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1,118 results for “Time series”
Global forest reference set with time series annual change information
<p><strong>Brief introduction of the reference set:</strong></p> <ul> <li>The initial sample points were stratified. The ratio of non-forest, forest and forest change stratum area is about 67:27:6. In order to increase the number of forest and forest change sample points, the ration of forest sample, forest change sample and non-forest sample points is roughly 4:1:1. The information of stratum identifier is provided in this sample set. The study generated 12925 initial sample points which were further reduced to 10339 points (6252 persisting forest sample points, 2049 change sample points and 2038 persisting non-forest sample points). The details are shown in Table 1. </li> </ul> <table> <caption>Table 1. Initial and finial sample size in each stratum</caption> <tbody> <tr> <td><strong>Stratum name</strong></td> <td><strong>Percent area of each stratum</strong></td> <td><strong>Initial sample size</strong></td> <td><strong>Final sample size</strong></td> <td><strong>Eliminated sample size</strong></td> </tr> <tr> <td>Forest</td> <td>27%</td> <td>8332</td> <td>6252</td> <td>1644</td> </tr> <tr> <td>Non-forest</td> <td>67%</td> <td>1892</td> <td>2038</td> <td>207</td> </tr> <tr> <td>Forest change</td> <td>6%</td> <td>2701</td> <td>2049</td> <td>735</td> </tr> <tr> <td>Sum</td> <td>100%</td> <td>12925</td> <td>10339</td> <td>2586</td> </tr> </tbody> </table> <p> </p> <ul> <li>The sample set will be provided in shapefile version. (The eliminated sample points will not be provided.) In this version, the provided attributions are shown in Table 2.</li> </ul> <p> </p> <table> <caption>Table 2. Details of provided attributions in this version</caption> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>per_f</td> <td>unchanged forest (0: No 1: Yes)</td> </tr> <tr> <td>per_nonf</td> <td>unchanged non-forest (0: No 1: Yes)</td> </tr> <tr> <td>change</td> <td>forest change (0: No 1: Yes)</td> </tr> <tr> <td>loss</td> <td>forest loss times</td> </tr> <tr> <td>gain</td> <td>forest gain times</td> </tr> <tr> <td>times</td> <td>change times (gain + loss)</td> </tr> <tr> <td>changeinfo</td> <td>year and order of change</td> </tr> <tr> <td>initial_s</td> <td>initial stratum identifier: change/perf (persisting forest)/nonf (persisting non-forest)</td> </tr> </tbody> </table> <p> </p> <ul> <li>This reference set will be updated regularly. Now it's version 1.1 (from 2000 to 2020).</li> </ul> <p> </p> <p><strong>NOTES:</strong></p> <ol> <li>Please be cautious while using the sample points with multiple change times. </li> <li>We encourage people to send us feedback if you found some mistakes while using this sample set via email.</li> <li>Welcome discussions around potential collaborations. 【You can email Jing (guoj15@tsinghua.org.cn).】</li> </ol> <p> </p> <p><strong>Citation:</strong></p> <p>Please cite the dataset including the version number and the following paper when using this data: </p> <p>Jing Guo, Zhiliang Zhu & Peng Gong (2022) A global forest reference set with time series annual change information from 2000 to 2020, International Journal of Remote Sensing, 43:9, 3152-3162, DOI: <a href="https://doi.org/10.1080/01431161.2022.2088256">10.1080/01431161.2022.2088256</a></p>
Sentinel-1 InSAR time-series and velocity map over the Bay Area (Descending track 42, 2015-2020)
<p>Supplemental material for <em>"Spatiotemporal variations of surface deformation, shallow creep rate, and slip partitioning between the San Andreas and southern Calaveras Fault"</em> at JGR-Solid Earth</p> <p>Citation: <strong>Li, Y</strong>., Bürgmann, R., & Taira, T. (2023). Spatiotemporal variations of surface deformation, shallow creep rate, and slip partitioning between the San Andreas and southern Calaveras Fault. <em>Journal of Geophysical Research: Solid Earth</em>, 128, e2022JB025363. <a href="https://doi.org/10.1029/2022JB025363">https://doi.org/10.1029/2022JB025363</a></p>
Wind and solar capacity factor time series by year and grid cell over the contiguous U.S.
