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4,276 results for “Estimation”
Raw data and scripts for "The Period-Modulated Harmonic Locked Loop (PM-HLL): A low-effort algorithm for rapid time-domain multi-periodicity estimation"
<p>This package contains all required scripts to generate the simulations and figures from the study "The Period-Modulated Harmonic Locked Loop (PM-HLL): A low-effort algorithm for rapid time-domain multi-periodicity estimation" by Volker Hohmann, published in Acta Acustica:</p> <p>The Period-Modulated Harmonic Locked Loop (PM-HLL): A low-effort algorithm for rapid time-domain multi-periodicity estimation<br>Volker Hohmann<br>Acta Acust. 5 56 (2021)<br>DOI: 10.1051/aacus/2021050</p> <p>When referring to this work, please cite the journal paper.</p> <p>Note that additive noise is generated at random, i.e., small differences in the estimation accuracy occur when repeating a simulation. For further details see the journal paper.</p> <p>Thank you for downloading the package. Your comments are very welcome!</p> <p>Method patented: DE Patent DE102021207339B3</p> <p>Author: Volker Hohmann, Carl von Ossietzky Universität Oldenburg, Germany</p>
Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision and Reproducibility
<p>This zip contains all input structures for paper the: Alchemical Free<br> Energy Estimators and Molecular Dynamics<br> Engines: Accuracy, Precision and Reproducibility</p> <p>Authors: Alexander D. Wade, Agastya P. Bhati, Shunzhou Wan, Peter V.Coveney</p> <p>The structures of the folders are protein/ligand_transformation/alchemical_leg/input/files</p> <p>The ligand transformation are derived from previous work by wang et al. (https://pubs.acs.org/doi/10.1021/ja512751q)</p> <p>There are two files for the solvent alchemical leg: complex.pdb and complex.prmtop</p> <p>complex.pdb is structure file that also denotes the alchemical atoms in the pdb beta column. complex.prmtop is an AMBER parameter/topology file</p> <p>For the complex alchemical leg there is an additional file constraints.pdb that contains the constraint information in the pdb beta column.</p> <p>These files can be used with TIES_MD (https://ucl-ccs.github.io/TIES_MD/) or other molecular dynamics engiens that take AMBER input.</p>
Improving surface melt estimation over the Antarctic Ice Sheet using deep learning: a proof of concept over the Larsen Ice Shelf
<p>Hu, Z., Kuipers Munneke, P., Lhermitte, S., Izeboud, M., and van den Broeke, M.: Improving Surface Melt Estimation over Antarctica Using Deep Learning: A Proof-of-Concept over the Larsen Ice Shelf, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-102, in review, 2021.</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>(1) MLP_model_surface_melt_corr.h5</strong> is the developed MLP model used for correcting RACMO2 surface melt.</p> <p><strong>(2) RACMO2_surface_melt_corr_MLP_AWS14.xlsx </strong>corrected surface melt [mm w.e. per day] from RACMO2 at AWS 14 during austral summers 2001 - 2016. The model inputs are (1) the simulated albedo, (2) the albedo difference between the observed and simulated albedo, (3) air temperature at 2m, (4) incoming shortwave radiation, (5) downwelling longwave radiation, (6) simulated surface melt, (7) Boolean melt flag, (8) surface melt difference to the previous day, and (9) record date as day of the year.</p> <p><strong>(3) RACMO2_surface_melt_corr_MLP_AWS17.xlsx </strong>The same as point 2 but for AWS 17</p> <p><strong>(4) RACMO2_surface_melt_corr_MLP_AWS18.xlsx </strong>The same as point 2 but for AWS 18</p> <p>Note: Data 2-4 are corrected RACMO2 simulations of surface melt at the pixels in RACMO2 27 km grid corresponding to AWS 14, 17, and 18 locations. They are not AWS observations.</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Related data set:</strong></p> <p>MODIS/Terra Surface Reflectance Daily L2G Global 1 km and 500 m SIN Grid product is available via the Land Processes Distributed Active Archive Center (LP DAAC) (https://doi.org/10.5067/MODIS/MOD09GA.006, last access: 3 December 2021). MODIS/Terra+Aqua Albedo Daily L3 Global 500 m SIN Grid product is also available via LP DAAC (https://doi.org/10.5067/MODIS/MCD43A3.006, last access: 3 December 2021). Sentinel-1 images are provided by the European Space Agency (ESA) (https://sentinel.esa.int/web/sentinel/sentinel-data-access, last access: 3 December 2021). Automatic weather station observations from AWS 14, 17, and 18 are available via https://doi.pangaea.de/10.1594/PANGAEA.910473 (last access: 3 December 2021). RACMO2 simulations (https://www.projects.science.uu.nl/iceclimate/models/antarctica.php#2-1, last access: 3 December 2021) are provided by van Wessem et al. (2018) which are available on request to the original authors.