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1,408 results for “Explainability”
Dataset for "Holocene hydroclimatic variability in the tropical Pacific explained by changing ENSO diversity."
<p>This repository contains the tropical Pacific sea surface temperature and global precipitation data from the CESM1 time slice experiments, which were used for the analysis presented in Karamperidou & DiNezio (2022), Nature Communications (https://www.nature.com/articles/s41467-022-34880-8)</p> <p> </p> <p>From Karamperidou & DiNezio (2022):</p> <p>“To assess the response of ENSO flavors to orbital forcing over the past 12,000 years (12ka), we use a suite of time-slice experiments in 3ka intervals with version 1 of the Community Earth System Model (CESM1). Each experiment is 400-600 years long and was run until the surface climate and oceanic processes controlling tropical climate, such as the depth of the thermocline in the equatorial Pacific or the Atlantic Meridional Overturning Circulation (AMOC), have reached equilibrium. All simulations exhibit minimal drift in global mean surface temperature (less than 0.05<sup>o</sup>C per century), tropical mean surface temperature (less than 0.04<sup>o</sup>C per century), the depth of the equatorial thermocline in the Pacific (less than 0.3m per century), and the strength of the AMOC (less than 0.25 Sv per century) during the periods used in the analyses. With the exception of the 12 ka BP interval which includes ice sheet changes and lower greenhouse gases, the primary forcing in the 0, 3, 6, and 9 ka BP intervals is changes in Earth's precession, and each simulation branched off its preceding one, starting from 0ka sequentially through the Holocene. The maximum TOA energetic imbalance does not exceed 0.45 Wm<sup>-2</sup>, which is much smaller than the imposed radiative forcing.”</p> <p> </p> <p> </p> <p>Karamperidou, C., DiNezio, P.N. Holocene hydroclimatic variability in the tropical Pacific explained by changing ENSO diversity. <em>Nat Commun</em> <strong>13</strong>, 7244 (2022). https://doi.org/10.1038/s41467-022-34880-8</p>
Dataset for 'Measuring and explaining disagreement about bird taxonomy'
<p>Dataset used for a research project that measures, classifies and explains disagreement about bird taxonomy. This is version 3, which has new data on research effort for each of the birdlife concepts, and no longer contains data about ecological and geographical predictors. This version of the data is used in the submission to EJT in november 2023.</p>
Data from: Padfield et al. (2016) Rapid evolution of metabolic traits explains thermal adaptation in phytoplankton. Ecology letters.
<p>This repository provides the data from the TPC and logistic growth curves from the paper:</p> <p>Padfield, D., Yvon‐Durocher, G., Buckling, A., Jennings, S., & Yvon‐Durocher, G. (2016). Rapid evolution of metabolic traits explains thermal adaptation in phytoplankton. Ecology letters, 19(2), 133-142.</p> <p>metadata.pdf gives a more detailed explanation of the data.</p>
Priority effects can be explained by competitive traits
<p>Code (updated after review)</p> <p>Traits - Trait values that were measurent and calculated from the individuals grown alone (updated after review).</p> <p>See version 2 for other files </p>
East African topography and volcanism explained by a single, migrating plume: supplementary data
<p>These data accompany the following paper:</p> <p>Hassan, R., Williams, S.E., Gurnis, M. and Müller, D., 2020. East African topography and volcanism explained by a single, migrating plume. <em>Geoscience Frontiers</em>, <em>11</em>(5), pp.1669-1680.</p> <p>The data (in simple text form) correspond to the dynamic topography and change in dynamic topography shown in Figure 7.</p>
Morpho-anatomical traits explain the effects of bacterial-feeding nematodes on soil bacterial community composition and plant growth and nutrition
<p>Soil Bacterial populations</p> <p>V3-V4, of the 16S rRNA gene using the primers 341F CCTAYGGGRBGCASCAG and 806R GGACTACNNGGGTATCTAAT.</p>
Validation Videos and eXplainable levels used in the Validation of the Conflict Detection and Resolution Use Case (ARTIMATION)
<p>This dataset contains the <strong>explaination levels and the video </strong>used in the validation of the Conflict Detection and Resolution (CD&R) use case.</p> <p>The solution are computed by a Genetic Algorithm developped by Nicolas Durand.<br> <br> Inside the dataset, one can find:</p> <p>-One archive, " Validation_Videos_Traffic.zip ", containing the video of traffic of every scenario.</p> <p>-One archive, " Validation_XAI_levels.zip", containing the Blackbox, Heatmap, and Storyboard eXplainable levels for each scenario.</p> <p>The videos and XAI levels are used in the validation exercice.</p>