<p>This data set contains hourly capacity factor time series of wind and solar resources over the contiguous U.S.</p> <p> </p> <p>The included time series cover four individual years and 2,586 grid cells. The years range from 2016 to 2019. The grid cells correspond to the grid cells of the NASA's MERRA-2 reanalysis data set into which the contiguous U.S. is subdivided. The grid cells have a spatial resolution of 0.5° latitude x 0.625° longitude with dimensions ranging from about 55 km x 45 km to 55 km x 62 km.</p> <p> </p> <p>This data set is used to generate the results of the following journal article:</p> <p>Enrico G. A. Antonini, Tyler H. Ruggles, David J. Farnham, Ken Caldeira, "The quantity-quality transition in the value of expanding wind and solar power generation", iScience 25 (4), 104140, 2022.</p> <p> </p> <p>Code and instructions required to reproduce the results reported in the above paper are available in the GitHub repositories at <a href="https://github.com/eantonini/Distributed_wind_and_solar_generation">https://github.com/eantonini/Distributed_wind_and_solar_generation</a> and <a href="https://github.com/carnegie/MEM_public/tree/Antonini_et_al_2022">https://github.com/carnegie/MEM_public/tree/Antonini_et_al_2022</a>.</p>
Data from: A 26-year time series of mortality and growth of the Pacific oyster C. gigas recorded along French coasts
<p><strong>Contents:</strong> database of oyster growth (<em>i.e., </em>the changes in mass over time) and mortality along French coasts since 1993. To build this database, we took advantage of the Pacific oyster production monitoring network coordinated by IFREMER (the French Research Institute for the Exploitation of the Sea). This network monitors the growth and mortality of spat (less than one-year-old individuals) and half-grown (between one and two-year-old individuals) <em>Crassostrea gigas</em> oysters since 1993. As the number of sites monitored over the years varied, we focused on 13 sites that were almost continuously monitored during this period. For these locations, we modeled growth and cumulative mortality for spat and half-grown oysters as a function of time, to cope with changes in data acquisition frequency, and produced standardized growth and cumulative mortality indicators to improve data usability. Code to reproduce these analyses are archived here, as well as figures included in the companion data paper: "A 26-year time series of mortality and growth of the Pacific oyster <em>C. gigas</em> recorded along French coasts".</p> <p><strong>Sampling protocol: </strong>in the oyster production monitoring network, oysters were mainly reared in plastic meshed bags fixed on iron tables, mimicking the oyster farmers practices. After their deployment at the beginning of the campaign (seeding dates from February to April depending on the year), growth and mortality were longitudinally monitored yearly. At each sampling date, local operators carefully emptied each bag in separate baskets, counted the dead individuals and alive ones, and removed the dead individuals. Then local operators weighed all alive individuals in each basket (mass taken at the bag level, protocol mainly used between 1993 and 1998 and since 2004) and/or collected 30 individuals to individually weigh them in the laboratory (mass taken at the individual level, protocol used between 1995 and 2010 for spat and since 1996 for half-grown oysters).</p> <p><strong>Data:</strong></p> <ul> <li>AllDataresco. csv is a csv file containing the raw observations of oyster growth and mortality recorded within the REMORA, RESCO and ECOSCOPA programs. This data set is a modified extraction (carried out on 2021-07-20) of the RESCO REMORA Database (https://doi.org/10.17882/53007) available in SEANOE, an academic publisher or marine research data. The table contains 571101 rows and 18 columns. Description of columns: <ul> <li>program: the name of the program. Blank cells indicate that this information was not available.</li> <li>mnemonic_site: the mnemonic is a unique identifier of the site and is constructed as follows: code of the marine area - P (for monitoring point) - order number of the monitoring location in the marine area. For example, 014-P-055.</li> <li>site: the name of the site.</li> <li>class_age: the age class of the oyster: N0 (spat), J1 (half-grown) or A2 (commercial size). Blank cells indicate that this information was not available.</li> <li>ploidy: the ploidy of the oysters: diploïdes or triploïdes (in English: diploid or triploid). Blank cells indicate that this information was not available.</li> <li>date: the date of data collection (format DD/MM/YYYY).</li> <li>mnemonic_date: mnemonic of the visit. The name of the quarterly operation (P0, P1, P2, P3 or RF: last data collection). For intermediate operations, we use the previous name of the operation followed by an underscore and the number of the week. For example, data collection on 2019-05-06 corresponds to P1_S19. Biométrie initiale (in English: initial biometrics) is equivalent to P0 (first data collected during the campaign).</li> <li>param: the name of the measured parameter: Nombre d'individus morts, Nombre d'individus vivants, Poids de l'individu or Poids total des individus vivants (in English: number of dead oysters, number of alive oysters, mass of the individual and total mass of alive individuals).