</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>You should also refer to and cite the following paper:</strong></p> <p>Hu, Z., Kuipers Munneke, P., Lhermitte, S., Izeboud, M., and van den Broeke, M.: Improving Surface Melt Estimation over Antarctica Using Deep Learning: A Proof-of-Concept over the Larsen Ice Shelf, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-102, in review, 2021.</p>
Underling data for "The Battery Life Estimation of a Battery under Different Stress Conditions"
<p>Each file reports the capacity trend as a function of the number of cycles, for NMC cells aged according to different protocols, as reported in the following table:</p> <table> <tbody> <tr> <td> <p><strong>Test Number</strong></p> </td> <td> <p><strong>Discharge Current (C-Rate)</strong></p> </td> <td> <p><strong>ΔSOC = SOCin − SOCfin</strong></p> </td> <td> <p><strong>Cycle between Control Tests</strong></p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>1 C</p> </td> <td> <p>80−20</p> </td> <td> <p>200</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>2 C</p> </td> <td> <p>80−20</p> </td> <td> <p>200</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>3 C</p> </td> <td> <p>80−20</p> </td> <td> <p>100 <sup>1</sup></p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>5 C</p> </td> <td> <p>80−20</p> </td> <td> <p>100</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>1 C</p> </td> <td> <p>90−10</p> </td> <td> <p>160</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>1 C</p> </td> <td> <p>70−30</p> </td> <td> <p>320</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p>5 C</p> </td> <td> <p>70−30</p> </td> <td> <p>320</p> </td> </tr> <tr> <td> <p>8</p> </td> <td> <p>3 C</p> </td> <td> <p>90−10</p> </td> <td> <p>160 </p> </td> </tr> <tr> <td> <p>9</p> </td> <td> <p>8 × 3C@80%DOD + 10 × 2C@40%DOD</p> </td> <td> <p>90−10, 70−30</p> </td> <td> <p>90</p> </td> </tr> </tbody> </table> <p>The number of the file corrrespond to the test number (e.g. B1 referers to results for a cell undergone test 1). Cells tested where 20 Ah EIG NMC-graphite.</p> <p>In order to compare the results for cells subjected to life tests with different ΔSOCs, it is necessary to appropriately quantize the cumulative charge, to obtain a suitably defined equivalent cycle. To this end, we consider the greatest common factor (GCF) among ΔSOCs. We then define the number of equivalent cycles (ECs) of each life test as the ratio between the ΔSOC value and GCF. In our case, we considered 3 ΔSOC: 40%, 60%, and 80%. The GCF is therefore 20%. Hence, the number of EC equals 2, 3, and 4 for each cycle at ΔSOC 40, 60, and 80%, respectively. Obviously, this is not the only quantization possible, but a different choice does not affect the validity of the model, as long as the transformation is linear, although it would lead to different results for the parameters.</p> <p>The capacity has been evaluated with a standard charge-discharge cycle at 0.5C rate, after a given number of test cycles, as reported in the files. All files are structured as follows:</p> <table> <tbody> <tr> <td>Column number</td> <td>1</td> <td>2</td> <td>3</td> </tr> <tr> <td>Field description</td> <td>Number of cycles</td> <td>Number of equivalrent cycles</td> <td>Relative capacity</td> </tr> </tbody> </table> <p>Relative capacity: ratio between the capacity value after N cycle and the capacity value at the beginning of life.</p>
Estimation of sulfuric acid concentrations using ambient ion composition and concentration data obtained by ion mass spectrometry measurements (APi-TOF)
<p>The dataset has been used to estimate the sulfuric acid concentration from APi-TOF data. Using the bisulfate ion, the sulfuric acid molecule clustered with the bisulfate ion (dimer) and the trimer, we were able to determine the daytime concentration of sulfuric acid with the concentration of ambient ions, including the condensation sink, ion-ion recombination and collision rate of HSO4- with H2SO4.</p>
Data from: Multi-image flock size estimation with CountEm: A case study with half a million Common Eiders and Greater Snow Geese.
