., under review in Annals of Forest Science). Missing values (e.g. following an unforcasted rain event, see Methods) were represented by the symbol “NA“ (Not Available).</li> </ol> <p>Note that the initiative was funded by a French organization dedicated the protection of the Mediterranean forest (The "Délégation à la Protection de la Forêt Méditerranéenne") and the raw dataset is available on a French website (http://www.reseau-hydrique.org/). However the raw dataset is not fully adapted to scientific purposes for several reasons. The dataset is not referenced (<em>i.e.</em> does not have a DOI) and its description is in French. In addition, the labels of sampling sites have evolved over time and some species were given a vernacular name. Finally, raw data are expressed on fresh mass basis (instead of dry mass as generally done in scientific studies) and present some outliers, duplications and inconsistencies. Additionally, uncertainties were not provided in the raw datasets. This is why INRA researchers recommend the usage of the improved dataset. In the forthcoming month, additional data regarding the environmental description, the ecology and history of the sites will be provided.</p> <p> </p> <p>Martin-StPaul, N; Pimont, F; Dupuy JL; Rigolot E; Ruffault J; Fargeon H; Cabane E; Duché Y; Savazzi R; Toutchkov M. Multisite and multispecies live fuel moisture content (LFMC) series in the French Mediterranean area since 1996. Under revision in Annals of Forest Science.</p> <p> </p>
Analog Series of Compounds with High Frequency of Activity in Screening Assays
<p>A set of 6941 analog series and associated data are provided. These series exclusively consist of compounds that are most frequently active across public screening assays. </p>
FIGURE 3. Cervical vertebral series from Callawayasaurus columbensis UCMP 38349 in Congenital and late onset vertebral fusions in long necked plesiosaurs: The first report of spondylosis deformans in Sauropterygians
FIGURE 3. Cervical vertebral series from Callawayasaurus columbensis UCMP 38349 in ventral aspect. 1. Vertebrae 23,24,25, anterior end, to right. Scale bar, 5 cm. 2. Enlargement of osteophytic bridging between vertebrae 23-24, 24- 25. Abbreviations: ns, neural spine; cr, cervical rib. Scale bar equals 5 cm.
FIGURE 4. Cervical vertebral series from Callawayasaurus columbensis UCMP 38349 in Congenital and late onset vertebral fusions in long necked plesiosaurs: The first report of spondylosis deformans in Sauropterygians
FIGURE 4. Cervical vertebral series from Callawayasaurus columbensis UCMP 38349 in ventral aspect. 1. Vertebrae 15-18, anterior end to right. Scale bar equals 5 cm. 2. Enlargement showing claw-like ventral expansion projecting antero-posteriorly between vertebrae 16-17, 17-18. 3. Ventral view of cervical series from UCMP 38349 without pathologies, vertebrae 32-35. Abbreviations: ns, neural spine; cr, cervical rib; ve, pathological ventral expansion. Scale bar equals 5 cm.