Model output used in the manuscript "Seasonality in carbon flux attenuation explains spatial variability in transfer efficiency"
<p>This *.zip file contains the model output from seasonal variability experiments using the NPZD-DOP GEOMAR biogeochemical model (<a href="https://doi.org/10.1016/j.pocean.2010.05.002" target="_blank" rel="noopener">Kriest et al., 2010</a>) coupled with the MITgcm 2.8deg ocean circulation via the transport matrix method (<a href="https://doi.org/10.1016/j.ocemod.2004.04.002" target="_blank" rel="noopener">Khatiwala et al., 2005</a>; <a href="https://doi.org/10.1029/2007GB002923" target="_blank" rel="noopener">Khatiwala, 2007</a>; <a href="https://doi.org/10.5281/zenodo.1246300" target="_blank" rel="noopener">Khatiwala, 2018</a>).</p> <p>These model outputs are presented and discussed in the Preprint "<em>Seasonality in carbon flux attenuation explains spatial variability in transfer efficiency</em>", published by Geophysical Research Letters (<a href="https://doi.org/10.1029/2023GL107050" target="_blank" rel="noopener">de Melo Viríssimo et al., 2024</a>). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original model. For this matter, we also refer you to <a href="https://doi.org/10.1029/2021GB007101" target="_blank" rel="noopener">de Melo Viríssimo et al. (2022)</a>.</p> <p>All files uploaded were generated from simulations run by the authors, except: the grid file, the salinity field, and the temperature field, which came with the model; and the density fields, who were computed from the MITgcm 2.8deg transport matrix by Dr Rafaelle Bernadello, using a TEOS-10 Matlab routine (<a href="http://www.teos-10.org/">http://www.teos-10.org/</a>).</p> <p>For specific information about each file uploaded, please refer to the README file. If you have any questions, please feel free to contact me.</p>
Publishing reproducible logbooks explainer comic strip
<p>This comic strip explains at a high level how to publish reproducible<br> notebooks using tools and services such as Jupyter and Binder.</p> <p>Files:<br> - reproducible_logbook.png: main picture<br> - reproducible_logbook_scenario.png: zoom on the scenario part of the picture<br> - reproducible_logbook.kra: original Krita source file<br> - reproducible_logbook_texts.svg: svg export (just the texts)<br> - reproducible_logbook_wo_text.png: png export without the texts (e.g. for translations)</p>
On demand customizable Virtual Environments with JupyterHub explainer comic strip
<p>This comic strip explains at a high level the convergence between JupyterHub and Binder to enable institutions to deploy a service for their members and users provisionning on demand customizable Jupyter-based Virtual Environments.</p> <p>Files:</p> <ul> <li>Community.png: main picture</li> <li>Community_text.svg: svg export: just the text</li> <li>Community_wo_text.svg: svg export: just the background image (e.g. for translations)</li> </ul>
Kin selection explains the evolution of cooperation in the gut microbiota, by Simonet & McNally, 2020, Dataset S1 and codes for statistical analysis and figures production
<p>Dataset S1 contains all raw and processed material referred to in the published article "Kin selection explains the evolution of cooperation in the gut microbiota". R codes files provide all codes to replicate the analysis. Please refer to the README file for a description of all code files. The manifest files are those obtained by accessing the HMP portal on April 2020 under Project > HMP, Body Site > feces, Studies>WGS-PP1, File Type > WGS raw sequences set, File format > FASTQ.</p> <p>We also provide access to these data and codes at our GitHub (https://github.com/CamilleAnna/HamiltonRuleMicrobiome gitRepos.git) which can be cloned to directly re-run this analysis. </p> <p><strong>Legends for Dataset S1:</strong></p> <ul> <li>Sheet 1: Metagenomic samples used and access links.</li> <li>Sheet 2: Reference on bacterial cooperation retrieved from Web of Science search: TI¯((microb* OR bacter* OR microorganis* OR micro-organis*) AND (coop* OR social*)</li> <li>Sheet 3: Retained bacteria cooperation keywords</li> <li>Sheet 4: GOs identified by annotating all MIDAS database genomes (5944 genomes) with PANNZER2.</li> <li>Sheet 5: Full list of potential bacterial cooperation GO terms and description of manual curation decisions.</li> <li>Sheet 6: Final list of bacterial cooperation GO used for the analysis</li> <li>Sheet 7: Genomic diversity of the bacterial population within and across host. Computed from MIDAS snp_diversity.py pipeline.</li> <li>Sheet 8: final dataset for statistical analysis.</li> <li>Sheet 9: per-gene annotation of cooperation.</li> </ul>