<p>The present data set is related to the manuscript "Multi-image flock size estimation with CountEm: A case study with half a million Common Eiders and Greater Snow Geese" submitted to the Ecosphere journal.</p> <p>The files COEI_data.csv and GSGO_data.csv contain data and results of the 179 COEI and 99 GSGO ECA Flocks images respectively. The files have one row per image. Both files have 9 columns corresponding to the following variables:</p> <ul> <li> “Filenumber” is used to identify the number of the corresponding image, namely “001” to “099” for GSGO, and “001” to “179” for COEI.</li> <li> “N”: Total number of annotated birds in the image.</li> <li> “Nest”: Bird number estimation (bN 439 ) obtained with a single real mode CountEm run.</li> <li> “CE_sim”: Empirical coefficient of error (relative standard error, CEe(bN 440 )) obtained from 2000 simulated measurements in simulation mode.</li> <li> “f”: Sampling fraction used in the real mode CountEm run.</li> <li>“n0”: Initial number of quadrats used in the real mode CountEm run.</li> <li>“Q”: Total number of birds (i.e. sample size) counted in the real mode CountEm estimation.</li> <li> “n”: Number of non-empty quadrats counted in the real mode CountEm estimation.</li> <li> “UserTime”: Counting time (in minutes) in the real mode CountEm estimation.</li> <li> “ManualTime”: Counting time (in minutes) of the manual annotation process with ImageJ.</li> </ul>
Database of survival and recapture estimates for odonates
<p>This excel file contains a review of published and unpublished estimates of survival and recapture rates for Odonata.</p> <p>The main data are in sheet "sex-age". Other sheets include references that were not used for the meta-analysis, and some statistical treatments and graphics.</p> <p>It has been used for the book chapter "<strong>Odonata survival: insights from mark-recapture experiments</strong>", of the book "<strong><em>Dragonflies and damselflies: Model organisms for ecological and evolutionary research, Second Edition"</em></strong>, edited by Alex Córdoba-Aguilar, Christopher D. Beatty, Jason T. Bried & Catalina M. Suárez-Tovar.</p> <p> </p>
Data from: How to date a crocodile – estimation of neosuchian clade ages and a comparison of four time-scaling methods
<p>Clade ages within the crocodylomorph clade Neosuchia have long been debated. Molecular and morphological studies have yielded remarkably divergent results. Despite recent advances, there has been no comprehensive relative comparison of the major time calibration methods available to estimate clade ages based on morphological data. We used four methods (cal3, Extended Hedman [EH], smoothed Ghost-Lineage-Analysis [sGLA] and the Fossilised Birth-Death model [FBD]) to date clade ages derived from a published crocodylomorph supertree and a new neosuchian phylogeny. All time-scaling methods applied here agree on the origination of Neosuchia during the Late Triassic/Early Jurassic, and the presence of the major extant eusuchian groups (Crocodyloidea, Gavialoidea, Alligatoroidea, and Caimaininae) by the end of the Late Cretaceous. The number of distinct lineages present before the K/Pg boundary is less certain, with support for two competing scenarios in which Crocodylinae, Tomistominae and Diplocynodontinae either: 1) diverged from other eusuchian lineages before the K/Pg boundary; or 2) evolved during a 'burst' of diversification after the K/Pg event. Cal3 and FBD are identified as the most suitable methods for time-scaling phylogenetic trees dominated by fossil taxa. Extended Hedman estimates are substantially older than the others, with larger standard deviations and a strong vulnerability to taxon sampling and topological changes. sGLA has similar problems and cannot be recommended either. We conclude that a detailed understanding of phylogenetic relationships, tree reconstruction methods, and good taxonomic coverage (in particular the inclusion of the oldest taxon in each clade) is essential when evaluating the results of such dating analyses.</p>
Imputed Multiple Sequence Alignment used in 'Estimating the relative proportions of SARS-CoV-2 strains from wastewater samples'
<p>Multiple Sequence Alignment of imputed SARS-CoV-2 sequences used in 'Estimating the relative proportions of SARS-CoV-2 strains from wastewater samples'</p>
Estimating Orchestration Load in Collaborative Learning Situations Using EDA - Activity 6
