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2,288 results for “Periodical”

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zenodo40/100

Dataset for Cicerale, Blanzieri, Sacco - How does decision-making change during challenging periods?

Dataset for experiment 1 (Bataclan terror attacks) and 2 (COVID pandemic - 1st and 2nd wave), results described in  Cicerale, Blanzieri, Sacco - How does decision-making change during challenging periods?, submitted to PLOS ONE. Version 2: Added dataset in CSV format, column keys and the original questions in Italian

opencc-by-4.0Dec 2021View details →

dryad40/100

Data from: Periodic environmental disturbance drives repeated ecomorphological diversification in an adaptive radiation of Antarctic fishes

The ecological theory of adaptive radiation has profoundly shaped our conceptualization of the rules that govern diversification. However, while many radiations follow classic early burst patterns of diversification as they fill ecological space, the longer-term fates of these radiations depend on many factors, such as climatic stability. In systems with periodic disturbances, species-rich clades can contain nested adaptive radiations of subclades with their own distinct diversification histories, and how adaptive radiation theory applies in these cases is less clear. Here, we investigated patterns of ecological and phenotypic diversification within two iterative adaptive radiations of cryonotothenioid fishes in Antarctica's Southern Ocean: crocodile icefishes and notoperches. For both clades, we observe evidence of repeated diversification into disparate regions of trait space between closely related taxa and into overlapping regions of trait space between distantly related taxa. We additionally find little evidence that patterns of ecological divergence are correlated with evolution of morphological disparity, suggesting that these axes of divergence may not be tightly linked. Finally, we reveal evidence of repeated convergence in sympatry that suggests niche complementarity. These findings reflect the dynamic history of Antarctic marine habitats, and may guide hypotheses of diversification dynamics in environments characterized by periodic disturbance.

opencc-zeroMay 2022View details →

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All-India district-scale climate projections for current (2006-2015), mid-century (2041-50), and end-century (2091-2100) periods

<ol> <li>This dataset contains monthly mean data for 10 meteorological variables for each district of India for current, mid-century, and end-century periods for all the districts of India. </li> <li>To generate this data, we first dynamically downscaled the CMIP5 CESM RCP8.5 projections over India for the Current (2006-2015), Mid-Century (2041-50), and End-Century (2091-2100) periods using the Weather Research and Forecasting (WRF) model to 10 km resolution. The 30 years of data are archived in the World Data Center for Climate (WDCC) at DKRZ (Barik et al. 2021 and 2022). Next, we processed the 10-km gridded downscaled data to calculate the monthly climatological mean by averaging over 10 years for each of the 3 periods. Finally, the monthly climatological means were processed in ArcGIS to develop the district scale datasets.</li> </ol>

opencc-by-4.0May 2022View details →

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Long-term dynamics of trace elements concentrations in the organism of the shrews (Sorex) during the periods of high and reduction emissions from the copper smelter

Data and code for mixed-model analysis for the article:  Mukhacheva S.V. (2022) Long-term dynamics of trace elements concentrations in the organism of the shrews (Sorex) during the periods of high and reduction emissions from the copper smelter" // Russian Journal of Ecology. Vol. 5.  Data provided by S.V. Mukhacheva Code provided by A.N. Sozontov

opencc-by-4.0May 2022View details →

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A temporally consistent 8-day 0.05° gap-free snow cover extent dataset over the Northern Hemisphere for the period 1981–2019