</li> <li>code_param: code of the measured parameter. INDVVIVNB = number of alive oysters, INDVMORNB = number of dead oysters, INDVPOID = mass of the individual, TOTVIVPOI = total mass of alive individuals (i.e., the mass of the bag).</li> <li>unit_measure: the unit of measurement: Gramme or Unité de dénombrement (d'individus, de cellules, ...)</li> <li>fraction: either the measure was made at the bag level on which case the fraction is "Sans objet" = Not applicable or the measure was made at the individual level (code_param = INDVPOID), in which case the fraction indicates the part of the oyster that was measured: Chair totale égouttée or coquille (in English: total flesh drained or shell).</li> <li>method: the method used to obtain the data. For the number of alive and dead oysters (code_param = INDVVIVNB and INDMORNB), the method is comptage macroscopique (in English: macroscopic count). For mass taken at the individual level (code_param = INDVPOID), the method is Pesée après lyophilisation or Pesée simple sans préparation (in English: weighing without preparation or weighing after lyophilization).</li> <li>id_ind: the id of the individual oyster when code_param is INDVPOI or the id of the bag when code_param is INDVVIVNB, INDVPOID and TOTVIVPOI.</li> <li>value: numeric value of the measurement.</li> <li>mnemonic_sampling: This is a concatenated field. Its coding is not consistent throughout the dataset. Indeed, it is sometimes composed of the first letter of the program name attached to 2 numbers indicating the year of data collection and the age class (gj: spat, ga: half-grown or commercial size oysters) - 2 letters indicating the region attached to a 4-character site identifier- mnemonic passage. For example, R05gj-NOBV02-P0 corresponds to data collected in the program REMORA in 2005 on gigas spat (gj) in Normandy (NO) in the site Géfosse 02 (BV02) in the 1<sup>st</sup> quarter (P0). Other times the mnemonic_prelevement is composed of the first two letters of the program name attached to 2 numbers indicating the year of data collection _ the age class (GJ: spat, GA18: half-grown, GA30: commercial size oysters) attached to the origin of the initial spat group (this information is not always indicated) (CN + number: identifier of wild-caught site, ET + character: identifier of the hatchery, NSI: Argenton hatchery via a standardized protocol) _ a 4-character identifier for the site. For example, RE12_GJET2_BV02 corresponds to data collected in the program REMORA in 2012 (RE12) on gigas spat born in hatchery 2 (GJET2) in the site Géfosse 02 (BV02). Finally, mnemonic_prelevement is sometimes: Biométrie initiale (initial biometrics), Biométrie initiale 6 mois (initial biometrics of spat), Biométrie initiale 18 mois and Biométrie initiale adulte (both correspond to initial biometrics of half-grown oysters), Biométrie initiale 30 mois (initial biometrics of commercial size oysters), Biométrie initiale NSI (initial biometrics of spat batch produced in Argenton Ifremer hatchery via a standardized protocol).</li> <li>long: The longitudinal coordinate of the site given in decimal in the WGS 84 system.</li> <li>lat: The latitudinal coordinate of the site given in decimal in the WGS 84 system.</li> <li>pop_init_batch: this is a concatenated field. It is composed of the two first letters of the name of the program name attached to 2 numbers indicating the year of data collection _ the age class code (GJ: gigas spat, GA: gigas half-grown, GA30: gigas commercial size) _ the origin of the initial spat group (CN: wild-caught, ET: hatchery, NSI: Argenton hatchery) attached to two numbers indicating the year of birth of the initial spat group _ the birth place of the initial spat group (this one is optional). For example, RE00_GJ_CN99_AR corresponds to data collected in the program REMORA in 2000 (RE00) on spat oysters (GJ) born in 1999 and wild-caught (CN99) in the Bay of Arcachon (AR). Blank cells indicate that this information was not available.</li> </ul> </li> </ul> <p> </p> <ul> <li>sites.csv is a csv file of 7 columns and 13 rows containing information about the 13 sites. Description of the columns found in the data set: <ul> <li>num: a unique identifier for each site. Ranges between 1 and 13.</li> <li>site: the abbreviated name of the site.</li> <li>Name: the full name of the site.</li> <li>zone_fr: the French name of the zone where data collection took place.</li> <li>zone_en: the English name of the zone where data collection took place.</li> <li>lat: the latitudinal coordinate of the site given in decimal in the WGS 84 system.</li> <li>long: the longitudinal coordinate of the site given in decimal in the WGS 84 system.</li> </ul> </li> </ul> <p> </p> <ul> <li>DataResco_clean.csv is the curated data set of oyster growth and mortality (csv file). The table contains 5178 rows and 13 columns. Each row corresponds to the mean cumulative mortality and mean mass of oysters for a specific date x site x age class combination. This is the data set we used to fit logistic and Gompertz models to describe mean mass and cumulative mortality at time <em>t</em>. Description of the columns found in the data set: <ul> <li>num, site, name, zone_en, lat, long: see the description above for the data set sites.csv.