Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
162
datasets available to search
ShareScore release 0.9.0
Dataset results
162 results for “invariability”
Dataset for "Invariance of Object Detection in Untrained Deep Neural Networks"
<p><strong>Dataset for<br> "Invariance of Object Detection in Untrained Deep Neural Networks"</strong><br> Jeonghwan Cheon, Seungdae Baek, and Se-Bum Paik*<br> *Contact: sbpaik@kaist.ac.kr<br> <br> To run demo codes for "<a href="https://github.com/vsnnlab/Invariance">Invariance of Object Detection in Untrained Deep Neural Networks</a>", please download files below.<br> <br> <strong>1. Image.zip</strong><br> <strong>- Object dataset (Foldername: selectivity_var)</strong>: This set was used to find units that selectively respond to a specific object class. It contains nine object classes (bed, chair, desk, dresser, nightstand, monitor, sofa, table, toilet) and 200 images are prepared to an object class. Each image has different object identities, which means it rendered from different object 3D models (Princeton ModelNet, a 3D CAD model dataset for computer vision and cognitive science [https://modelnet.cs.princeton.edu/]). To render image of object dataset, horizontal viewpoint variation angle was randomly set between -30° and +30°. In object dataset, brightness and contrast of images are statistically comparable across the object class.<br> <strong>- Viewpoint dataset for invariance test (Folder name: invariance_test)</strong>: This set was used to test the viewpoint invariant characteristic of object selective units. This dataset consists of 13 subsets which has different viewpoints from -180° to +180° in linear scale step. It contains 200 different object identities in an object class, which are the same as those used in the object dataset.<br> <strong>- Viewpoint dataset for finding invariant unit (Folder name: invariance_unit)</strong>: This set was used to find object selective units that specifically or invariantly responded to object images of different viewpoints. This dataset consists of five angle-based viewpoint classes (-60°, -30°, 0°, 30°, 60°) with 50 object identities which were not used to find object selective unit<br> <strong>- SVM dataset (Folder name: SVM_var)</strong>: This set was used to train and test SVM which performs object detection task. It contains 60 different object identities in an object class, which were not used to find object selective unit. Specifically, it consists of 18 subsets which has different viewpoint variation range from 0° to 180°. For example, subset with 180° viewpoint variation range contains images which shows different viewpoints of objects within range of -90° and +90°.</p>
Dataset for "Band structures and Z2 invariant of 2D transition metal dichalcogenides from fully relativistic Dirac--Kohn--Sham theory using Gaussian-type orbitals"
<p>Two-dimensional (2D) materials exhibit a wide range of remarkable phenomena, many of which owe their existence to the relativistic spin--orbit coupling (SOC). To understand and predict properties of materials containing heavy elements, such as the transition metal dichalcogenides (TMDs), full account of relativistic effects is mandatory in first-principles calculations. We present an all-electron method based on the four-component Dirac Hamiltonian and Gaussian-type orbitals (GTOs) that overcomes complications associated with linear dependencies and ill-conditioned matrices arising when diffuse functions are included in the basis. Until now, there has been no systematic study of the convergence of GTO basis sets for periodic solids neither at the nonrelativistic nor the relativistic level. Here, we provide such a study of relativistic band structures of the 2D TMDs in the hexagonal (2H), tetragonal (1T), and distorted tetragonal (1T') structural phases while focusing on SOC-driven properties (the Rashba splitting and the $\mathbb{Z}_2$ topological invariant). We demonstrate that our approach is valid even if large basis sets with multiple basis functions corresponding to each valence atomic orbital (denoted triple- and quadruple-$\zeta$) are used in the relativistic regime. The method does not require the use of pseudopotentials and provides access to all electronic states within the same framework, paving the way for direct studies of material properties that depend heavily on the electron density near atomic nuclei where relativistic effects and SOC are strongest, such as the parameters of the spin Hamiltonian.</p>
Math format invariance data & code
<p>Here we provide preprocessed fMRI data and analysis scripts used in "Quantitative modeling demonstrates format-invariant representations of mathematical problems in the brain”</p> <p>The preprocessed data are provided for each participant (sub-01 to sub-08).</p> <p>The analysis scripts require MATLAB (R2019b) and FreeSurfer v7.1.1.</p> <p> </p> <p>Ridge regression</p> <pre><code>ID = 'sub-01'; Method = 'Operator'; Code = 1; %1: all operators %2: only single operators %3: only double operators Ridge(ID, Method, Code) Ridge_CrossModal(ID, Method, Code)</code></pre> <p>Format invariance and specificity</p> <pre><code>ModInv(ID, Method, Code) ModInv_MinusRead(ID, Method, Code) ModSpec(ID, Method, Code)</code></pre> <p>Representational similarity analysis </p> <pre><code>RSM_Group_WholeCortex(Method, Code) RSM_Group_WholeCortex_MinusRead(Method, Code)</code></pre> <p>Principal component analysis</p> <pre><code>PCA_Group_WholeCortex(Method, Code)</code></pre> <p> </p> <p>If you have any questions, please send an email to nakai.tomoya [at] neuro.mimoza.jp. </p>