Northern Hemisphere (NH) snow cover extent (SCE) is one of the most important indicator of climate change for its unique surface property. However, short temporal coverage, coarse spatial resolution, and different snow discrimination approach among published SCE products hampers its detailed studies. Using the Advanced Very High Resolution Radiometer Surface Reflectance (AVHRR-SR) Climate Data Record (CDR) and several ancillary datasets, this study generated a temporally consistent 8-day 0.05° gap-free NH terrestrial SCE product for the period 1981–2019 as part of the Global LAnd Surface Satellite dataset (GLASS) product suite. This process consistent of five steps. First, a decision tree algorithm with multiple threshold tests was applied to detect SCE from daily AVHRR-SR CDR. Second, we merge two existing daily SCE products to take advantage of their spatial coverage. Third, an aggregation process was used to detect the maximum SCE in each 8-day periods. Forth, the GLASS SCE was generated with the help of snow cover probability climatology. Fifth, the validation process was carried out to evaluate the quality of GLASS SCE. Validation results by using 562 Global Historical Climatology Network stations during 1981–2017 (r=0.61, p<0.05) and MOD10C2 during 2001–2019 (r=0.97, p<0.01) proved that the GLASS SCE product is credible in snow cover frequency monitoring. Moreover, cross-comparison between GLASS SCE and surface albedo during 1982–2018 further confirmed its values in climate changes studies. The GLASS SCE data set provides binary maps of snow cover for the Northern Hemisphere from September 1981 to the December 2019. The data are organized by year and provided in GeoTIFF formats. The gridcells were flagged as “0” if classified as "Non-snow", "1" if retrieved from AVHRR satellite observations, and "2" if  filled by IMS snow climatology. Spatial Coverage: N: 90, S: 0, E: 180, W: -180 Spatial Resolution: 0.05 deg x 0.05 deg Samples = 7200 Lines = 1800 Temporal Coverage: September 1981 to December 2019 Temporal Resolution: 8-day

opencc-by-4.0Aug 2021View details →

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Datasets used on the analysis of Mediterranean Mass mortality events during the 2015-2019 period

This upload contains three datasets in CSV files and a PDF file with the specific description of the CSV files. These data was used for the analysis of the mass mortality events reported during the period 2015-2019 across the Mediterranean. The datasets are 1) a CSV file with the data used for the description of the spatial-temporal, depth and biological patterns of mortality observed in the Mediterranean Sea in the 2015-2019 period; 2) a CSV file with the data used to conduct the analyses on the relationship between marine heatwaves (MHW) days found on the surface (averaged per monitored area and year) and the corresponding mass mortality incidence of benthic organisms; 3) a CSV file with the data used to conduct the analyses on the relationship between in-situ MHW days (averaged per monitored area, depth and year) and the corresponding mass mortality incidence.  Data were obtained through benthic community field surveys conducted by 33 research teams from 11 Mediterranean countries. Surveys covered thousands of kms of coastline, spanning 13º of latitude (32 °S to 45 °N) and 40º of longitude (-5°W to 35°E) in the Mediterranean Sea. The dataset provides the most updated inventory of mass mortality events records for benthic species between 2015-2019 in the region. The surveys were conducted in 142 monitoring areas. Monitoring areas were considered as geographic areas (10-25 km coastline, e.g., a marine protected area and the nearby coast) sharing common environmental features.  In situ temperature conditions datasets base consists of high frequency (hourly) time series obtained using HOBO data loggers (accuracy ± 0.21°C) set-up at standard depths along rocky walls by divers, generally every 5 m from the surface to 40 m depth.This dataset as in the case of the mortality was assembled under the T-MEDNet initiative (<a href="http://www.t-mednet.org/">www.t-mednet.org</a>). Satellite derived sea surface temperature (SST) across the Mediterranean Sea was obtained from CMEMS (https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=SST_MED_SST_L4_REP_OBSERVATIONS_010_021). The data consists of daily (night-time), gap free, optimally interpolated foundation SST at ~4 km resolution from AVHRR with improved accuracy and stability over the 1982-2019 period