</li> <li>campaign: the year of data collection. Ranges between 1993 and 2018.</li> <li>class_age: the age class of the oyster (i.e. spat: N0 or half-grown: J1).</li> <li>batch: the identifier of the batch (group of oysters born from the same reproductive event, having experienced strictly the same zootechnical route). It is a field that concatenates the campaign, the age class of oysters (spat: N0 or half-grown: J1), the origin of the initial spatgroup (wild-caught: CAPT or Ifremer hatchery: ECLO), ploidy (diploid: 2n) and birthplace of the original spatgroup (AR: Bay of Arcachon or E4: Ifremer hatchery of Argenton).</li> <li>date: the day of data collection (format YYYY-MM-DD).</li> <li>DOY: the day of the year (count of days since the beginning of the year). It ranges between 46 and 354.</li> <li>mean_CM: the mean cumulative mortality of oysters. It ranges between 0 and 0.956 (<em>i.e.</em>, between 0–95.6%). We first calculated the cumulative mortality for each bag x date x site x age class combination according to the following formula: CM<em><sub>t</sub></em> = 1 – ((1 – CM<em><sub>t-1</sub></em>) × (1 – IM<em><sub>t</sub></em>)). CM<em><sub>t</sub> </em>= Cumulative mortality at time <em>t</em>; CM<em><sub>t-1 </sub></em>= Cumulative mortality at time <em>t</em>-1; IM<em><sub>t</sub></em> = Mortality rate at time <em>t</em>. IM<em><sub>t</sub></em> was obtained by dividing the number of dead oysters by the sum of alive and dead oysters at time <em>t</em>. When several bags were followed, we then averaged the cumulative mortality per date x site x age class combinations. IM<sub>t</sub> was obtained by dividing the number of dead oysters by the sum of alive and dead oysters at time <em>t</em>. When several bags were followed, we then averaged the cumulative mortality by date x site x age class combination. NA values indicate that this information was not available.</li> <li>mean_mass: The mean mass of oysters in grams. It ranges between 0.28 and 122.51 g. For mass data collected until 2008, we calculated the mean of the individual mass per date x site x age class combination by averaging the mass of the individuals. In other cases (mass data collected since 2009), we calculated the mean mass of individuals for each bag x date x site x class age combination by dividing the total mass of living oysters by the number of living individuals and then averaged data by date x site x age class combination. The mean mass is thus the mean of the individual mass until 2008 and the mean mass of individuals since 2009. NA values indicate that this information was not available.</li> </ul> </li> </ul> <p> </p> <ul> <li>data/clean/DataResco_predicted.csv is a csv file containing the cumulative mortality and mass of oysters predicted by the best sigmoid model. The table contains 148239 rows and 11 columns. Each row corresponds to the cumulative mortality and mass predicted for a specific day x campaign x site x age class combination. <ul> <li>num, site, name, zone_en, lat, long: see the description above for the data set sites.csv.</li> <li>campaign: the year of the data prediction. Ranges between 1993 and 2018.</li> <li>classe_age: the age class of the oyster (<em>i.e.,</em> spat: N0 or half-grown: J1).</li> <li>DOY: the day of the year (count of days since the beginning of the year). It ranges from 65 (median day of seeding date) to 337 (median day of the end of the monitoring).</li> <li>CM_pred: cumulative mortality predicted by the best model (<em>i.e.,</em> Gompertz model).</li> <li>mass_pred: mass predicted (in grams) by the best model (<em>i.e.,</em> logistic model).</li> </ul> </li> </ul> <p><strong>Code: </strong></p> <ul> <li>0_map_sampling_location.R contains the code to recreate the map of the sampling sites</li> <li>1_cleaning_data.R contains the code for data cleaning. This script also computes the mean cumulative mortality and mean mass of oysters per date x site x class age combination (DataResco_clean.csv)</li> <li>2_analysis_sigmoide.Rmd does the logistic and Gompertz models of the mean cumulative mortality and mean mass for spat and half-grown oysters. This script computes the cumulative mortality and mass of oysters predicted by the best sigmoid model (DataResco_predicted.csv).</li> </ul> <p>Contacts: for questions, please contact: elodie.fleury@ifremer.fr or Julien.normand@ifremer.fr</p>
Data from: Girth increment changes in response to soil water availability in lowland dipterocarp forest in Borneo: an individualistic time-series analysis
