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230 results for “time series data”
Data from: Decadal-scale time series highlight the role of chronic disturbances in driving ecosystem collapse in the Anthropocene
<p>These data support a publication in the Journal Ecology that describes 37 years of change on the coral reefs of St. John, US Virgin Islands. In this paper, four decades of surveys from two coral reefs (9 and 14 m depth) off St. John, US Virgin Islands, are used to quantify the associations of acute and chronic events with the changes in benthic community structure. These reefs profoundly changed over 36 years, with coral death altering species assemblages to depress abundances of the ecologically important coral <em>Orbicella</em> spp. and elevating the coverage of macroalgae and crustose coralline algae/turf/bare space (CTB). Linear mixed models revealed the prominent role of chronic variation in temperature in accounting for changes in coverage of corals, macroalgae, and CTB, with rising temperature associated with increases in coral cover on the deep reef, and declines on the shallow reef. Hurricanes were also associated with declines in coral cover on the shallow reef, and increases on the deep reef. Multivariate analyses revealed strong associations between community structure and temperature, but weaker associations with hurricanes, bleaching, and diseases. These results highlight the overwhelming importance of chronically increasing temperature in altering the benthic community structure of Caribbean reefs.</p>
Replication Package for the Paper: Transfer Learning with Time Series Data: A Systematic Mapping Study
<p>This is a replication package for the paper "Transfer Learning with Time Series Data: A Systematic Mapping Study".</p> <p>It provides</p> <ul> <li>a documentation of the conducted electronic literature search,</li> <li>exports of the search results from each literature database,</li> <li>and an excel file on the included literature and extracted data.</li> </ul>
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>
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>
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>
Vegetation greenesss data for the Aït Benhaddou Catchment, Morocco. Includes: 1984-2019 NDVI time series, breakpoint analysis results, and resillience indicator results, among others.
<p>This dataset is comprised of two main parts, both originating from different but related works. </p> <p>The NDVI timeseries and breakpoint analysis were originally developed by Vermeer (2021) for the MSc thesis: Vermeer, A. L. (2021). <em>Ecological stability in the face of climatic disturbances: a case study of a dryland ecosystem in the Moroccan High Atlas Mountains</em>. These data include a harmonized timeseries of Normalized Difference Vegetation Index from different Landsat missions at 30x30 meter resolution for the Aït Benhaddou catchment in Morocco. It also includes the output of a breakpoint analysis that was conducted using this dataset, which showcases different statistical breakpoints in NDVI after a severe drought that occured between 1998 and 2002. Shapefiles, a DEM and masks of irrigiated areas for the catchment are also included. For more information about these data, consult Vermeer (2021).</p> <p>The secondary part of this dataset was produced by Grootoonk (2024) for the MSc thesis: Grootoonk, W. (2024). <em>Relations between temporal resilience indicators and trend breakpoints in a dryland high-mountain catchment, </em>drawing upon the original dataset from Vermeer (2021). These data include Kendall's tau values for the resillience indicators variance and lag-one autocorrelation, computed using a rolling window for each pixel. Results for differerent window sizes (WS) for both indicators are included. </p> <p>Beyond these main results, a number of additional data sources are provided. These are Kendall's tau for precipitation variance in the area, produced using CHIRPS data (https://www.chc.ucsb.edu/data/chirps) and a NSI soil salinity map produced from Landsat imagery. See Grootoonk (2024) for more information. </p>
Data package for paper "Transformer models for astrophysical time series and the GRB prompt-afterglow relation"
<p>This is a data package accompanying the paper "Transformer models for astrophysical time series and the GRB<br>prompt-afterglow relation". The code used to acquire the data is in the "data" folder. The code used to analyse the data is in the "analysis" folder.</p> <p>DOI paper: <a href="https://doi.org/10.1093/rasti/rzae026">10.1093/rasti/rzae026</a></p>
"Toy Data Set" referenced in the article "A deep-learning based analysis framework for ultra-high throughput screening time-series data" (https://doi.org/10.1101/2024.08.22.609110)
<p>This data set, referenced as "toy data set" in the article "A deep-learning based analysis framework for ultra-high throughput screening time-series data" (<a href="Lint-to-article">https://doi.org/10.1101/2024.08.22.609110</a>), mimics a high-throughput screening data set. To demonstrate the application of our analysis framework described in the main article this toy data set was generated. It contains in total 1536000 individual transient signals, splitted in 5 batches of each 200 plates in 1536-well plate format. Five distinct signal classes were used to resemble typical shapes encountered in biological experiments. Fequency of occurrences for each class is reported in the main article.</p>
GPS position time series data for the Tatun Volcano Group (TVG) region