opencc-by-4.0Dec 2021View details →

zenodo40/100

Ehrhart series coefficients and quasi-period for random rational polytopes

Ehrhart series coefficients and quasi-period for random rational polytopes A dataset of Ehrhart data for 84000 randomly generated rational polytopes, in dimensions 2 to 4, with quasi-periods 2 to 15. The polytopes used to generate this data were produced by the following algorithm: <ol> <li>Fix \(d\) a positive integer in \(\{2,3,4\}\).</li> <li>Choose \(r\in\{2,\ldots,15\}\) uniformly at random.</li> <li>Choose \(d + k\) lattice points \(\{v_1,\ldots,v_{d+k}\}\) uniformly at random in a box \([-5r,5r]^d\), where \(k\) is chosen uniformly at random in \(\{1,\ldots,5\}\).</li> <li>Set \(P := \mathrm{conv}\{v_1,\ldots,v_{d+k}\}\). If \(\mathrm{dim}(P)\) is not equal to \(d\) then return to step 3.</li> <li>Choose a lattice point \(v\in P \cap \mathbb{Z}^d\) uniformly at random and replace \(P\) with the translation \(P-v\).</li> <li>Replace \(P\) with the dilation \(P/r\).</li> </ol> The final dataset was produced by first removing duplicate records, and then downsampling to a subset with 2000 datapoints for each pair \((d,q)\), where \(d\) is the dimension of \(P\) and \(q\) is the quasi-period of \(P\), with \(d\in\{2,3,4\}\) and \(q\in\{2,\ldots,15\}\). For details, see the paper:  Machine Learning the Dimension of a Polytope, Tom Coates, Johannes Hofscheier, and Alexander M. Kasprzyk, 2022. If you make use of this data, please cite the above paper and the DOI for this data:  doi:10.5281/zenodo.6614829 quasiperiod.txt.gz The file "quasiperiod.txt.gz" is a gzip-compressed plain text file containing key:value records with keys and values as described below, where each record is separated by a blank line. There are 84000 records in the file. Example record ULID: 01G57JBYP2ZW825E0NT4Q9JQNQ Dimension: 2 Quasiperiod: 2 Volume: 97 EhrhartDelta: [1,50,195,289,192,49] Ehrhart: [1,50,198,...] LogEhrhart: [0.000000000000000000000000000000,3.91202300542814605861875078791,5.28826703069453523626966617327,...] (The values for Ehrhart and LogEhrhart in the example have been truncated.) For each polytope \(P\) of dimension \(d\) and quasi-period \(q\) we record the following keys and values in the dataset: ULID: A randomly generated string identifier for this record. Dimension: A positive integer. The dimension \(2 \leq d \leq 4\) of the polytope \(P\). Quasiperiod: A positive integer. The quasi-period \(2 \leq q \leq 15\) of the polytope \(P\). Volume: A positive rational number. The lattice-normalised volume \(\mathrm{Vol}(P)\) of the polytope \(P\). EhrhartDelta: A sequence \([1,a_1,a_2,\ldots,a_N]\) of integers of length \(N + 1\), where \(N := q(d + 1) - 1\). This is the Ehrhart \(\delta\)-vector (or \(h^*\)-vector) of \(P\). The Ehrhart series \(\mathrm{Ehr}(P)\) of \(P\) is given by the power-series expansion of \((1 + a_1t + a_2t^2 + \ldots + a_Nt^N) / (1 - t^q)^{d+1}\). Ehrhart: A sequence \([1,c_1,c_2,\ldots,c_{1100}]\) of positive integers. The value \(c_i\) is equal to the number of lattice points in the \(i\)-th dilation of \(P\), that is, \(c_i = \#(iP \cap \mathbb{Z}^d)\). Equivalently, \(c_i\) is the coefficient of \(t^i\) in \(\mathrm{Ehr}(P) = 1 + c_1t + c_2t^2 + \ldots = (1 + a_1t + a_2t^2 + \ldots + a_Nt^N) / (1 - t^q)^{d+1}\). LogEhrhart: A sequence \([0,y_1,y_2,\ldots,y_{1100}]\) of non-negative floating point numbers. Here \(y_i := \log c_i\).

opencc-zeroJun 2022View details →

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Model outputs of Wei et al. (2022): "Salt intrusion as a function of estuary length in periodically weakly stratified estuaries", published in Geophysical research Letters.

The .mat file includes all model data used in the study "Salt intrusion as a function of estuary length in periodically weakly stratified estuaries", published in Geophyscial Research Letters, 2022. The .txt file contains description of all physical variables contained in the .mat file.

opencc-by-4.0Jul 2022View details →

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Probabilistic forecasts of the daily maximum of the Kp index produced by the SERENADE prototype model and three empirical models for the period 2010-2018