A framework for step-wise explaining how to solve constraint satisfaction problems
<p>We explore the problem of step-wise explaining how to solve constraint satisfaction problems, with a use case on logic grid puzzles. More specifically, we study the problem of explaining the inference steps that one can take during propagation, in a way that is easy to interpret for a person. Thereby, we aim to give the constraint solver explainable agency, which can help in building trust in the solver by being able to understand and even learn from the explanations. The main challenge is that of finding a sequence of simple explanations, where each explanation should aim to be as cognitively easy as possible for a human to verify and understand. This contrasts with the arbitrary combination of facts and constraints that the solver may use when propagating. We propose the use of a cost function to quantify how simple an individual explanation of an inference step is, and identify the explanation-production problem of finding the best sequence of explanations of a CSP. Our approach is agnostic of the underlying constraint propagation mechanisms, and can provide explanations even for inference steps resulting from combinations of constraints. In case multiple constraints are involved, we also develop a mechanism that allows to break the most difficult steps up and thus gives the user the ability to zoom in on specific parts of the explanation. Our proposed algorithm iteratively constructs the explanation sequence by using an optimistic estimate of the cost function to guide the search for the best explanation at each step. Our experiments on logic grid puzzles show the feasibility of the approach in terms of the quality of the individual explanations and the resulting explanation sequences obtained.</p>
Data and documentation from: Microclimate explains little variation in year-round decomposition across an Arctic tundra landscape
<p>The zip file contains data and code to reproduce the analysis in the submitted manuscript entitled <i>Microclimate explains little variation in year-round decomposition across an Arctic tundra landscape</i>. Please see the manuscript for further details on background, methodology, results and discussion.</p>
Datasets and results of the paper titled "Are citation networks relevant to explain academic promotions? An empirical analysis of the Italian national scientific qualification"
<p>These are the <strong>input datasets</strong> and the <strong>results of the analyses</strong> reported on the paper titled <strong>"Are citation networks relevant to explain academic promotions? An empirical analysis of the Italian national scientific qualification"</strong>.</p> <p><strong>Abstract:</strong> </p> <p>The aim of this paper is to study the role of citation network measures in the assessment of scientific maturity. Referring to the case of the Italian national scientific qualification (ASN), we investigate if there is a relationship between citation network indices and the results of the researchers’ evaluation procedures. In particular, we want to understand if network measures can enhance the prediction accuracy of the results of the evaluation procedures beyond basic performance indices. Moreover, we want to highlight which citation network indices prove to be more relevant in explaining the ASN results, and if quantitative indices used in the citation-based disciplines assessment can replace the citation network measures in non-citation-based disciplines. Data concerning Statistics and Computer Science disciplines are collected from different sources (ASN, Italian Ministry of University and Research, and Scopus) and processed in order to calculate the citation-based measures used in this study. Following, we apply classification models to estimate the effects of network variables. We find that network measures are strongly related to the results of the ASN and significantly improve the explanatory power of the models, especially for the research fields of Statistics. Additionally, citation networks in the specific sub-disciplines are far more relevant than those in the general disciplines. Finally, results show that the citation network measures are not a substitute of the citation-based bibliometric indices.