DEEPICE Stories Videos series - Insight into Ice & Climate - Videos with French subtitles
<p><em>DEEPICE Stories – Insights into Ice & Climate </em>is a series of 15 videos<em> </em>created in collaboration with the 15 PhD students of the European research project <a href="https://deepice.cnrs.fr/">DEEPICE</a>. These 3-minute educational video clips give an overview of scientific research on ice cores. <a href="https://deepice.cnrs.fr">https://deepice.cnrs.fr</a></p> <p><u>Credits</u></p> <p>Writing & presentation: Geunwoo Lee, Hanne Notø, Eirini Malegiannaki, Piers Larkman, Miguel Angel Sanchez Moreno, Lison Soussaintjean, Florian Painer, Niklas Kappelt, Lisa Ardoin, Inès Ollivier, Romilly Harris Stuart, Fyntan Shaw, Qinggang Gao, Ailsa Chung, Daniel Gunning</p> <p>Coordination : Marie Kazeroni (LSCE-CNRS)</p> <p>Direction: Dorothée Adam-Mazard (Inuaprod) & Marie Kazeroni (LSCE-CNRS)</p> <p>Editing: Thomas d’Aram</p> <p>Motion Design: Pauline Fuchs</p> <p>Co-production: DEEPICE & Inuaprod</p> <p>___________________________________________________________________________________________________________________</p> <p><em><span>DEEPICE Stories - Insights into Ice & Climate </span></em><span>est une série de 15 vidéos créées en collaboration avec les 15 doctorants du projet de recherche européen <a href="https://deepice.cnrs.fr/">DEEPICE</a>. Ces capsules éducatives de 3 minutes donnent un aperçu de la recherche scientifique sur les carottes de glace. <a href="https://deepice.cnrs.fr">https://deepice.cnrs.fr</a></span></p> <p>Crédits</p> <p><span><span>Ecriture et présentation : Geunwoo Lee, Hanne Notø, Eirini Malegiannaki, Piers Larkman, Miguel Angel Sanchez Moreno, Lison Soussaintjean, Florian Painer, Niklas Kappelt, Lisa Ardoin, Inès Ollivier, Romilly Harris Stuart, Fyntan Shaw, Qinggang Gao, Ailsa Chung, Daniel Gunning<br></span></span></p> <p><span><span>Coordination : Marie Kazeroni (LSCE-CNRS)</span></span></p> <p><span><span>Direction : Dorothée Adam-Mazard (Inuaprod) & Marie Kazeroni (LSCE-CNRS)</span></span></p> <p><span><span>Montage et édition : Thomas d’Aram</span></span></p> <p><span><span>Motion Design : Pauline Fuchs</span></span></p> <p><span><span>Co-production: DEEPICE & Inuaprod</span></span></p>
Train and test datasets used for the paper "Neural network time-series classifiers for gravitational-wave searches in single-detector periods"
<p>This repository contains the datasets used for training and testing during the work discussed in the paper "<a href="https://iopscience.iop.org/article/10.1088/1361-6382/ad40f0" target="_blank" rel="noopener">Neural network time-series classifiers for gravitational-wave searches in single-detector periods</a>". Please refer to this paper for more details on how the dataset was produced and cite it if you use these data:</p> <p><em>A. Trovato et al "Neural network time-series classifiers for gravitational-wave searches in single-detector periods", Class. Quant. Grav. 2024 DOI 10.1088/1361-6382/ad40f0.</em></p> <p>In this repository you will find six files in format npz, three of which refer to the test dataset and three to the train dataset. Each file name is of the type {label}_{train or test}.npz where "label" can be "glitch", "noise" or "signal", while the second part of the name indicates whether the file was used for training or testing.</p> <p>Each file is a collection of numpy arrays so it should be read with python. It contains 3 numpy arrays: 'X', 'Y' and 'metadata'. 'X' is a matrix containing 1-second segments of data sampled at 2048 Hz of the LIGO-Livingston detector, so it has shape: (number of samples, 2048). 'Y' contains the label for each segment, which is 0 for noise, 1 for signal and 2 for glitch, so it has shape: (number of samples,). In this case, the information on 'Y' is redundant since it's given directly by the filename. The 'metadata' matrix contains 17 metadata for each sample only for the case of signals, for glitch or noise it contains just 17 zeros for each sample. The shape of 'metadata' is thus: (number of samples, 17). For the signal files, for each sample the metadata is an array with these components:</p> <ol> <li>GPS start of the file from which this segment comes</li> <li>starting GPS time of this segment</li> <li>duration of the segment [s]</li> <li>mass1 [solar masses]</li> <li>mass2 [solar masses]</li> <li>spin1z</li> <li>spin2z</li> <li>inclination [radians]</li> <li>coalescence phase [radians]</li> <li>distance [Mpc]</li> <li>right_ascension [radians]</li> <li>declination [radians]</li> <li>polarization [radians]</li> <li>SNR (signal to noise ratio)</li> <li>shift of the signal w.r.t. the timeseries [s]</li> <li>length of the signal [s]</li> <li>fraction of the signal contained in the time window</li> </ol> <p>Number of samples:</p> <ul> <li>80000 for the file glitch_test.npz</li> <li>69998 for the file glitch_train.npz</li> <li>500000 for the file noise_test.npz</li> <li>250000 for the file noise_train.npz</li> <li>500000 for the file signal_test.npz</li> <li>250000 for the file signal_train.npz</li> </ul> <p>An example of few lines of python code to read each file is:</p> <pre><code>import numpy as np f = np.load("filename.npz") X = f['X'] Y = f['Y'] m = f['metadata'] </code></pre> <p>For the preparation of these data, we acknowledge the use of the following software packages: GWpy [1], PyCBC [2] and LALSuite [3]. </p> <p>This research has made use of data or software obtained from the Gravitational Wave Open Science Center (<a href="https://gwosc.org/" target="_blank" rel="noopener">gwosc.org</a>), a service of the LIGO Scientific Collaboration, the Virgo Collaboration, and KAGRA. This material is based upon work supported by NSF's LIGO Laboratory which is a major facility fully funded by the National Science Foundation, as well as the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. Virgo is funded, through the European Gravitational Observatory (EGO), by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale di Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by institutions from Belgium, Germany, Greece, Hungary, Ireland, Japan, Monaco, Poland, Portugal, Spain. KAGRA is supported by Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan Society for the Promotion of Science (JSPS) in Japan; National Research Foundation (NRF) and Ministry of Science and ICT (MSIT) in Korea; Academia Sinica (AS) and National Science and Technology Council (NSTC) in Taiwan.</p> <p>[1] https://gwpy.github.io<br>[2] https://pycbc.org<br>[3] https://lscsoft.docs.ligo.org/lalsuite</p>
Historical time-series reconstruction benchmark dataset of Landsat bi-monthly aggregates from GLAD ARD-2 at 30-m resolution with stratified sampling based on ESA CCI
<h2>Description</h2> <p>Historical time-series reconstruction benchmark dataset presented here is designed for evaluating and comparing the performance of time series reconstruction methods in the context of land cover change detection. The dataset is based on the European Space Agency Climate Change Initiative (ESA CCI) land cover dataset, which has been aggregated into 18 classes to facilitate analysis. The dataset includes information on land cover dynamics from 2000 to 2020, focusing on identifying and characterizing changes in land cover over time.</p> <h3><strong>Data Collection and Processing:</strong></h3> <p>The dataset is derived from the ESA CCI land cover dataset, which provides information on land cover classes at a global scale. The original dataset, containing 37 land cover classes, was aggregated into 18 classes based on similarity. Pixels with stable land cover over the study period and pixels with one or multiple land cover changes were identified and grouped into strata for sampling purposes.</p> <p>Sampling points were selected using a stratified sampling design, ensuring representation across different land cover classes and change scenarios. Approximately 2600 points were selected from each stratum, resulting in a total of 51,978 sampling points. The selected points were uniformly distributed along the strata, with spatial variations accounted for.</p> <p>Bimonthly time series data were extracted for each sampling point from 1997 to 2022, capturing temporal dynamics in land cover. Artificial gaps were introduced into the time series data to simulate real-world data loss, allowing for the evaluation of time series reconstruction methods under varying gap densities.</p> <p>The time series values were extracted from Landsat GLAD imagery using the specified spectral bands, including blue, green, red, NIR, SWIR1, SWIR2, and thermal bands. Additionally, a clear quality band was also extracted.</p> <h3>Data Details</h3> <ul> <li><strong>Time Period:</strong> 1997-01-01 to 2022-12-31</li> <li><strong>Type of Data: </strong>R data frame / Geopackage points.</li> <li><strong>Collection/Derivation:</strong> Derived from Landsat ARD v2, processed with Scikit-map.