<p>GPS Time Series Data Used in "Transient Deformation in the Tatun Volcano Group, Taiwan: A Spatiotemporal GPS Analysis" by Chang et al.:</p> <p>1. Time series data of six GPS stations in TVO (YM03, YM05, YM06, YM07, YMN4, and YMSM, see Figure 1 of the main text) are included in the zip file "tvo.final_igb14.pos.tar.gz".</p> <p>2. The files are in plain text with the stardard PBO data format (https://www.unavco.org/data/gps-gnss/derived-products/docs/knowledgetree-docs-old/gps_timeseries_format.pdf), which is also listed and explained at the top of each file.</p>
Data from: Evaluating consumptive and nonconsumptive predator effects on prey density using field times series data
Determining the degree to which predation affects prey abundance in natural communities constitutes a key goal of ecological research. Predators can affect prey through both consumptive effects (CEs) and nonconsumptive effects (NCEs), although the contributions of each mechanism to the density of prey populations remain largely hypothetical in most systems. Common statistical methods applied to time series data cannot elucidate the mechanisms responsible for hypothesized predator effects on prey density (e.g., differentiate CEs from NCEs), nor provide parameters for predictive models. State space models (SSMs) applied to time series data offer a way to meet these goals. Here, we employ SSMs to assess effects of an invasive predatory zooplankter, Bythotrephes longimanus, on an important prey species, Daphnia mendotae, in Lake Michigan. We fit mechanistic models in a SSM framework to seasonal time series (1994-2012) using a recently developed, maximum likelihood-based optimization method, iterated filtering, which can overcome challenges in ecological data (e.g. nonlinearities, measurement error, and irregular sampling intervals). Our results indicate that B. longimanus strongly influences D. mendotae dynamics, with mean annual peak densities of B. longimanus observed in Lake Michigan estimated to cause a 61% reduction in D. mendotae population growth rate and a 59% reduction in peak biomass density. Further, the mechanism underlying the B. longimanus effect is most consistent with an NCE via reduced birth rates. The SSM approach also provided estimates for key biological parameters (e.g., demographic rates) and the contribution of dynamic stochasticity and measurement error. Our study therefore highlights the utility of SSMs to enhance inference for species interactions from time series data. In particular, our findings provide evidence derived directly from survey data that the invasive zooplankter B. longimanus is affecting zooplankton demographics and offer parameter estimates needed to inform predictive models that explore the effect of B. longimanus under different scenarios such as climate change.
A Non-destructive Method to Create a Time Series of Surface Area for Coral Using 3D Photogrammetry (Data)
<p>This is the underlying data for the publication "A Non-destructive Method to Create a Time Series of Surface Area for Coral Using 3D Photogrammetry" by Daniel D Conley and Erin N. R. Hollander published in 2021.</p>
LA-ICP-MS line scan data and time-series analysis outputs for Baltic Sea sediment core F80
<p>The datafile contains two sheets: HTM and MCA, corresponding to geochemical data from the Holocene Thermal Maximum and Medieval Climate Anomaly intervals, respectively, of a sediment core from the Baltic Sea (site F80, 58°00.00N, 19°53.81E, water depth 191m, Fårö Deep, collected during the HYPER/COMBINE cruise of R/V Aranda, May/June 2009). In each sheet, columns A-J contain Laser Ablation (LA)-ICP-MS line scan data of element ratios in resin-embedded sediment (Mo/Al, Fe/Al and Br/P) presented in the time domain (Age in years BP). Dating of the sediment core is described in the accompanying manuscript and references therein. These profiles are presented in three forms: Raw= raw data resampled to 1 year resolution; Det= detrended and normalized to unit variance; Gau; Gaussian bandpass filter at a period of 20-100 years. Columns L-S contain time-series analysis results of the detrended, normalized elemental ratios in period domain, including power spectra of each ratio (Blackman-Tukey window, columns M-O) and cross-spectral analysis (Blackman-Tukey window, bandwidth 5 years) of Mo/Al vs Br/P (columns P-Q) and Mo/Al vs Fe/Al (columns R-S), respectively. All analyses were performed in Analyseries 1.1.1 (Paillard et al., 1996). Figures containing the data have been submitted as part of a manuscript to Geophysical Research Letters (Jilbert et al., forthcoming),</p> <p> </p> <p>Paillard, D., Labeyrie, L., & Yiou, P. (1996). Macintosh program performs time‐series analysis. <em>Eos, Transactions American Geophysical Union</em>,<em> 77</em>(39), 379-379. <a href="https://doi.org/10.1029/96EO00259">https://doi.org/10.1029/96EO00259</a></p> <p>Jilbert, T., Gustafsson, B.G., Veldhuijzen, S., Reed, D.C., van Helmond, N.A.G.M., Hermans, M., & Slomp, C.P (forthcoming). Iron-phosphorus feedbacks drive multidecadal oscillations in Baltic Sea hypoxia. Submitted to <em>Geophysical Research Letters</em></p>
Simulated CO2 time series data based on Jena CO2 inversion and TM3 transport model, and MIROC-ACTM
<p>Each file contains simulated CO2 time series at each surface station. The model, simulation type, and station are specified in the file name. These simulations are driven by either varying winds alone (e.g., Jena_W) or varying winds and fluxes (e.g., Jena_WF). The only MIROC-ACTM run is named ACTM_W_MLO.</p>
Transitioning from microsatellites to SNP-based microhaplotypes in genetic monitoring programs: lessons from a 20-year time series of paired data.