</p> <p><strong>Code</strong></p> <p>The code to collect and process the data used in this paper is available on GitHub at <a href="https://github.com/DigitalDataLab/ASN16-18_CitationNetwork">https://github.com/DigitalDataLab/ASN16-18_CitationNetwork</a><strong>.</strong> </p> <p><strong>Dataset description</strong></p> <p>The files <strong>AdjacencyMatrix_01B1.csv</strong>, <strong>AdjacencyMatrix_09H1.csv</strong>, <strong>AdjacencyMatrix_13D1.csv</strong>, <strong>AdjacencyMatrix_13D2.csv</strong> and <strong>AdjacencyMatrix_13D3.csv</strong> are the citation matrices for Italian academics (i.e. ASN candidates and permanent positions in the Italian academic system) in the Recruitment Fields (RFs) 01/B1, 09/H1, 13/D1, 13/D2 and 13/D3, respectively.</p> <p>The files <strong>AdjacencyMatrix_CS.csv</strong> and <strong>AdjacencyMatrix_ST.csv</strong> are the citation matrices for the Italian academics in the Computer Science disciplines (i.e. RFs 01/B1 and 09/H1) and the Statistical disciplines (i.e. RFs 13/D1, 13/D2 and 13/D3), respectively.</p> <p>The files <strong>CS_01B1_1.csv, CS_09H1_1.csv, ST_13D1_1.csv, ST_13D2_1.csv</strong> and <strong>ST_13D3_1.csv</strong> contain the data used to build the logistic regression models presented in the paper for the Italian academics at the Full Professor (FP) level.</p> <p>The files <strong>CS_01B1_2.csv, CS_09H1_2.csv, ST_13D1_2.csv, ST_13D2_2.csv</strong> and <strong>ST_13D3_2.csv</strong> contain the data used to build the logistic regression models presented in the paper for the Italian academics at the Associate Professor (AP) level.</p> <p>The file <strong>Codebook.pdf</strong> is the codebook of the previous ten files.</p> <p>The file <strong>Appendix.pdf</strong> contains the final results of the stepwise logistic regressions computed for each level (i.e. Full Professor and Associate Professor) and Recruitment Field in the Computer Science and Statistics disciplines.</p> <p>The file <strong>NormalityAssessment.pdf</strong> contains the normality assessment of citation network indices. </p>
Characterizing and explaining impact of disease-associated mutations in proteins without known structures or structural homologues
<p>AlphaFold and RoseTTAFold models of domains of disease associated human proteins without structures/known homologues.</p> <p>Tables containing the model quality, model region, sequence alignment statistics, matched FunFam, associated GO terms for the FunFam, ddG of mutation, pathogenicity of mutation, if mutation is near a predicted functional site (conserved residue/ligand binding site/protein-protein interface)</p>
Explainable AI for unveiling deep learning pollen classification model - Pollen dataset
<p>Dataset consists automatic particle detector Rapid-E measurements of pollen grains from 12 classes: Acer, Alnus, Alopecurus, Carex, Cupressus, Dactylis, Juglans, Morus, Platanus, Populus, Salix and Ulmus. Data are available i json format.</p> <p>Dataset also contains preprocessed data packed into csv files of 3 modalities: spectrum, lifetime, scattering and additional features from scattering and lifetime data are also available. These are ready to be used with machine learning models. Labels 0, 1, 2, ... 11 correspond to alphabetical order of examined pollen classes Acer, Alnus, Alopecurus ... Ulmus.</p> <p> </p>
Increased Central European forest mortality explained by higher harvest rates driven by enhanced productivity
<p># Increased Central European forest mortality explained by higher harvest rates driven by enhanced productivity</p> <p>## Author<br> Marieke Scheel, Lund University, Sweden, marieke.scheel@gmail.com</p> <p>## Description<br> Data underlying analysis in:<br> Marieke Scheel, Mats Lindeskog, Benjamin Smith, Susanne Suvanto, Thomas A. M. Pugh<br> Increased Central European forest mortality explained by higher harvest rates driven by enhanced productivity<br> Scripts underlying analysis:<br> https://github.com/mariekesche/harvest_driven_canopy_mortality</p> <p>Folder: harvest_checks<br> - NFI_Germany.txt, National Forest Index data from Germany as difference between inventories in 200-2003 and 2011-2013, values are given as fraction, n: number of NFI plots in a grid cell, HARVEST_ALL: clear-cut harvest, HARVEST_PARTIAL: thinning harvest, NATDEAD: natural dead (not harvested)</p> <p>Folder: manag_climfix (S_man,clim)<br> - cflux_forest_rel.txt, Net Primary Production (NPP) values of forests in kg [C]/ m^2 year (column 4)<br> - crownloss.txt, m^2 of crown/m^2 of ground lost per year<br> - diam_crownarea.txt, total m^2 of crown/m^2 of ground