</li> <li><strong>Coordinate Reference System:</strong> EPSG:4326</li> <li><strong>Bounding Box:</strong> All the globe</li> <li><strong>File Format:</strong> RDS</li> </ul> <p> </p> <h3><strong>Reclassified Classes of ESA CCI Land Cover Dataset</strong></h3> <table> <tbody> <tr> <td> <div> <div> <p><strong>Aggregated Class Code</strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Aggregated Class Label</strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Original ESA CCI Classes</strong></p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>10</p> </div> </div> </td> <td> <div> <div> <p>Cropland rainfed</p> </div> </div> </td> <td> <div> <div> <p>10, 11, 12</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>30</p> </div> </div> </td> <td> <div> <div> <p>Mosaic cropland | natural vegetation</p> </div> </div> </td> <td> <div> <div> <p>30, 40</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>50</p> </div> </div> </td> <td> <div> <div> <p>Tree cover broadleaved evergreen</p> </div> </div> </td> <td> <div> <div> <p>50</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>60</p> </div> </div> </td> <td> <div> <div> <p>Tree cover broadleaved deciduous</p> </div> </div> </td> <td> <div> <div> <p>60, 61, 62</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>70</p> </div> </div> </td> <td> <div> <div> <p>Tree cover needleleaved evergreen</p> </div> </div> </td> <td> <div> <div> <p>70, 71, 72</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>80</p> </div> </div> </td> <td> <div> <div> <p>Tree cover needleleaved deciduous</p> </div> </div> </td> <td> <div> <div> <p>80, 81, 82</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>90</p> </div> </div> </td> <td> <div> <div> <p>Tree cover mixed leaf type</p> </div> </div> </td> <td> <div> <div> <p>90</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>100</p> </div> </div> </td> <td> <div> <div> <p>Mosaic tree and shrub | herbaceous cover</p> </div> </div> </td> <td> <div> <div> <p>100, 110</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>120</p> </div> </div> </td> <td> <div> <div> <p>Shrubland</p> </div> </div> </td> <td> <div> <div> <p>120, 121, 122</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>150</p> </div> </div> </td> <td> <div> <div> <p>Sparse vegetation</p> </div> </div> </td> <td> <div> <div> <p>150, 151, 152, 153</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>160</p> </div> </div> </td> <td> <div> <div> <p>Tree cover flooded</p> </div> </div> </td> <td> <div> <div> <p>160, 170</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>180</p> </div> </div> </td> <td> <div> <div> <p>Shrub or herbaceous cover flooded</p> </div> </div> </td> <td> <div> <div> <p>180</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>200</p> </div> </div> </td> <td> <div> <div> <p>Bare areas</p> </div> </div> </td> <td> <div> <div> <p>200, 201, 202</p> </div> </div> </td> </tr> </tbody> </table> <p>In the table, each row represents a reclassified land cover class, identified by a unique code. The 'Original ESA CCI Classes' column lists the specific land cover classes from the European Space Agency Climate Change Initiative dataset that are grouped together to form each broader category. Note that land cover classes not listed in this table were retained in their original value and were not reclassified.</p> <h3><strong>File Format</strong></h3> <p>The dataset comprises observations spanning from January 1997 to November 2022, capturing data for 51,978 samples.</p> <ul> <li>blue.rds: Time series data for the blue spectral band.</li> <li>green.rds: Time series data for the green spectral band.</li> <li>red.rds: Time series data for the red spectral band.</li> <li>nir.rds: Time series data for the near-infrared (NIR) spectral band.</li> <li>swir1.rds: Time series data for the shortwave infrared 1 (SWIR1) spectral band.</li> <li>swir2.rds: Time series data for the shortwave infrared 2 (SWIR2) spectral band.</li> <li>thermal.rds: Time series data for the thermal infrared band.</li> <li>clear.rds: Time series data for the clear quality band, used for masking out cloudy observations.</li> </ul> <p>How open the files in R:</p> <p><code>blue <- readRDS("blue.rds")</code></p> <p>To open the files in Python, you need to the <code>pyreadr</code> library:</p> <p><code>import pyreadr</code><br><code>blue = pyreadr.read_r('blue.rds')</code></p> <p> </p>
Emily Dickinson's Poems: Series #1, #2, and #3
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Concept list of Vietnamese based on the Intercontinental Dictionary Series (IDS)
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Station M time series study (NE Pacific) CTD data (cruises 2006-2022, surface to 4000 m depth)
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ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.