<p><span>Time-series data offer a way of investigating the causes driving ecological processes as phenomena. To test for possible differences in water relations between species of different forest structural guilds at Danum (Sabah, NE Borneo), daily stem girth increments (gthi), of 18 trees across six species were regressed individually on soil moisture potential (SMP) and temperature (TEMP), accounting for temporal autocorrelation (in GLS-arima models), and compared between a wet and a dry period. The best-fitting significant variables were SMP the day before and TEMP the same day. The first resulted in a mix of positive and negative coefficients, the second largely positive ones. An adjustment for dry-period showers was applied. Interactions were stronger in dry than wet period. Negative relationships for overstorey trees can be interpreted in a reversed causal sense: fast transporting stems depleted soil water and lowered SMP. Positive relationships for understorey trees meant they took up most water at high SMP. The unexpected negative relationships for these small trees may have been due to their roots accessing deeper water supplies (if SMP was inversely related to that of the surface layer), and this was influenced by competition with larger neighbour trees. A tree-soil flux dynamics manifold may have been operating. Patterns of mean diurnal girth variation were more consistent among species, and time-series coefficients were negatively related to their maxima. Expected differences in response to SMP in the wet and dry periods did not clearly support a previous hypothesis differentiating drought and non-drought tolerant understorey guilds. Trees within species showed highly individual responses when tree size was standardized. Data on individual root systems and SMP at several depths are needed to get closer to the mechanisms that underlie the tree-soil water phenomena in these tropical forests. Neighborhood stochasticity importantly creates varying local environments experienced by individual trees.</span></p>
Towards understanding the importance of time-series features in automated algorithm performance prediction
<p><strong>merged_feature_importance.csv</strong> - CSV with feature importance values with different meta-models, forecasting algorithms, and feature importance methods computed on 30 different train/test splits.</p> <p><strong>Catch22.csv</strong> - Catch22 features (raw time-series)</p> <p><strong>Catch22Log.csv</strong> - Catch22 features (log time-series)</p> <p><strong>Catch22Diff.csv</strong> - Catch22 features (differenced time-series)</p> <p><strong>TSFresh.csv</strong> - TSFresh features (raw time-series)</p> <p><strong>TSFreshLog.csv</strong> - TSFresh features (log time-series)</p> <p><strong>TSFreshDiff.csv</strong> - TSFresh features (differenced time-series)</p> <p><strong>mape.csv</strong> - sMAPE performance for all forecasting algoirthms</p>
3D+time nuclei tracking dataset of confocal fluorescence microscopy time series of C. elegans embryos
<p>The dataset consists of 3 confocal microscopy time series of <em>C. elegans</em> embryos, fully tracked with StarryNite followed by manual curation</p> <ul> <li>3 raw time-series and the corresponding tracks/lineage trees</li> <li>temporal resolution; 75s</li> <li>temporal extent: 400 frames, tracked for at least 370 frames</li> <li>spatial resolution (zyx): 0.75 x 0.15 x 0.15 μm</li> <li>spatial extent (zyx):/ 41 x 512 x 512px</li> <li>Microscope: Zeiss Axio Observer.Z1</li> </ul> <p>The annotations were created using the method described in:</p> <p><em> Santella, A., Du, Z. & Bao, Z. A semi-local neighborhood-based framework for probabilistic cell lineage tracing. BMC Bioinformatics 15, 217 (2014). <a href="https://doi.org/10.1186/1471-2105-15-217">https://doi.org/10.1186/1471-2105-15-217</a></em></p> <p>Additionally the data was extended and used for the development of a new tracking method in the following publication:</p> <p><em> Hirsch, P., Malin-Mayor C., Santella, A., Preibisch, S., Kainmueller, D., Funke, J. Tracking by weakly-supervised learning and graph optimization for whole-embryo C. elegans lineages. MICCAI 2022.</em></p> <p>For questions please contact Peter Hirsch (<a href="mailto:peter.hirsch@mdc-berlin.de">peterhirsch@posteo.de</a>).</p>
Kamchatka 2013 GNSS Time series
<p>GNSS time series presented in the article: "The 2013 slab-wide Kamchatka earthquake sequence"</p> <p>The columns of the files correspond to </p> <p>Year ; Month ; Day ; Hour ; Minute ; Second ; East position (mm) ; North position (mm) ; Up position (mm) ; East uncertainty (mm) ; North uncertainty (mm) ; Up uncertainty (mm) ;</p>
Training and validation data for artificial neural networks using three-dimensional partial convolutions to fill gaps in satellite image time series
<p>This dataset contains training and validation data for artificial neural networks using three-dimensional partial convolutions to fill gaps in satellite image time series. The data have been derived from Sentinel-5P total column carbon monoxide observations, using the offline processing stream.</p> <p><strong>Preprocessing</strong></p> <p>The following operations have been applied on the original S5P imagery:</p> <ol> <li>Images have been resampled to 0.1 by 0.1 degree spatial resolution</li> <li>Pixels with quality assessment value less than or equal to 0.5 have been set to NA</li> <li>Images have been aggregated by day of observation</li> <li>Images have been cropped to -60 to 60 degrees latitude</li> <li>Images have been devided into spatiotemporal blocks of size 128 x 128 pixels and 16 days</li> </ol> <p>Imagery has been recorded between 2021-01-01 and 2021-11-25. Notice that both the training and the validation blocks have been randomly sampled from all available blocks.