per year split into DBH classes</p> <p>Folder: manag_co2fix (S_man,CO2)<br> - cflux_forest_rel.txt, Net Primary Production (NPP) values of forests in kg [C]/ m^2 year (column 4)<br> - crownloss.txt, m^2 of crown/m^2 of ground lost per year<br> - diam_crownarea.txt, total m^2 of crown/m^2 of ground per year split into DBH classes</p> <p>Folder: manag_ndepfix (S_man,N)<br> - cflux_forest_rel.txt, Net Primary Production (NPP) values of forests in kg [C]/ m^2 year (column 4)<br> - crownloss.txt, m^2 of crown/m^2 of ground lost per year<br> - diam_crownarea.txt, total m^2 of crown/m^2 of ground per year split into DBH classes</p> <p>Folder: manag_nofix (S_man)<br> - cflux_forest_rel.txt, Net Primary Production (NPP) values of forests in kg [C]/ m^2 year (column 4)<br> - closs.txt, biomass loss in kg [C]/m^2 year split into DBH classes<br> - closs_harv.txt, biomass loss due to harvest in kg [C]/m^2 year split into DBH classes<br> - cpool_forest_rel.txt, biomass of forests in kg [C]/m^2 year<br> - crownloss.txt, m^2 of crown/m^2 of ground lost per year<br> - crownloss_age.txt, m^2 of crown/m^2 of ground lost per year due to age<br> - crownloss_dist.txt, m^2 of crown/m^2 of ground lost per year due to disturbance<br> - crownloss_fire.txt, m^2 of crown/m^2 of ground lost per year due to fire disturbance<br> - crownloss_greff.txt, m^2 of crown/m^2 of ground lost per year due to growth efficiency<br> - crownloss_harv.txt, m^2 of crown/m^2 of ground lost per year due to harvest<br> - crownloss_other.txt, m^2 of crown/m^2 of ground lost per year due to other reasons<br> - crownloss_thin.txt, m^2 of crown/m^2 of ground lost per year due to natural thinning<br> - diam_cmass_wood.txt, wooden biomass in kg [C]/m^2 year split into DBH classes<br> - diam_crownarea.txt, total m^2 of crown/m^2 of ground per year split into DBH classes<br> - diam_dens.txt, total number of trees/m^2 year split into DBH classes<br> - stemloss.txt, number of trees/m^2 year lost in respective cells<br> - stemloss_harv.txt, number of trees/m^2 year lost in respective cells due to harvest</p> <p>Folder: manag_nothin (S_nothin)<br> - cflux_forest_rel.txt, Net Primary Production (NPP) values of forests in kg [C]/ m^2 year (column 4)<br> - crownloss.txt, m^2 of crown/m^2 of ground lost per year<br> - diam_cmass_wood.txt, wooden biomass in kg [C]/m^2 year split into DBH classes<br> - diam_crownarea.txt, total m^2 of crown/m^2 of ground per year<br> - lai.txt, leaf area index (LAI) for simulated species and plant functional types (PFTs)</p> <p>Folder: PNV (S_PNV)<br> - cflux.txt, Net Primary Production (NPP) values in kg [C]/ m^2 year (column 4)<br> - crownloss.txt, m^2 of crown/m^2 of ground lost per year<br> - crownloss_age.txt, m^2 of crown/m^2 of ground lost per year due to age<br> - crownloss_dist.txt, m^2 of crown/m^2 of ground lost per year due to disturbance<br> - crownloss_fire.txt, m^2 of crown/m^2 of ground lost per year due to fire disturbance<br> - crownloss_greff.txt, m^2 of crown/m^2 of ground lost per year due to growth efficiency<br> - crownloss_other.txt, m^2 of crown/m^2 of ground lost per year due to other reasons<br> - crownloss_thin.txt, m^2 of crown/m^2 of ground lost per year due to natural thinning<br> - diam_cmass_wood.txt, wooden biomass in kg [C]/m^2 year split into DBH classes<br> - diam_crownarea.txt, total m^2 of crown/m^2 of ground per year split into DBH classes</p> <p>Folder: dependencies<br> - gridlist.txt, coordinates of 0.5°x0.5° grid cells that simulations were run on; longitude, latitude, FAO number, size in m^2<br> - landcover_eu.txt, input file LPJ-GUESS model showing changes from natural to forest (harvest); longitude, latitude, year, natural, forest, barren</p> <p>## Dependencies<br> - canopy mortality rates published in "Senf C Pflugmacher D Zhiqiang Y Sebald J Knorn J Neumann M Hostert P and Seidl R 2018 Canopy mortality has doubled in Europe’s temperate forests over the last three decades Nature Communications 9 4978 10.1038/s41467-018-07539-6"<br> - harvest removal rates published in "Ceccherini G Duveiller G Grassi G Lemoine G Avitabile V Pilli R and Cescatti A 2020<br> Abrupt increase in harvested forest area over Europe after 2015 Nature 583 72-77 10.1038/s41586-020-2438-y"<br> - FAO forest removal area data data retrieved from https://www.fao.org/faostat/en/#data/GF (24.07.2021), modified for overview (1st column: Area Code, 2nd column: Year, 3rd column: Area in 1000 ha)</p>
Classification of Artificial Intelligence and eXplainable Artificial Intelligence publications in Air Traffic Management
