Find research datasets worth reusing

Long-term time series of mussel and scallop abundances in the Bay of Seine (Normandy, France).

In Normandy (France), and particularly in the Bay of Seine, the great scallop Pecten maximus and the blue mussel Mytilus edulis are the main bivalve molluscs species harvested by the local fishing fleet. In this area, the scallop stock is one of the most important in Europe and appears to be growing since a few years. Its exploitation actually sustains an important economic activity (with a landed volume of 15.000 tons per year). However, population growth is not monotonous and yearly recruitment of young scallops seems to be very variable. For mussels, a fleet of nearly 40 boats exploited the natural banks in the subtidal area, with a landed volume ranging between 1.000 and 5.000 tons, exceptionally reaching 30.000 tons per year depending of the yearly fluctuations of the juvenile mussels’ settlement. Since 2014, however, the yearly prospective campaigns have pointed a dramatic decline of the recruitment throughout the area. Long-term data series on bivalve settlement usually show strongly fluctuating patterns, due to the environmental sensitivity of mollusk-recruitment process. While scallop and mussel stocks historically presented strong fluctuations of abundance, yearly monitoring programs were required to estimate annual abundance, and to adjust the fishing effort in line with the available resources. In accordance with stakeholders and fishermen, Ifremer (the French Research Institute for Exploitation of the Sea) and the Regional Fisheries Committee conducted these campaigns since 1992 for scallops and 1982 for mussels. Here, we present the dataset synthetizing the results of these campaigns in order to extend the reuse potential of these data. Particularly, database may help to quantify the effects of environmental variations on recruitment of two marine bivalve species and forecast simulations of recruitment under climate change scenario For scallops, a various number of one nautical mile squares were sampled each year in the southern part of the Bay of Seine (approximately south of 49°35 north latitude). This number was determined following the results of the campaigns conducted in the previous years, to ensure the representativeness of the abundance estimators. In each sampling unit, the fisher boat hauled 2 survey dredges with a 2 meters wide aperture. Harvested individuals were then classified by age-class, and numbered. Later, we calculated back an abundance index per age-class and per surface unit covered by the dredges. For mussels, 5 small banks located in the south-western part of the Bay of Seine were prospected each year. Grandcamp bank is approximately located between 1°07’ W and 0°56’ W, and 49°26’ N. and 49°23’ N. Ravenoville bank boundaries are 1°16’ W-1°07’ W, and 49°32’ N-49°25’N. Réville bank boundaries are 1°15’ W and 1°10’ W, and 49°37’ N and 49°34’ N. Moulard bank boundaries are 1°15’ W and 1°10’ W, and 49°41’ N and 49°38’ N. Barfleur bank boundaries are 1°21’ W and 1°10’ W, and 49°48’ N and 49°40’ N. During the annual campaigns, chartered fishermen vessels prospected the different banks by dredging, following a sampling design adapted to the mussel distribution and divided by a various number of one mile squares. Each year and for each bank, they realized one haul in the sampling squares located in the periphery of the bank to determine the mussel coverage. Once the boundaries of the bank delimited, the sampling effort was uniformly distributed within the area. In any one haul, the mussels were weighted and a subsample of the harvested individuals were numbered, measured and weighted individually. Later, we estimated the mean individual mass per size-class, and deduced the number of mussel per size-class and per sampling unit. In this case, the abundance index consists in an index of dredging-yield, expressed in kilograms per minute of hauling one dredge. This index was calculated for the commercial-sized mussels (> 40 mm) only, because of the reduced catchability of smaller mussels. The dataset contains 4 columns and 186 rows. The columns are ‘year’, ‘species’, ‘area’, ‘index’.

monthly time series in Qaidam basin from 2002 to 2019

The time series of monthly precipitation in Qaidam basin is converted by monthly observations of five groud meteorological stations from 2002 to 2019. The origional observation data of five ground meteorological stations is downloaded from the website (http://data.cma.cn/en) with the permission.