</p> <p><br> <strong>Data Format and Naming Conventions</strong></p> <p>Input and output data blocks are stored as GeoTIFF files, where bands represent time. Notice the following file naming conventions:</p> <ul> <li>Files starting with <em>X</em> represent input measurements for training, where artificial gaps have been added.</li> <li>Files starting with <em>Y</em> represent true measurements without artificially added gaps (but still containing gaps in many cases).</li> <li>Binary masks of input data where all pixels with valid measurements are 1 and others 0 are stored in files whose name starts with <em>MASK</em></li> <li>Files starting with <em>VALMASK</em> contain a binary mask where only pixels that are available in Y but not in X are 1. The latter is used for validation on artificially removed pixels only.</li> </ul> <p>Numbers in filenames encode spatial and temporal block indexes.</p> <p>In addition, the dataset contains prediction of the validation blocks from different models in the `predictions` directory. The subfolders contain output from different models:</p> <ul> <li>mean refers to simple block-wise mean predictions.</li> <li>timeseries refers to simple linear time series interpolation.</li> <li>gapfill refers to the method proposed in [1].</li> <li>stmra refers to the method proposed in [2].</li> <li>STpconv refers to predictions passed on an artificial neural netowork with three-dimensional partial convolutions.</li> </ul> <p><strong>References</strong></p> <p>[1] Gerber, F., de Jong, R., Schaepman, M. E., Schaepman-Strub, G., & Furrer, R. (2018). Predicting missing values in spatio-temporal remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 56(5), 2841-2853.</p> <p>[2] Appel, M., & Pebesma, E. (2020). Spatiotemporal multi-resolution approximations for analyzing global environmental data. Spatial Statistics, 38, 100465.</p>
Model parameter time series of the Business Roadmap for sustainable development of the Mar Menor and surrounding Campo de Cartagena
<p>The Business Roadmap and Policy Recommendations (BRM) represents 14 solutions for sustainable development of the Mar Menor and surrounding Campo de Cartagena that were developed during an extensive co-design process (workshops, expert interviews, online questionnaires) with representatives from all relevant sectors involved. A full description of the BRM, its co-development, and the modelled impacts on Key Performance Indicators of sustainability can be found in<a href="http://doi.org/10.5281/zenodo.7142764"> Martínez-López et al (2022)</a>.</p> <p>The model parameter time-series in this dataset indicate if and when a solution is turned on or off between 1964 and 2070 based on preferences indicated by stakeholders. These time-series represent input parameters for the System Dynamics model of the socio-ecosystem of the Mar Menor-campo de Cartagena developed in the COASTAL project and that is used to quantify impacts on key performance indicators of sustainability. This System Dynamics based model, developed by Martínez-López et al (2022) can be consulted <a href="http://doi.org/10.5281/zenodo.7142764">here</a>.</p> <p>The Business Road Map and Policy Recommendations (BRM) consists of the following 4 milestones and related 14 solutions:</p> <p>(I) <strong>Rural ecotourism</strong>:</p> <p>1. The promotion of rural ecotourism activities.</p> <p>(II) <strong>Coastal ecotourism</strong>:</p> <p>2. The promotion of coastal ecotourism activities.</p> <p>(III) <strong>Sustainable agriculture</strong>:</p> <p>3. Implementation of nutrients, soil, and water retention measures</p> <p>4. Reduction in fertilizer use</p> <p>5. Denitrification of brine wastes from groundwater treated for irrigation</p> <p>6. Decrease in agricultural water demand per hectare (i.e. 10% of decrease by default in the model)</p> <p>(IV) <strong>Integrated sustainable management</strong>:</p> <p>7. Control of the extension of irrigated areas</p> <p>8. Promotion of environmental education</p> <p>9. Control of the number of groundwater wells (i.e. maximum 500 wells by default in the model)</p> <p>10. Promotion of small (<10MW) (agro)photovoltaic facilities</p> <p>11. Surface water pumping from the Albujón ephemeral stream</p> <p>12. Control of other point sources of pollution to the lagoon</p> <p>13. Groundwater pumping and treatment</p> <p>14. Increase in sea water desalination amount (twice the BAU value)</p>
Database for climate time series homogenization with metadata
<p>Usefulness of metadata in the automatic version of ACMANTv5 was tested.<br> A benchmark database has been developed, which consists of 41 datasets<br> of 20,500 networks of 170,000 synthetic monthly temperature time series<br> and the relating metadata dates. The research was supported by the<br> Catalan Meteorological Service. The research results will be published<br> in the open access MDPI journal Atmosphere. </p> <p>See more in the "Readme.txt" file of the dataset.</p>
Datacubes of InSAR time series for active volcanoes