<p>v1.0 version used and partially published in "A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory". In this version, it references mainly Transportation Reasearch Part C, ICRAT, Journal of ATM, and ATM Seminar, IEEE transaction on ITS, but not only</p>
Assemblies, synapse clustering and network topology interact with plasticity to explain structure-function relationships of the cortical connectome
<p>Dataset linked to the article with the same title</p> <p>The model itself is very similar to its non-plastic counterpart under the following DOI: <a href="../record/7930275">10.5281/zenodo.7930275</a>, i.e. a 1.5 mm diameter cortical tissue comprising 211,712 neurons and their connectivity in the front limb and jaw subregions and the dysgranular zone of the Paxinos & Watson rat brain atlas. It's formatted in the open <a href="https://github.com/AllenInstitute/sonata">SONATA</a> standard and contains neuron locations and their properties (such as morphological types, cortical layer, etc.), their detailed morphologies, and synaptic connectivity (with all their anatomical and physiological parameters). The main difference from the non-plastic version is the addition of plasticity related parameters to <em>O1/S1nonbarrel_neurons__S1nonbarrel_neurons__chemical/edges.h5. </em>Extrinsic synaptic connections from the thalamus are included in this release, but for inputs from neurons in the remainder of non-barrel somatosensory cortex please see the non-plastic version of the circuit.</p> <p><strong>Analyzing the model</strong></p> <p>The model can be analyzed in terms of its anatomy, physiology and connectivity using the packages <a href="https://neurom.readthedocs.io/en/stable/">NeuroM</a>, <a href="https://bluebrainsnap.readthedocs.io/en/stable/">BlueBrain SNAP</a> and <a href="https://github.com/BlueBrain/ConnectomeUtilities">ConnectomeUtilities</a>. (see first Jupyter notebook)</p> <p><strong>Simulating the model</strong></p> <p>To simulate the model we'd recommend using out using our open-source simulator <a href="https://github.com/BlueBrain/neurodamus">Neurodamus</a>. The reference version is the branch <em>nbS1-2023</em>, which is archived under the following DOI: <a href="http://doi.org/10.5281/zenodo.8075202">10.5281/zenodo.8075202</a>. Instructions on how to use the simulator are provided on the GitHub page linked above. Briefly, you'll first have to <a href="https://github.com/BlueBrain/neurodamus#install-neurodamus">install Neurodamus</a>. Next, build a <em>"special"</em> executable that include compiled versions of ion channel and synapse models. To do that, follow <a href="https://github.com/BlueBrain/neurodamus#build-special-with-mod-files">these instructions</a>, where <em>mod-files-from-released-circuit </em>is replaced by the location of <em>O1/mods</em> on your system. Finally, <a href="https://github.com/BlueBrain/neurodamus#examples">run a simulation</a>. The specific simulation conditions and stimuli are specified in simulation configuration files. An exemplary simulation configuration is included in this release (<em>simulation_config.zip</em>).</p> <p><strong>Analyzing simulation results</strong></p> <p>Simulation results can be analyzed with <a href="https://bluebrainsnap.readthedocs.io/en/stable/">BlueBrain SNAP</a>, <a href="https://github.com/BlueBrain/ConnectomeUtilities">ConnectomeUtilities</a>, and <a href="https://github.com/BlueBrain/assemblyfire">assemblyfire</a>. Notebooks 2-5 go though these analysis and recreate some of the panels from our article. In most cases the notebooks can be run with the shared HDF5 files and don't require running any simulations.</p> <p><strong>Version 2</strong></p> <p>Bug fix in simulation_config.json and therefore new version of results (and corresponding notebooks). The underlying circuit model (O1.xz) did not change from v1.</p> <p>--</p> <p><em>The development of this dataset was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.</em></p>
Raw data for "Sparse periodicity-based auditory features explain human performance in a spatial multi-talker auditory scene analysis task"
<p>Raw data for the simulation study " Sparse periodicity-based auditory features explain human performance in a spatial multi-talker auditory scene analysis task" [1].</p> <p>[1] Josupeit, A., Schoenmaker, E., van de Par, S., & Hohmann, V. (2018). Sparse periodicity‐based auditory features explain human performance in a spatial multitalker auditory scene analysis task. <em>European Journal of Neuroscience</em>, https://doi.org/10.1111/ejn.13981.</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.