This dataset contains the probabilistic outputs of SERENADE's first prototype model dedicated to the forecasting of the \(\textit{Kp}_{\textrm{max, 24 h}}\) index for forecasting horizons ranging between 2 and 7 days. The period covered is the one of the SDOML dataset, which is 2010-05 --- 2018-12. All data is contained in a single pickle file, that can be opened in Python, using the following code lines: \(\texttt{import pickle}\\ \texttt{with open(DATA_PATH+`/serenade_outputs.pkl', `rb') as f:}\\ ~~~~\texttt{dict_outputs = pickle.load(f)}\) The pickle file contains a dictionnary, which itself contains Pandas DataFrames. Each DataFrame corresponds to a forecasting horizon. The dictionnary's keys are the forecasting horizons stored as strings, that is: \(\texttt{dict_output.keys() = [`2',`3',`4',`5',`6',`7']}\) The DataFrames are indexed by datetime. They contain the observed (true) hourly values of the daily maximum of the Kp index, the forecasts provided by SERENADE and three baseline models (Climatology model, Persistence model and 27-day Recurrence model). The forecast values include the mean and the standard deviation of the forecast normal distributions. Missing moments are due to the absence of EUV images needed to provide the forecast at the given moment.

opencc-by-4.0Jul 2022View details →

dryad40/100

Demographic consequences of changes in environmental periodicity

The fate of natural populations is mediated by complex interactions among vital rates, which can vary within and among years. While the effects of random, among-year variation in vital rates have been studied extensively, relatively little is known about how periodic, non-random variation in vital rates affects populations. This knowledge gap is potentially alarming as global environmental change is projected to alter common periodic variations, such as seasonality. We investigated the effects of changes in vital-rate periodicity on populations of three species representing different forms of adaptation to periodic environments: the yellow-bellied marmot (Marmota flaviventer), adapted to strong seasonality in snowfall; the meerkat (Suricata suricatta), adapted to inter-annual stochasticity as well as seasonal patterns in rainfall; and the dewy pine (Drosophyllum lusitanicum), adapted to fire regimes and periodic post-fire habitat succession. To assess how changes in periodicity affect population growth, we parameterized periodic matrix population models and projected population dynamics under different scenarios of perturbations in the strength of vital-rate periodicity. We assessed the effects of such perturbations on various metrics describing population dynamics, including the stochastic growth rate, log λS. Overall, perturbing the strength of periodicity had strong effects on population dynamics in all three study species. For the marmots, log λS decreased with increased seasonal differences in adult survival. For the meerkats, density dependence buffered the effects of perturbations of periodicity on log λS. Finally, dewy pines were negatively affected by changes in natural post-fire succession under stochastic or periodic fire regimes with fires occurring every 30 years, but were buffered by density dependence from such changes under presumed more frequent fires or large-scale disturbances. We show that changes in the strength of vital-rate periodicity can have diverse but strong effects on population dynamics across different life histories. Populations buffered from inter-annual vital-rate variation can be affected substantially by changes in environmentally-driven vital-rate periodic patterns; however, the effects of such changes can be masked in analyses focusing on inter-annual variation. As most ecosystems are affected by periodic variations in the environment such as seasonality, assessing their contributions to population viability for future global-change research is crucial.

opencc-zeroSep 2022View details →

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Dataset of measurements of the soil CO2 flux and soil brightness temperature at Le Biancane (geothermal field of Larderello-Travale, Tuscany, Italy) in the May-June 2021 period.

Dataset of measurements of the soil CO2 flux and soil brightness temperature at Le Biancane (geothermal field of Larderello-Travale, Tuscany, Italy) in the period May-June 2021. The dataset is structured as follows: Column A is the progressive number of the point (#); Column B is the Longitude of the point, datum WGS 1984; Column C is the Latitude of the point, datum WGS 1984; Column D is the Universal Transverse Mercator (UTM) Longitude coordinate, datum WGS 1984, zone 32N; Column E is the Universal Transverse Mercator (UTM) Latitude coordinate, datum WGS 1984, zone 32N; Column F is the soil brightness temperature, in °C; Column G is the soil CO2 flux in grams of CO2 per square meter, per day (g m-2 day-1)

opencc-by-4.0Oct 2022View details →

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Hoofprints in the Sand Supplement S5: LSI Summary Stats by Site, Period, Element

Table of summary statistics produced in the LSI analysis per site, period, and skeletal element, as demonstrated in Harding, S. et al. Hoofprints in the Sand: A Metric Study of Livestock on the Southern Phoenician Coast. In preparation for Quaternary International. v.2 changed the spelling of Tel Shiqmona, abbreviation SHQ

opencc-by-4.0Dec 2021View details →

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Dataset of structural measurements at Le Biancane (geothermal field of Larderello-Travale, Tuscany, Italy) in the May-June 2021 period.