<p>Datacubes of InSAR time series for the 20 volcanoes (*) flagged in the paper "Large-scale demonstration of machine learning for the detection of volcanic deformation in Sentinel-1 satellite imagery" by Biggs et al. (Bull Volc, 2022).</p> <p>Each file contains the time series of cumulative displacements for one volcano in the *.nc format. There are four fields with "DATA" : cumulative LOS displacements (unit=meters), "lon" : longitude (unit=degrees), "lat" : latitude (unit=degrees) and "time" : number of days since the first date. The first date can be found in the attribute "units" of the variable "time".</p> <p>(*) The five volcanoes: Sierra Negra, Fernandina, Cerro Azul, Wolf and Alcedo volcanoes are contained in the single file "galapagos_128D_09016_110500.nc".</p>
InSAR time-series and FEM Model of the Post-seismic Surface Deformation following the 2013 Baluchistan Earthquake
<p>Subduction zone accretionary prisms are commonly modeled as elastic structures where permanent deformation is accommodated by faulting and folding of otherwise elastic materials, yet accretionary prisms may exhibit other deformation styles over relatively short time scales. In this study, we use 6.5-year (2014-2021) Sentinel-1 InSAR time-series of post-seismic deformation in the Makran accretionary prism of southeast Pakistan to characterize non-linear viscoelastic deformation within an active accretionary prism on short timescales (months to years). We constructed a series of 3-D finite-element models of the Makran subduction zone, including an accretionary prism, and constrained the elastic thickness of the upper wedge and the flow-law parameters (power-law exponent, activation enthalpy, and pre-exponential constant) of the lower wedge through forward model fits to the InSAR time-series. Our results show that the prism is elastically thin (8-12 km) and the non-linear viscoelastic relaxation of the deep portions of the prism alone can sufficiently explain the post-seismic surface deformation. Our best fitting flow-law parameters (<em>n</em> = 3.76±0.39, <em>Q</em> = 82.2±37.73 kJ mol<sup>-1</sup>, and <em>A</em> = 10<sup>-3.36±4.69</sup>) are consistent with triggering of low temperature dislocation creep within fluid-saturated siliciclastic rocks. We believe that the fluids necessary for this weakening originate from sedimentary underplating and/or the presence the hydrocarbons. The presence of power-law rheology within the lower wedge impacts the estimated plate coupling and the stress state in the subduction system, with respect to the conventional elastic wedge model, and hence need to be considered in future earthquake cycle models.</p>
Microdata on vector abundance and IRS quality assurance (Estimating the impact of indoor residual spraying on sandfly abundance and incidence of visceral leishmaniasis in India from 2016 to 2022: an interrupted time-series analysis and modelling study)
<p>This repository contains the microdata on vector abundance and quality assurance of indoor residual spraying (IRS) that was used to estimate the impact of IRS on sandfly abundance and incidence of visceral leishmaniasis (VL) in India, as described in the paper "Estimating the impact of indoor residual spraying on sandfly abundance and incidence of visceral leishmaniasis in India from 2016 to 2022: an interrupted time-series analysis and modelling study" by Coffeng et al (<a href="https://doi.org/10.1016/S1473-3099(24)00420-1">https://doi.org/10.1016/S1473-3099(24)00420-1</a>). These data were collected as part of a BMGF-funded project led by dr. Michael Coleman at the Liverpool School for Tropical Medicine, as described in an earlier paper by Deb et al (<a href="https://doi.org/10.1371/journal.pntd.0009101">https://doi.org/10.1371/journal.pntd.0009101</a>).</p> <p>This repository does not include microdata on VL cases as these are owned by India's National Center for Vector Borne Disease Control (NCVBDC, <a href="https://ncvbdc.mohfw.gov.in/" target="_blank" rel="nofollow noreferrer noopener">https://ncvbdc.mohfw.gov.in/</a>).</p>
Multifractality approach of a generalized Shannon index in financial time series
<p>Multifractality is a concept that extends locally the usual ideas of fractality in a system. Nevertheless, the multifractal approaches used lack a multifractal dimension tied to an entropy index like the Shannon index. This paper introduces a generalized Shannon index (GSI) and demonstrates its application in understanding system fluctuations. To this end, traditional multifractality approaches are explained. Then, using the temporal Theil scaling and the diffusive trajectory algorithm, the GSI and its partition function are defined. Next, the multifractal exponent of the GSI is derived from the partition function, establishing a connection between the temporal Theil scaling exponent and the generalized Hurst exponent. Finally, this relationship is verified in a fractional Brownian motion and applied to financial time series. In fact, this leads us to propose an approximation called local fractional Brownian motion approximation, where multifractal systems are viewed as a local superposition of distinct fractional Brownian motions with varying monofractal exponents. Also, we furnish an algorithm for identifying the optimal q-th moment of the probability distribution associated with an empirical time series to enhance the accuracy of generalized Hurst exponent estimation.</p>
The raw position time series of 4 continous GNSS sites (YAAN, QLAI, CHDU, PIXI) and the final GNSS velocity solution of the Chengdu-Chongqing economic area (CCEA)
Open the record for dataset details and reuse information.