Invariant echo localization using bat pinnae
<p>This dataset works with the scripts provided in the GitHub repository in the link:<br> https://github.com/zaco87/Paper-Invariance-Localization</p> <p>matlab_variables_data.mat - should be loaded to Matlab. It has the entire data that is needed for running the Matlab programs provided with the paper's repository on GitHub</p> <p> </p>
Simulation Data for Electron Scattering due to Asymmetric Drift-Orbit Bifurcation: Geometric Jumps of Adiabatic Invariant
<p>Simulation dataset for the paper “Electron Scattering due to Asymmetric Drift-Orbit Bifurcation: Geometric Jumps of Adiabatic Invariant.” The archive “artemis_solar_dataset.zip” contains the solar wind magnetic field data used to plot Figure 1. The three “…_jump.txt” files are tracing data used to plot Figures 6a, 6b, and 6c, while “test_particle.txt” is the tracing data for Figure 5. The file “t04_L_shells__Pd_4.00_B_5.00_theta_225.txt” provides magnetic field B(s, MLT) profiles at different L-shells (used to plot Figure 7); each line corresponds to a specific MLT sector at one specific L-shell.</p>
A scalable realization of local U(1) gauge invariance in cold atomic mixtures
Open the record for dataset details and reuse information.
Physically asymmetric division of the C. elegans zygote ensures invariably successful embryogenesis
Open the record for dataset details and reuse information.
Experimental demonstration of allometric invariance of plant response to density over the course of wheat domestication
Open the record for dataset details and reuse information.
Data from: Task-specific invariant representation in auditory cortex
Open the record for dataset details and reuse information.
Data from: An identification of invariants in life history traits of amphibians and reptiles
<p>While many morphological, physiological, and ecological characteristics of organisms scale with body size, some do not change under size transformation. They are called invariant. A recent study recommended five criteria for identifying invariant traits. These are based on that a trait exhibits a unimodal central tendency and varies over a limited range with body mass (type I), or that it does not vary systematically with body mass (type II). We methodologically improved these criteria and then applied them to life history traits of amphibians, Anura, Caudata (eleven traits), and reptiles (eight traits). The numbers of invariant traits identified by criteria differed across amphibian orders and between amphibians and reptiles. Reproductive output (maximum number of reproductive events per year), incubation time, length of larval period, and metamorphosis size were type I and II invariant across amphibians. In both amphibian orders, reproductive output and metamorphosis size were type I and II invariant. In Anura, incubation time and length of larval period and in Caudata, incubation time were further type II invariant. In reptiles, however, only number of clutches per year was invariant (type II). All these differences could reflect that in reptiles body size and in amphibians, Anura, and Caudata metamorphosis (neotenic species go not through it) and the trend towards independence of egg and larval development from water additionally constrained life history evolution. We further demonstrate that all invariance criteria worked for amphibian and reptilian life history traits, although we corroborated some known and identified new limitations to their application.</p>
Data from: Invariant antagonistic network structure despite high spatial and temporal turnover of interactions
Recent work has suggested that emergent ecological network structure exhibits very little spatial or temporal variance despite changes in community composition. However, the changes in network interactions associated with turnover in community composition have seldom been assessed. Here we examine whether changes in ecological networks are best detected by standard emergent network metrics or by assessing internal network changes (i.e. interaction and composition turnover). To eliminate possible spatial or phylogenetic effects, that in large-scale studies may obscure mechanisms structuring networks and interactions, we sampled multiple antagonistic (plant-herbivore) networks for a single diverse plant family (the Restionaceae) in the hyperdiverse Cape Floristic Region. These are the first plant-herbivore networks constructed for this global biodiversity hotspot. We found invariant emergent network structure despite considerable changes in insect and plant composition across communities over time and space. In contrast, there was high interaction turnover between networks. Seasonally, this was driven by turnover in insect species and insect host switching. Spatially, this was driven by simultaneous turnover in plant and insect species, suggesting that many insects are host specific or that both groups exhibit parallel responses to environmental gradients. Spatial interaction turnover was also driven by turnover in plants, showing that many insects can utilise multiple (possibly closely related) hosts and this may create divergent selection gradients that promote insect speciation. Thus we show highly variable interaction fidelity, despite invariant emergent network structure. We suggest that evaluating internal network changes may be more effective at elucidating the processes structuring networks, and many fine-scale changes may be obscured when only calculating emergent network metrics.