The dataset contains measurements of fractures and bedding at Le Biancane (geothermal field of Larderello-Travale, Tuscany, Italy) in the period May-June 2021. The term fractures in this dataset indicate a break in a rock where the orthogonal opening is predominant; when clear lateral displacement by shearing is observed, then we adopt the term fault accordingly to the definition by National Research Council (1996). The topological analysis has been conducted using the methods described by Sanderson and Nixon (2015; 2018). This dataset consists of two text files described below. structural-dataset.txt: this file contains the measured fractures and bedding planes, it is structured as follow: Column A is the Longitude of the point, datum WGS 1984; Column B is the Latitude of the point, datum WGS 1984; Column C is the dip direction of the measured structure; Column D is the dip of the measured structure; Column E is the type of the measured structure (fracture, fault, bedding)   topological-analysis.txt: this file contains the measurement done for the topological analysis on nine sites at Le Biancane area, it is structured as follow: Column A is the code of the site; Column B is the Longitude of the point, datum WGS 1984; Column C is the Latitude of the point, datum WGS 1984; Column D is the number of nodes I (NI); Column E is the number of nodes Y (NY); Column F is the number of nodes X (NX); Column G is the percent of nodes I (%NI); Column H is the percent of nodes Y (%NY); Column I is the percent of nodes X (%NX); Column J is the probability of connection of nodes I-I (PII’); Column K is the probability of connection of nodes I-C (PIC’); Column L is the probability of connection of nodes C-C (PCC’); Column M is the radius in meter (r) of the circle used for the topological analysis; Column N is the value of the parameter CL; Column O is the value of the parameter CB; Column P is the area (m^2) of the circle; Column Q is the fracture intensity;

opencc-by-4.0Oct 2022View details →

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Text-fig. 6. Correlation of the Cheringoma and Mazamba formations on the basis of benthic foraminiferans and mammals respectively. Identifications of foraminiferans are from Newton (1924) and Abrard (1928), and the ranges of foraminiferans are from Sella-Kiel et al. (1998). The time scale is from Gradstein et al. (2020). The distribution of Nummulites atacicus is included, but it is not known whether it is reworked from older deposits. If the identification is valid, it would support the thesis that there was a period of Ypresian deposition in the vicinity during which remains of the species were fossilised. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique

Text-fig. 6. Correlation of the Cheringoma and Mazamba formations on the basis of benthic foraminiferans and mammals respectively. Identifications of foraminiferans are from Newton (1924) and Abrard (1928), and the ranges of foraminiferans are from Sella-Kiel et al. (1998). The time scale is from Gradstein et al. (2020). The distribution of Nummulites atacicus is included, but it is not known whether it is reworked from older deposits. If the identification is valid, it would support the thesis that there was a period of Ypresian deposition in the vicinity during which remains of the species were fossilised.

opencc-by-4.0Dec 2021View details →

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[Supporting Information] Are Peruvians moving towards healthier diets with lower environmental burden? Household consumption trends for the period 2008-2021

Supporting information from the manuscript: Are Peruvians moving towards healthier diets with lower environmental burden? Household consumption trends for the period 2008-2021. The main goal of this study was to comprehensively analyze the evolution in diet quality in Peru in the period 2008-2021 based on apparent household purchases extracted from the National Household Survey (ENAHO, by its acronym in Spanish). Furthermore, this study identified patterns in the temporal and spatial variability of food consumption, differences in consumption based on poverty levels, and gaps in achieving consumption levels of macronutrients and calories recommended by international nutritional authorities. dataset1: contains the consumption of 96 food products in kg/person/year per household, for a time horizon from 2008 to 2021. dataset2: contains the caloric and macronutrient content in kcal or g macronutrient per 100g of 92 food items. dataset3: contains the consumption of calories, and macronutrients in g/person/day per household, for a time horizon from 2008 to 2021. dataset1_labels: contains the data dictionary of dataset1 dataset2_labels: contains the data dictionary of dataset2 dataset3_labels: contains the data dictionary of dataset3

opencc-by-4.0Mar 2024View details →

zenodo40/100

Train and test datasets used for the paper "Neural network time-series classifiers for gravitational-wave searches in single-detector periods"