<p>Skin conductivity of the teacher while orchestrating a Pyramid activity. The green highlight indicates an SCR concurred with the teacher report: "When students told me that after increasing time in the "improving phase" they could not continue editing their improved answer.".</p>
Estimating Orchestration Load in Collaborative Learning Situations Using EDA - Activity 2
<p>Skin conductivity of the teacher while orchestrating a Pyramid activity. During this activity, the teacher reported: "I noticed that the scenario for the task that I shared with student was not the one I planned (I have several ... and was confused with the one I picked) so I had to read the scenario as well .. while students where completing the Pyramid activity. In any case I know all scenarios very well and was quick for me to remember it.".</p>
Estimating Orchestration Load in Collaborative Learning Situations Using EDA - Activity 3
<p>Skin conductivity of the teacher while orchestrating a Pyramid activity. The teacher did not report any stressful situation during this activity.</p>
Estimating Orchestration Load in Collaborative Learning Situations Using EDA - Activity 1
<p>Skin conductivity of the teacher while orchestrating a Pyramid activity. The green highlight indicates an SCR concurred with the teacher report: "I was running out of time, and needed to reduce time in the XXX activity. However I'm used to this kind of situations, and was not highly stressed."</p>
Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery
<p><strong>Power Generation Data Set</strong></p> <p>This data set contains imaging data acquired by ESA's Sentinel-2<br> Earth-observing satellite constellation [1] for a sample of power stations that were picked using geographic coordinates <br> provided by the European Pollutant Release and Transfer Register [2]. The images<br> contain scenes of power stations, some of which are actively<br> emitting smoke plumes.</p> <p>This data set was created with the goal to automatically segment plumes, predict the type of fired fuel, predict the rate of power generation and estimate the amount of CO2 emissions, directly from remote sensing images.</p> <p><br> <strong>Description</strong><br> </p> <p>Each image is provided in the GeoTIFF file format, contains a total of 13 bands. Images have either a shape of 120x120 or 300x300 pixels (corresponding to a square area with an edge length of respectively 1.2 km and 3.0 km on the ground)<br> .</p> <p>This repository contains a total of 2131 images. This<br> repository contains a collection of JSON files that hold manual segmentation labels for plumes. Segmentation<br> labels were generated using label-studio [3]. Please note that polygon edge coordinates have to be scaled to fit the images.</p> <p><br> <strong>Content</strong></p> <p>The following files are contained in this repository:</p> <ul> <li>README.md - this file</li> <li>images.zip [2.0GB] - contains 2131 GeoTIFF images</li> <li>segmentation_labels.zip [1.5MB] - contains 2131 JSON files</li> <li>labels.csv [310KB] - contains additional labels for each image: <ul> <li>Generation output rate [4],[5]</li> <li>Country</li> <li>Type of fired fuel</li> <li>Latitude and longitude of the power plant</li> <li>Concurrent weather information (temperature, humidity and wind vector)</li> </ul> </li> </ul> <p> </p> <p><strong>Acknowledgement</strong></p> <p>If you use this data set, please cite our publication:</p> <p> Hanna, J., Mommert, M., Scheibenreif, L., Borth, D.,<br> "Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery",<br> Tackling Climate Change with Machine Learning workshop at NeurIPS 2021.</p> <p>Please refer to this publication for additional information on the data set.</p> <p>The code used for this publication is available at https://github.com/HSG-AIML/RemoteSensingCO2Estimation.</p> <p> </p> <p><br> <strong>Author</strong></p> <p>Joëlle Hanna</p> <p>University of St. Gallen, AIML Lab, School of Computer Science joelle.hanna@unisg.ch</p> <p><br> <strong>References</strong><br> </p> <p>[1]: https://earth.esa.int/web/sentinel/missions/sentinel-2<br> [2]: https://www.eea.europa.eu/data-and-maps/data/industrial-reporting-under-the-industrial<br> [3]: https://labelstud.io/<br> [4]: https://transparency.entsoe.eu/generation/r2/actualGenerationPerGenerationUnit/show<br> [5]: https://doi.org/10.5281/zenodo.3574566</p>
ModEst - Precise estimation of genome size from NGS data