Optical modeling of nonspherical pine and mugwort pollen aerosols using the invariant imbedding T-matrix and improved geometric optics methods
<p><strong>Data for </strong></p> <p><strong><em>Optical modeling of nonspherical pine and mugwort pollen aerosols using the invariant imbedding T-matrix and improved geometric optics methods</em>.</strong></p> <p> </p> <p>Mail:</p> <p><a href="mailto:bilei@zju.edu.cn">bilei@zju.edu.cn</a></p>
Artefact for paper "LTL under reductions with weaker conditions than stutter-invariance"
<p><strong>LTLSensitivityToLength : LTL artefacts on length-sensitivity</strong></p> <p>Procedures and artefacts to reproduce experiments on "length" sensitivity of LTL formulas.</p> <p>We use both Tapaal <a href="https://www.tapaal.net/">https://www.tapaal.net/</a> and ITS-Tools <a href="https://lip6.github.io/ITSTools-web/">https://lip6.github.io/ITSTools-web/</a> in these experiments on models taken from the model-checking competition 2021 <a href="https://mcc.lip6.fr/">https://mcc.lip6.fr/</a>.</p> <p>See also companion GitHub project here : https://github.com/yanntm/LTLSensitivityToLength/</p> <p><strong>Workflow and steps</strong></p> <p>Please refer to the source of the "~/demo.sh" file for more details on the different steps.</p> <ul> <li>Step 0 : download the VM provided by the conference from here : <a href="https://zenodo.org/record/5562597">https://zenodo.org/record/5562597</a> Click the ".ova" to download the VM then open it and start it with VirtualBox.</li> <li>Step 1 : login to the VM with user/pass : tacas22/tacas22. You can change keyboard setting by doing : right click background, Display Setting, left in "Region & Language", "Input sources", first "Add" French/your keyboard type (it is in "other") then trash "US-en" using the garbage can icon. Right-click background->"Open in terminal" will now give us a terminal with correct keyboard.</li> <li>Step 2 : Download and deploy artefact in the VM. Use the Zenodo link at the bottom of this page to download and place it at the root of the tacas22 home: in /home/tacas22/. Then deploy with tar xvzf home.tgz.</li> <li>Step 3 : There are several Readme with more details but the rest is done using the demo.sh so simply run it. It will enact the further steps.</li> </ul> <p><strong>Steps in the demo</strong></p> <p>Much more detail is available in the ~/demo.sh file.</p> <ul> <li>Step 0 : install debian dependencies. We use R language and modern Java.</li> <li>Step 1 : Analyze sensitivity to length of LTL formulas and compute reduced models. This step is handled by ITS-tools. Because the benchmark is so huge we only reproduce using three model instances (out of 2822). More examples can be run by editing "step 1" of demo.sh to add more runs.</li> <li>Step 2 : run an MCC model-checker with both reduced and original model/formula pairs. Collect logs and compute a CSV summary file from these raw logs. We run both Tapaal and ITS-Tools. We did our best to simulate the conditions we ran in (we capture output in OAR.XX.out and OAR.XX.err files), but we use a cluster and OAR to reserve cpu on it in the full experiment so the match is not perfect. The actual scripts we used on the cluster are also part of this distribution however.</li> <li>Step 3 : build tables from the CSV and formulas of literature on sensitivity to length in practice. This corresponds to the Table in section 4.1 of the paper. For a large set of formulas we compute whether it is stutter insensitive, shortening insensitive, lengthening insensitive, or arbitrary. This step can actually be performed after or before Step 2 it only depnds on data produced at step 1.</li> <li>Step 4 : build tables and plots from the CSV of Step 2 using R. The demo script builds both the plots using the data you have just collected at step 1 and using the full data from our experiment. To make these auditable, we provide the CSV resulting from our cluster run in ~/tacas22/Rscripts/clusterLog as well as all the logs of this run in the ~/tacas22/logsCluster.tgz that were parsed to build the CSV.</li> </ul> <p><strong>Contents</strong></p> <p>We have deployed all dependencies.</p> <ul> <li>~/usr/ contains local installations of the LTL manipulation library Spot and of libraried required for R (used in analysis)</li> <li>~/tapaal/ contains Tapaal, configured for MCC mode. It was built from the bzr depot of Tapaal from source, following instructions helpfully provided by the authors Jiri Srba et al.