This repository contains the datasets used for training and testing during the work discussed in the paper "<a href="https://iopscience.iop.org/article/10.1088/1361-6382/ad40f0" target="_blank" rel="noopener">Neural network time-series classifiers for gravitational-wave searches in single-detector periods</a>". Please refer to this paper for more details on how the dataset was produced and cite it if you use these data: A. Trovato et al "Neural network time-series classifiers for gravitational-wave searches in single-detector periods", Class. Quant. Grav. 2024 DOI 10.1088/1361-6382/ad40f0. In this repository you will find six files in format npz, three of which refer to the test dataset and three to the train dataset. Each file name is of the type {label}_{train or test}.npz where "label" can be "glitch", "noise" or "signal", while the second part of the name indicates whether the file was used for training or testing. Each file is a collection of numpy arrays so it should be read with python. It contains 3 numpy arrays: 'X', 'Y' and 'metadata'. 'X' is a matrix containing 1-second segments of data sampled at 2048 Hz of the LIGO-Livingston detector, so it has shape: (number of samples, 2048). 'Y' contains the label for each segment, which is 0 for noise, 1 for signal and 2 for glitch, so it has shape: (number of samples,). In this case, the information on 'Y' is redundant since it's given directly by the filename. The 'metadata' matrix contains 17 metadata for each sample only for the case of signals, for glitch or noise it contains just 17 zeros for each sample. The shape of 'metadata' is thus: (number of samples, 17). For the signal files, for each sample the metadata is an array with these components: <ol> <li>GPS start of the file from which this segment comes</li> <li>starting GPS time of this segment</li> <li>duration of the segment [s]</li> <li>mass1 [solar masses]</li> <li>mass2 [solar masses]</li> <li>spin1z</li> <li>spin2z</li> <li>inclination [radians]</li> <li>coalescence phase [radians]</li> <li>distance [Mpc]</li> <li>right_ascension [radians]</li> <li>declination [radians]</li> <li>polarization [radians]</li> <li>SNR (signal to noise ratio)</li> <li>shift of the signal w.r.t. the timeseries [s]</li> <li>length of the signal [s]</li> <li>fraction of the signal contained in the time window</li> </ol> Number of samples: <ul> <li>80000 for the file glitch_test.npz</li> <li>69998 for the file glitch_train.npz</li> <li>500000 for the file noise_test.npz</li> <li>250000 for the file noise_train.npz</li> <li>500000 for the file signal_test.npz</li> <li>250000 for the file signal_train.npz</li> </ul> An example of few lines of python code to read each file is: <pre><code>import numpy as np f = np.load("filename.npz") X = f['X'] Y = f['Y'] m = f['metadata'] </code></pre> For the preparation of these data, we acknowledge the use of the following software packages: GWpy [1], PyCBC [2] and LALSuite [3].  This research has made use of data or software obtained from the Gravitational Wave Open Science Center (<a href="https://gwosc.org/" target="_blank" rel="noopener">gwosc.org</a>), a service of the LIGO Scientific Collaboration, the Virgo Collaboration, and KAGRA. This material is based upon work supported by NSF's LIGO Laboratory which is a major facility fully funded by the National Science Foundation, as well as the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. Virgo is funded, through the European Gravitational Observatory (EGO), by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale di Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by institutions from Belgium, Germany, Greece, Hungary, Ireland, Japan, Monaco, Poland, Portugal, Spain. KAGRA is supported by Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan Society for the Promotion of Science (JSPS) in Japan; National Research Foundation (NRF) and Ministry of Science and ICT (MSIT) in Korea; Academia Sinica (AS) and National Science and Technology Council (NSTC) in Taiwan. [1] https://gwpy.github.io [2] https://pycbc.org [3] https://lscsoft.docs.ligo.org/lalsuite

opencc-by-4.0Apr 2024View details →

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EEG recordings during resting-state and the maintenance periods of a spatial working memory task in humans