<p>Accurate estimates of genome sizes are important parameters for both theoretical and practical biodiversity genomics. We present here a fast, easy-to-implement and precise method to estimate genome size from the number of bases sequenced and the mean sequencing depth. To estimate the latter, we take advantage of the fact that a precise estimation of the Poisson distribution parameter lambda is possible from truncated data, restricted to the part of the sequencing depth distribution representing the true underlying distribution. With simulations we could show that reasonable genome size estimates can be gained even from low-coverage (10X), highly discontinuous genome drafts. Comparison of estimates from a wide range of taxa and sequencing strategies with flow-cytometry estimates of the same individuals showed a very good fit and suggested that both methods yield comparable, interchangeable results.</p>
Improving estimations of life history parameters of small animals in mesocosm experiments: A case study on mosquitoes
<p>We used an experimental setup with 48 aquatic mesocosms, each with twenty first instar mosquito (<em>Culex pipiens</em>) larvae and under one of twelve treatments with varying temperatures and nutrient concentrations. We took daily subsamples of the aquatic life stages as well as counting the emerging adults. We developed a method to estimate the survival and development probabilities at each life stage, based on optimising a matrix population model. We used two different approaches, one calculating the difference between predictions and observations based on a normal distribution, and the other using a combination of a normal and a multinomial distribution. For each approach, the resulting optimisation problem had around 100 parameters, making conventional gradient descent ineffective with our limited number of data points. We solved this by computing the formal derivatives of our matrix model.</p>
Recent estimate of Asian elephants in Borneo reveals a smaller population
<p>Asian elephants occurring in northern Borneo form a geographically isolated and genetically distinct population. Of this, the subpopulation of Central Sabah holds the greatest opportunity for long-term survival, due to a relatively large population size and occurrence over a vast, contiguous, and protected habitat. We surveyed this subpopulation in 2015 using advanced methods to obtain a population size estimate. We used the distance-sampling framework and laid out transects following a stratified random design for counting elephant dung piles; measured dung decay following the 'retrospective' method; and used Bayesian analysis to estimate dung decay rate and dung pile density. Thus, we estimated a posterior mean dung decay rate of 212 days (95% BCI: 133–319), an overall elephant density of 0.07 per km<sup>2</sup> (95% BCI: 0.03–0.11), and a population size of 387 elephants (95% BCI: 169–621). These estimates were far lower than the population size of 1132 individuals and density of 1.18 per km<sup>2 </sup>estimated in 2008. It is unlikely that there has been a steep population decline, as there were no drastic land-use changes between 2008 and 2015, nor were there other identifiable causes for a population decline. Therefore, it appears that the methodological and analytical flaws in the previous estimate are the most plausible reason for this observed difference. Given that the new estimate suggests a much smaller population, it is prudent and precautionary to use the new estimate as the basis for all policy decisions and conservation actions for elephants in Sabah.</p>
Candidate-species delimitation in Desmognathus salamanders reveals gene flow across lineage boundaries, confounding phylogenetic estimation and clarifying hybrid zones
Dusky Salamanders (genus Desmognathus) currently comprise only 22 described, extant species. However, recent mitochondrial and nuclear estimates indicate the presence of up to 49 candidate species based on ecogeographic sampling. Previous studies also suggest a complex history of hybridization between these lineages. Studies in other groups suggest that disregarding admixture may affect both phylogenetic inference and clustering-based species-delimitation. With a dataset comprising 233 Anchored Hybrid Enrichment (AHE) loci sequenced for 896 Desmognathus specimens from all 49 candidate species, we test three hypotheses regarding i) species-level diversity, ii) hybridization and admixture, and iii) misleading phylogenetic inference. Using phylogenetic and population-clustering analyses considering gene flow, we find support for at least 47 candidate species in the phylogenomic