Time series of the longitudinal gradient of Venus's brightness temperature measured by Akatsuki LIR

The files contain the time series of the longitudinal gradient of Venusian cloud's brightness temperature measured by LIR onboard JAXA's Venus orbiter Akatsuki. The data were derived and analyzed in the paper "Planetary-Scale Waves Seen in Thermal Infrared Images of Venusian Cloud Top" by Kajiwara et al. The filename represents the latitude for each time series (For example, "10N" means 10 degrees north, and "EQ" means the equator). In all files, the first column gives the approximate elapsed time in days from 18 May 2017: the exact dates are given in the paper.  The second column gives the longitudinal gradient of the brightness temperature in unit of K/degree.

LA-ICP-MS line scan data and time-series analysis outputs for Baltic Sea sediment core F80

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),   Paillard, D., Labeyrie, L., & Yiou, P. (1996). Macintosh program performs time‐series analysis. Eos, Transactions American Geophysical Union, 77(39), 379-379. <a href="https://doi.org/10.1029/96EO00259">https://doi.org/10.1029/96EO00259</a> 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 Geophysical Research Letters

opencc-by-4.0Sep 2021View details →

Synthetic soil temperature time-series

The synthetic experiments are implemented to investigate the impact of different environmental conditions on the uncertainty of thermal diffusivity estimates. We generate synthetic temperature fields that represent various types of temperature gradients and fluctuations. This is achieved through forward modeling (i.e., heat-conduction process in a heterogeneous medium using an explicit finite difference method) with initial, top, and bottom boundary conditions set equal to the temperature time series observed at a monitoring site in Alaska during summer (synthetic_data_summer.csv ) and autumn (synthetic_data_autumn.csv ), and by assuming a soil column composed of three layers (i.e., top layer at 0.05–0.1 m, middle layer at 0.1–0.42 m, and bottom layer at 0.42–1.05 m). The thermal diffusivity in the three layers is assumed to be constant over time and equal to 0.16, 0.27 and 0.43 mm2s−1 for the case of summer temperatures and 0.25, 0.75 and 0.6 mm2s−1 for autumn. Each .csv file has 14 columns: first column containes information on the date and time on which temperature was recorded, the remaining 13 columns are soil temperature at 0.05, 0.10, 0.15, 0.20 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, 0.95 and 1.05 m below the ground surface. Impact of soil temperature trend and fluctuations on thermal diffusivity estimates is evaluated for various synthetic temperature fields  including (a) summer trend and fluctuations (synthetic_data_summer.csv ), (b) detrended fluctuations (synthetic_data_summer_detrended.csv ), (c) smoothed out daily and smaller fluctuations (synthetic_data_summer_noDiurnalFluct.csv), and (d) without fluctuations (synthetic_data_summer_noFluct.csv ).

opencc-by-4.0Sep 2021View details →

Simulated CO2 time series data based on Jena CO2 inversion and TM3 transport model, and MIROC-ACTM

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.

opencc-by-4.0Sep 2021View details →

Wind Time Series Dataset

This dataset comes from a single turbine on an inland wind farm.  The dataset covers the duration of one year, but data at some of the time instances are missing. Two time resolutions are included in the dataset: the 10-min data and the hourly data; the latter is the further average of the former.  For each temporal resolution, the data is arranged in three columns. The first column is the time stamp, the second column is the wind speed, and the third column is the wind power. This dataset is used in Chapter 2 of the <a href="https://aml.engr.tamu.edu/book-dswe/">Data Science for Wind Energy</a> book.

opencc-by-4.0Oct 2021View details →

Manual 4D annotations of Micro X-ray CT time-series (4D dataset)

The 4D (3D+time) manual annotations of https://doi.org/10.5281/zenodo.4293394. For the annotation the SuRVoS workbench was used (https://doi.org/10.5281/10.5281/zenodo.247547) and our proposed hidden Markov model (HMM-T, https://doi.org/10.5281/zenodo.4416013 ) designed to refine 4D semantic segmentations made by a 3D semantic segmentation CNN after its applied on 4D data. Only slices 740-742 and 747-749 (refining to the first axis) are partially annotated. We acknowledge Diamond Light Source for the time on I13-2 under proposal mt9396.

opencc-zeroOct 2022View details →

Transitioning from microsatellites to SNP-based microhaplotypes in genetic monitoring programs: lessons from a 20-year time series of paired data.