Scripts used to analyze data for the manuscript submitted for publication in EJN Script_Curve_Fitting_HBM.rtf Dr. Hadj Boumediene Meziane: hbmeziane@gmail.com  We therefore considered this continuous change in power as an extraneous variable yk(x) impacting the measured power spectrum Pow(Ek), and modeled it with a binomial equation that best fit the data, where the coefficients in pi are in descending powers, and the length of p is (n+1), k is trial number (k = 1 to 10): yk(x) = p1 . x2 + p2 . x + p3 In order to statistically compare the topographies between the trials with perfect recall and the trials with failed recall, we subtracted this variable from the mean spectral topographies of each subject and for each electrode by first producing the mean spectral curves of each maintenance trial in the theta and alpha frequency bands, taking into account the IAF, and then calculating the coefficients (p1, p2 and p3) of the binomial equation using the Matlab function polyfit.m. Once the coefficients were determined, this estimate was subtracted from each power spectrum matrix using the following formula: PowFit(Ek) = Pow (Ek) – yk(x)   Script_Perf_Fail_EEG_Power_Spec_HBM.rtf Dr. Hadj Meziane: hbmeziane@gmail.com This script calculates EEG power spectra then compares perf and fail conditions, then plots brain topographies with statical results   Script_Perf_Fail_EEG_Sources_Spec_HBM.rtf Dr. Hadj Boumediene Meziane: hbmeziane@gmail.com This script compares EEG source spectra then compares Perf vs. Fail conditions then plot statistical results (significant voxels) on MRI volume

opencc-by-4.0Aug 2023View details →

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Data from: Determining critical periods for thermal acclimatisation using a Distributed Lag Non-linear Modelling approach

Rapid changes in thermal environments are threatening many species worldwide. Thermal acclimatisation processes may partially buffer species from the impacts of these changes, but currently the knowledge about the temporal dynamics of acclimatisation remains limited. Acclimatisation phenotypes are typically determined in laboratory conditions that lack the variability and stochasticity that characterize the natural environment. Through a Distributed Lag Non-linear Model (DLNM), we use field data to assess how the timing and magnitude of past thermal exposures influence thermal tolerance. We apply the model to two Scottish freshwater Ephemeroptera species living in natural thermal conditions. Model results provide evidence that rapid heat hardening effects are dramatic and reflect high rates of change in temperatures experienced over recent hours to days. In contrast, temperature change magnitude impacted acclimatisation over the course of weeks but had no impact on short term responses. Our results also indicate that individuals may de-acclimatise their heat tolerance in response to cooler environments. Based on the novel insights provided by this powerful modelling approach, we recommend its wider uptake among thermal physiologists to facilitate more nuanced insights in natural contexts, with the additional benefit of providing evidence needed to improve the design of laboratory experiments.

opencc-zeroMay 2024View details →

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Global soil moisture simulated by SoilClim and mHM models at 0.5° resolution for the 1980–2022 period

This deposit contains two .zip archives (SoilClim_AWR_2m_1980_2022.zip and mHM_SM_2m_1980_2022.zip), each containing 1570 GeoTIFF files.  The file SoilClim_AWR_2m_1980_2022.zip contains 10-day simulations of relative available water (AWR), where 100% represents the full field capacity and 0% represents the wilting point, produced the SoilClim water balance model for the 2.0 m soil depth, covering a global nonglaciated land with a 0.5° resolution, excluding latitudes above 72° N and all of Antarctica, for the 1980–2022 period. The file mHM_SM_2m_1980_2022.zip contains 10-day simulations of soil moisture (SM), produced the mesoscale Hydrologic Model (mHM) for the 2.0 m soil depth, covering a global nonglaciated land with a 0.5° resolution, excluding latitudes above 72° N and all of Antarctica, for the 1980–2022 period.

opencc-by-4.0May 2024View details →

zenodo40/100

Figure 1 in Monitoring the feeding and parental care behavior of a pair of free-living owls (Tyto furcata) in the nest during the reproductive period in Rio de Janeiro, Brazil

Figure 1. Couple of Tyto furcata image captured by the security camera positioned opposite from the nest.Campos dos Goytacazes, RJ.

opencc-by-4.0Sep 2024View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record