dataset, some of which are newly characterized here while others represent combinations of previously named lineages that are collapsed in the current dataset. Within these, we observe significant phylogeographic structure, with up to 64 total geographic genetic lineages, many of which hybridize either narrowly at contact zones or extensively across ecological gradients. We find strong support for both recent admixture between terminal lineages and ancient hybridization across internal branches. This signal appears to distort concatenated phylogenetic inference, wherein more heavily admixed terminal specimens occupy apparently artifactual early diverging topological positions, occasionally to the extent of forming false clades of intermediate hybrids. Additional geographic and genetic sampling and more robust computational approaches will be needed to clarify taxonomy, and to reconstruct a network topology to display evolutionary relationships in a manner that is consistent with their complex history of reticulation. --
MODYS-video: 2D Human pose estimation data and Dyskinesia Impairment Scale scores from children and young adults with dyskinetic cerebral palsy
<p>The dataset contains the 2D coordinates in pixels of body landmarks (wrists, ankles, shoulders, hips, knees and ankles) extracted from 188 videos of 34 children with dyskinetic cerebral palsy using DeepLabCut [1] and appertaining clinical scores of the Dyskinesia Impairment Scale (DIS) [2].</p> <p>The videos were collected during the item “lying in rest” and “sitting in rest” of the DIS at three time points during a clinical trial on the effect of intrathecal baclofen [3]. Children had a mean age of 14y2m (SD 4.0), 26 were male. Their gross motor function classification system level ranged from IV-V and their manual ability classification system level from III-V. Original videos have length of 4-35 seconds with a resolution of 720x575 pixels and are sampled with 25 Hz. We added stick figures to complement the data for context and ease of understanding. They were created from the 2D coordinates that were extracted with a likelihood >0.8.</p> <p>Clinical scoring was performed by three trained experts (according to the DIS) on the original videos. Within the items “lying in rest” and “sitting in rest” the amplitude and duration of dystonia and choreoathetosis of the trunk, proximal right arm, proximal left arm, proximal right leg and proximal left leg are scored on a 0-4 ordinal scale and calculated towards a percentage score between 0-1.</p> <p>The dataset can be used in a machine learning approach to automatically assess dystonia and choreoathetosis of children with dyskinetic cerebral palsy using 2D coordinates of body points extracted from videos.</p> <p> </p> <p>References:</p> <p>1. Mathis, A., et al., DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat Neurosci, 2018. 21(9): p. 1281-1289.</p> <p>2. Monbaliu, E., et al., The dyskinesia Impairment Scale: a new instrument to measure dystonia and choreoathetosis in dyskinetic cerebral palsy. Dev Med Child Neurol, 2012. 54: p. 278-283.</p> <p>3. Bonouvrie, L.A., et al., The Effect of Intrathecal Baclofen in Dyskinetic Cerebral Palsy: The IDYS Trial. Ann Neurol, 2019. 86: p. 79-90.</p> <p> </p> <p> </p>
Influence of motor and cognitive tasks on time estimation
<p>The folder<strong> "DataTapisTime" </strong>contains <strong>thirty-two .MAT files.</strong> The .mat files’ name includes the number of subject (“SXX”, from S01 to S16) and the condition (Sitting or Walking). Each matrix contains 7 columns:</p> <ul> <li><strong>Column 1:</strong> Trial number</li> <li><strong>Column 2:</strong> Cognitive task code – 1) Look; 2) Read; 3) Solve Simple; 4) Solve Hard</li> <li><strong>Column 3:</strong> Time interval code – 1) 15 sec; 2) 30 sec; 3) 60 sec; 4) 90 sec; 5) 120 sec</li> <li><strong>Column 4:</strong> Tested duration (in seconds)</li> <li><strong>Column 5:</strong> Exact duration measured online (as a control)</li> <li><strong>Column 6:</strong> If the trial was “look” or “read” task the structure is composed only by “NaN”. In the two solve tasks the structure contains information about the operations to be solved. It contains 6 columns: 1) number1; 2) number2; 3) Participant solution; 4) Reaction time; 5) Correct solution; 6) Response correction (0: Wrong; 1: correct: NaN=not given).</li> <li><strong>Column 7:</strong> Temporal estimation (in seconds)</li> </ul>
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.