Time series methods for cyclical ecological data
<p>Biodiversity monitoring has entered an era of `big data', exemplified by a near-continuous collection of sounds, images, chemical, and other signals from organisms in diverse ecosystems. Such data streams have the potential to help identify new threats, assess the effectiveness of conservation interventions, as well as generate new ecological insights. However, appropriate analytical methods are often still missing, particularly with respect to characterizing cyclical temporal patterns. Here, we present a framework for characterizing and analyzing ecological responses that represent nonstationary, complex temporal patterns and demonstrate the value of using Fourier transforms to decorrelate continuous data points. In our example, we use a framework based on three approaches (spectral analysis, magnitude squared coherence, and principal component analysis) to characterize differences in tropical forest soundscapes within and across sites and seasons in Gabon. By reconstructing the underlying, cyclic behavior of the soundscape for each site, we show how one can identify circadian patterns in acoustic activity. Soundscapes in the dry season had a complex diel cycle, requiring multiple harmonics to represent daily variation, whilst in the wet season there was less variance attributable to the daily cyclic patterns. Our framework can be applied to most continuous, or near-continuous ecological data collected at a fine temporal resolution, allowing ecologists to explore patterns of temporal autocorrelation at multiple levels for biologically meaningful trends. Such methods will become indispensable as biological big data are used to understand the impact of anthropogenic pressures on biodiversity and to inform efforts to mitigate them.</p>
HydroPenIndia: A catalogue of streamflow metrics, meteorological time series and catchment attributes of Peninsular India
<p><em>HydroPenIndia, </em>a catalogue of streamflow metrics, hydro-meteorological time series and landscape attributes of 204 catchments of Peninsular India is introduced. This catalogue consists of daily hydro-meteorological time series (rainfall, soil moisture, potential evapotranspiration, actual evapotranspiration, maximum temperature, minimum temperature, longwave radiation, shortwave radiation, wind speed and humidity) for a period of 36 years from 1980-2015. The time series of 26 streamflow metrics, 13 topographic metrics, 12 climate indices, 15 hydrologic signatures, 8 land cover descriptors, 6 geologic characteristics, 6 soil characteristics (see table 8) and 13 human intervention indices are also included in this dataset. <em>HydroPenIndia</em> is an initiative to encourage hydrologists to advance knowledge of hydrological processes by contributing to fundamental research questions on Indian catchments. Free availability of the dataset will provide access to global users to represent India in large-sample hydrology studies. <em>HydroPenIndia</em> is derived from multiple databases to help researchers start their research without wasting time on collecting and processing datasets. This catalogue will motivate researchers to solve pertinent issues related to water management, quantification and risk assessment of hydrologic extremes, unravelling regional scale hydrologic functioning and climate change impact assessment over Peninsular India.</p>
Seasonal footprints on ecological time series and jumps in dynamic states of protein configurations from a nonlinear forecasting method characterization (DataSet)
<p>Data from the article: <br>"Seasonal footprints on ecological time series and jumps in dynamic states of protein configurations from a nonlinear forecasting method characterization", L. Reyes, K. Campos, G. D. Avendaño, L. González-Paz, A. Vivas, Y. J. Alvarado, and S. Flores.</p> <p>Data to be used with some implementation of the forecasting method of reference:<br>Sugihara G. and May R. M., Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series, <em>Nature</em> <strong>344</strong>, 734–741 (1990).</p>
Usable observations over Europe: Evaluation of compositing windows for landsat and sentinel-2 time series
<p>Landsat and Sentinel-2 data archives provide ever-increasing amounts of satellite data. However, the availability of usable observations greatly varies spatially and temporally. Pixel-based compositing that generates temporally equidistant cloud-free synthetic images can mitigate temporal variability, by constructing uninterrupted time series using different compositing windows. Here, we evaluated the feasibility of using compositing windows ranging from five days to one year for 1984-2021 Landsat and 2015-2021 Sentinel 2 time series to derive uninterrupted time series across Europe. We considered separate and joint use of both data archives and analyzed the spatio-temporal availability of composites during each calendar year and pixel-specific growing season across a variety of time windows and hypothesizing data interpolation. Our results demonstrated opportunities and limitations in the available data records to support medium- and long-term analyses requiring uninterrupted time series of composites with sub-annual temporal resolution. Spatial disparities across different compositing windows provide guidance on the feasibility of workflows relying on different data densities and on the challenges in wall-to-wall analyses. The feasibility of consistent time series based on composites with sub-monthly aggregation periods was mostly limited to the combined Landsat and Sentinel-2 archives after 2015, yet in some geographies requires interpolation of up to 50% of data.</p>
ScienceDex guides
Understand access before you commit
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.