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 (Hybognathus amarus) 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.

opencc-zeroNov 2022View details →

Data for: Ectoparasite population dynamics affected by host body size but not host density or water temperature in a 32-year long time series

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.

opencc-zeroDec 2022View details →

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

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 (Hydrurga leptonyx) 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 (ẟ15N and ẟ13C) 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.

opencc-by-4.0Jan 2023View details →

Dataset: Evaluation of post-hoc interpretability methods in time-series classification

This repository contains the dataset, trained models as well as results for the article Evaluation of post-hoc interpretability methods in time-series classification. The code to reproduce the results presented in the article is available on <a href="https://github.com/hturbe/InterpretTime">GitHub</a>. More details on the data and results can be found in the article. Files: <ul> <li>datasets.zip: Include the three datasets used in the article: <ul> <li>ECG: Processed version of the CPSC dataset from Classification of 12-lead ECGs: the PhysioNet - Computing in Cardiology Challenge 2020.</li> <li>fordA: Dataset from the <a href="https://www.cs.ucr.edu/~eamonn/time_series_data_2018/">UCR Time Series Classification Archive</a></li> <li>synthetic: Synthetic dataset developed specifically for the purpose of the article</li> </ul> </li> <li>trained_models.zip: Include CNN, transformer and bi-lstm trained on the three datasets</li> <li>results_paper.zip: Computed relevance and evaluation metrics for the trained models <ul> <li>model_interpretability: Include the relevance computed using the different interpretability methods as well as the computed metrics for each method </li> <li>summary_results: Summary of the evaluation metrics across all interpretability methods for each dataset as well as an excel file summarising the metrics across all datasets.</li> </ul> </li> </ul>

opencc-by-4.0Jan 2023View details →

Time Series Analysis of a Daytime Urban Heat Island in Dar es Salaam Metropolitan Areas.

The study aimed to determine the existing linear influence levels of various causative factors of Urban Heat Island (UHI) by using Geographically Weighted Regression Model (GWR) that determines non-stationarity by generating a new equation for each sample size. Urban heat island occurs when higher temperatures reside in urban areas compared to surrounding areas due to the replacement of natural vegetation with construction materials during development purposes. Time Series with Linear Regression was used to determine the linearity between dependent and independent variables. Moderate Resolution Imaging Spectroradiometer (MODIS) products such as MOD11A1, MCD43A1, MOD09A1 and MOD13A1 were used to acquire Land Surface Temperature (LST), Albedo, Indexed-Based Built-Up Index (IBI) and Enhanced Vegetation Index (EVI) respectively. In-situ datasets of the wind speed were acquired from Tanzania Meteorological Agency (TMA). UHI was observed to have a non-linear trend with IBI and wind speed and a linear trend with Albedo and EVI. The strongest values of UHI were observed at the city center, IBI was observed as a leading causative factor by having a non-lineality influence of 0.023 followed by Albedo, wind speed and EVI with an influence of 0.019, 0.016 and -0.015 respectively. Since IBI and Albedo contribute more to the development of heat island, urban residents should be encouraged to use construction materials with a lower absorption rate of solar energy.

opencc-by-4.0Jan 2023View details →

STE CECs: Coastal Radon Time-series v1.0

Description: Radon in coastal water time-series data and accompanying water and meteorological parameters collected from 27 different locations globally. These data were used to train and validate two deep learning models. Data for each study site were provided by authors - please see the "References" sheet in the document for the original source and citation of the data. Associated code can be found here: 10.5281/zenodo.7581389 Associated publication accepted (in press) in Water Resources Research. Column names in the "data" sheet are as follows: datetime = date and time of measurement (local time, MM/DD/YY HH:MM) Location = name of location of measurement (data = string data) Aquifer_type =  categorical aquifer type (data = string data, options: rocky, sandy, or muddy). depth_m = depth of water column (m). Measured with CTD probe or similar. ctdtemp_C = water temperature (degrees Celsius) at point of radon measurement. Measured with CTD probe or similar. ctdsal = water salinity (unitless) at point of radon in water measurement. Measured with CTD probe or similar. windsp_ms = wind speed, 10 m above sea level (units = m/s). Data from wunderground.com from closest weather station for all sites except for Kīholo Bay, HI, USA (data sourced from RAWS USA Climate Archive, Puu Waawaa station: https://raws.dri.edu/cgi-bin/rawMAIN.pl?hiHPUW) and FSUCML, FL, USA (data sourced from FAWN Carrabell Station: https://fawn.ifas.ufl.edu/data/reports/) airtemp_C = air temperature (units = degrees Celsius). Data from wunderground.com from closest weather station for all sites except for Kīholo Bay, HI, USA (data sourced from RAWS USA Climate Archive, Puu Waawaa station: https://raws.dri.edu/cgi-bin/rawMAIN.pl?hiHPUW) and FSUCML, FL, USA (data sourced from FAWN Carrabell Station: https://fawn.ifas.ufl.edu/data/reports/) Rn_Bqm3 = radon in water (units = Bq/m^3). Measured with Durridge RAD7 or similar radon-in-air detector or underwater gamma spectrometer (e.g., Dulai et al., 2016: https://doi.org/10.1007/s10967-015-4580-9).