<p>Many long-term genetic monitoring programs began before next-generation sequencing became widely available. Older programs can now transition to new marker systems usually consisting of 1000s of SNP loci, but there are still important questions about comparability, precision, and accuracy of key metrics estimated using SNPs. Ideally, transitioned programs should capitalize on new information without sacrificing continuity of inference across the time series. We combined existing microsatellite-based genetic monitoring information with SNP-based microhaplotypes obtained from archived samples of Rio Grande silvery minnow (<em>Hybognathus amarus</em>) across a 20-year time series to evaluate point estimates and trajectories of key genetic metrics. Demographic and genetic monitoring bracketed multiple collapses of the wild population, and included cases where captive-born repatriates comprised the majority of spawners in the wild. Even with smaller sample sizes, microhaplotypes yielded comparable and in some cases more precise estimates of variance genetic effective population size, multilocus heterozygosity and inbreeding compared to microsatellites because many more microhaplotype loci were available. Microhaplotypes also recorded shifts in allele frequencies associated with population bottlenecks. Trends in microhaplotype-based inbreeding metrics were associated with the fraction of hatchery-reared repatriates to the wild, and should be incorporated into future genomic monitoring. Although differences in accuracy and precision of some metrics were observed between marker types, biological inferences and management recommendations were consistent.</p>
Data for: Ectoparasite population dynamics affected by host body size but not host density or water temperature in a 32-year long time series
<p>Host density, host body size, and ambient temperature have all been positively associated with increases in parasite infection. However, the relative importance of these factors in shaping long-term parasite population dynamics in wild host populations is unknown due to the absence of long-term studies. Here, we examine long-term drivers of gill lice (Copepoda) infections in Arctic charr (Salmonidae) over 32 years. We predicted that host density and body size and water temperature would all positively affect parasite population size and population growth rate. Our results show that fish size was the main driver of gill lice infections in Arctic charr. In addition, Arctic charr became infected at smaller sizes and with more parasites in years of higher brown trout population size. Negative intraguild interactions between brown trout and Arctic charr appear to drive smaller Arctic charr to seek refuge in deeper areas of the lake, thus increasing infection risk. There was no effect of host density on the force of infection, and the relationship between Arctic charr density and parasite mean abundance was negative, possibly due to an encounter-dilution effect. The population densities of host and parasite fluctuated independently of one another. Water temperature had negligible effects on the temporal dynamics of the gill lice population. Understanding long-term drivers of parasite population dynamics is key for research and management. In fish farms, artificially high densities of hosts lead to vast increases in the transmission of parasitic copepods. However, in wild fish populations fluctuating at natural densities, the surface area available for copepodid attachment might be more important than the density of available hosts.</p>
Data from: Whiskers provide time-series of toxic and essential trace elements, Se:Hg molar ratios, and stable isotope values of an apex Antarctic predator, the leopard seal
<p>In an era of rapid environmental change and increasing human presence, researchers need efficient tools for tracking contaminants to monitor the health of Antarctic flora and fauna. Here, we examined the utility of leopard seal whiskers as a biomonitoring tool that reconstructs time-series of significant ecological and physiological biomarkers. Leopard seals (<em>Hydrurga leptonyx</em>) are a sentinel species in the Western Antarctic Peninsula due to their apex predator status and top-down effects on several Antarctic species. However, there are few data on their contaminant loads. We analyzed leopard seal whiskers (n = 18 individuals, n = 981 segments) collected during 2018–2019 field seasons to acquire longitudinal profiles of non-essential (Hg, Pb, and Cd) and essential (Se, Cu, and Zn) trace elements, stable isotope (ẟ<sub>15</sub>N and ẟ<sub>13</sub>C) values and to assess Hg risk with Se:Hg molar ratios. Whiskers provided between 46 and 286 cumulative days of growth with a mean ~125 days per whisker (n = 18). Adult whiskers showed variability in non-essential trace elements over time that could partly be explained by changes in diet. Whisker Hg levels were insufficient (<20 ppm) to consider most seals being at "high" risk for Hg toxicity. Nevertheless, maximum Hg concentrations observed in this study were greater than that of leopard seal hair measured two decades ago. However, variation in the Se:Hg molar ratios over time suggest that Se may detoxify Hg burden in leopard seals. Overall, we provide evidence that the analysis of leopard seal whiskers allows for the reconstruction of time-series ecological and physiological data and can be valuable for opportunistically monitoring the health of the leopard seal population and their Antarctic ecosystem during climate change.</p>
Time series of environmental data at LTER-MC in the Gulf of Naples (1984-2015)
<p>The present database contains data of mean temperature (TEMP), salinity (PSAL) and chlorophyll <em>a</em> (CHLT) collected in the surface (0-2 m) layer at the LTER-MC station in the Gulf of Naples (Tyrrhenian Sea, western Mediterranean) during the 1984-2015 period. For seven observations, the temperature and salinity values recorded at 5 m were reported owing to missing data at 0 and 2 m depths. The methods used for data collection and analysis are reported in Sabia et al. (2019) (https://doi.org/10.12681/mms.15935).</p>
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>
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.