opencc-by-4.0Dec 2022View details →

Time series analysis of tegument ultrastructure of in vitro transformed miracidium to mother sporocyst of the human parasite Schistosoma mansoni

Here is a compilation of all the Scanning Electron Microscopy pictures at our disposal regarding the in vitro transformation of miracidia to mother sporocysts of Schistosoma mansoni. These datas were partially published in: <a href="https://doi.org/10.1016/j.actatropica.2023.106840">https://doi.org/10.1016/j.actatropica.2023.106840</a>

opencc-zeroFeb 2023View details →

Steady-state time series and code

Pressure-flow travelling waves are a key topic for understanding arterial haemodynamics. However, wave trasmission and reflection processes induced by body posture changes have not been thoroughly explored yet. Current in-vivo research has shown that the amount of wave reflection detected at the central level (ascending aorta, aortic arch) decreases during tilting to the upright position, despite the widely-proved stiffening of the cardiovascular system (CVS). Moreover, it is not known whether the optimized configuration of the arterial system typical of the supine condition - i.e., enabled propagation of direct waves vs. trapping of reflected waves, protecting the heart - is preserved or not after posture changes. To shed light on these aspects, we propose a multiscale modelling approach to inquire into posture-induced arterial wave dynamics elicited by simulated head-up tilting. In spite of remarkable remodelling of the human vasculature following posture changes, our analysis shows that: (i) vessels lumens at arterial bifurcations remain well matched in the forward direction; (ii) the reduced wave reflection observed at the central level seems to be caused by backward propagation of weakened pressure waves produced by cerebral autoregulation; and (iii) backward wave trapping is preserved upon orthostatic stress.

opencc-by-4.0Jul 2023View details →

GNSS position time series (.neu files) and seismic velocity strcture (Profil_lat2822_Vp_Vs.dat) used in the manuscript

These are the raw GNSS time series files (in .neu format) and seismic velocity strcture file (Profil_lat2822_Vp_Vs.dat) we used in the manuscript. These data are not allowed to use before the manuscript is accepted.

opencc-by-4.0Feb 2023View details →

Widespread flood severity index time series for Germany

The dataset provides time series from 1951 to 2013 of monthly flood severity index and mean monthly precipitation derived for Germany (DE) and its three hydroclimatic regions (NW: North-Western region; NE: North-Eastern region; South: Southern region). The dataset also provides most frequent event type recorded in Germany for each months.  The dataset was used to train and test a dilated Convolutional Neural Network for forecasting monthly flood severity in Germany (Tarasova et al., submitted to Earth's Future).

opencc-by-4.0Mar 2023View details →

Time series of phytoplankton size classes at LTER-MC in the Gulf of Naples (1984-2015)

This database contains the abundance data of four phytoplankton size classes from the Long Term Ecological Research Station MareChiara (st. LTER-MC, 40°48.5’ N, 14°15’ E) over the years 1984-2015. Samples were collected with a Niskin bottle in surface waters, fixed with neutralized formaldehyde (0.8-1.6% final concentration), and counted at the inverted microscope. Linear measurements were taken on a number of phytoplankton specimens for each species and biovolumes were calculated based on species-specific shapes. The species were grouped in size classes (< 5 µm, 5-15 µm, 15-30 µm, > 30 µm) based on their equivalent spherical diameter (ESD).