</li> <li>~/tacas22/Spot-Binary-Builds/ is the folder used to build Spot with appropriate flags. We used "build_cluster.sh" script. You do not need to do this however it is deployed in ~/usr/local. The companion github is <a href="https://github.com/yanntm/Spot-Binary-Builds">https://github.com/yanntm/Spot-Binary-Builds</a>.</li> <li>~/tacas22/packages/ contains the apt-get debian packages needed to run our demo. Essentially we need R language and Java language support.</li> </ul> <p>There are instructions on how to rebuild these dependencies from scratch in each subfolder. Again it is not necessary to do so with the provided archive.</p> <p>Then the tools/models :</p> <ul> <li>~/tacas22/ITS-Tools-MCC contains the ITS-Tools distribution as well as the inputs from the MCC'21 edition. Both of these are extracted and installed following the instruction on the three github : <a href="https://github.com/yanntm/pnmcc-models-2021">https://github.com/yanntm/pnmcc-models-2021</a> (curated/annotated models from MCC 21) <a href="https://github.com/yanntm/pnmcc-tests">https://github.com/yanntm/pnmcc-tests</a> (For the test/runner framework) <a href="https://github.com/yanntm/ITS-Tools-MCC">https://github.com/yanntm/ITS-Tools-MCC</a> (For ITS-tools distributed for the MCC)</li> <li>~/tacas22/LTLPatterns/ contains formulas collected from the literature as well as a script to analyze their sensitiivty and compute metrics on them. See the README in that folder for more details.</li> <li>~/tacas22/Rscripts/ contains scripts to analyze the results and produce plots used in the paper. See the README in that folder for more details. It also contains the CSV produced from our cluster run (in clusterLog/) to reproduce the plots (these were built using the full logs provided in ~/tacas22/logsCluster.tgz and analyzed with the perl scripts from the ~/tacas22/ITS-Tools-MCC folder)</li> </ul> <p>The requirements for this setup include:</p> <ul> <li>A version of Spot : <a href="https://spot.lrde.epita.fr/">https://spot.lrde.epita.fr/</a> to both translate LTL to an automaton and analyze its sensitivity.</li> <li>The models and formulas from the <a href="https://mcc.lip6.fr/">Model Checking Contest 2021</a>. We grab these from our <a href="https://github.com/yanntm/pnmcc-models-2021">PNMCC Models 2021</a> repository that itself builds the files using the official distribution of the MCC.</li> <li>Our test/runner framework for these examples, available from <a href="https://github.com/yanntm/pnmcc-tests">https://github.com/yanntm/pnmcc-tests</a></li> </ul> <p>Then we need a MCC compatible tool that can compete in the LTL category of the contest. We used~:</p> <ul> <li>A version of <a href="http://ddd.lip6.fr/">ITS-tools</a> : we use the version packaged for the MCC competition, available from here : <a href="https://github.com/yanntm/ITS-commandline">ITS-tools for MCC</a></li> <li>A version of <a href="https://www.tapaal.net/">Tapaal</a> : we build it from the source repositories with flags to enable MCC mode. See repository here : <a href="https://bazaar.launchpad.net/~verifypn-maintainers/verifypn/new-trunk/files/head:/Scripts/MCC21/competition-scripts">https://bazaar.launchpad.net/~verifypn-maintainers/verifypn/new-trunk/files/head:/Scripts/MCC21/competition-scripts</a> and <a href="https://code.launchpad.net/verifypn">https://code.launchpad.net/verifypn</a></li> </ul> <p><strong>License</strong></p> <p>This work is provided under the terms of GPL v3 or more recent.</p> <p>(C) Yann Thierry-Mieg, Denis Poitrenaud, Etienne Renault, Emmanuel Paviot-Adet. Sorbonne Université, CNRS. 2021.</p>
Inference and Test Generation Using Program Invariants in Chemical Reaction Networks Artifacts
<p>The artifacts for Inference and Test Generation Using Program Invariants in Chemical Reaction Networks, published at ICSE 2022.</p> <p>The pdf of the paper can be accessed at <a href="https://ieeexplore.ieee.org/document/9794130">IEEEXplore</a>.</p> <p><strong>To cite this work, please use the citation below:</strong></p> <pre>@INPROCEEDINGS{GertenICSE22, author={Gerten, Michael C. and Marsh, Alexis L. and Lathrop, James I. and Cohen, Myra B. and Miner, Andrew S. and Klinge, Titus H.}, booktitle={2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)}, title={Inference and Test Generation Using Program Invariants in Chemical Reaction Networks}, month={May}, year={2022}, pages={1193-1205}, doi={10.1145/3510003.3510176}}</pre> <p>The artifacts are also available on <a href="https://github.com/LavaOps/ICSE-2022-Artifacts">GitHub</a>.</p> <p><strong>This is an updated version of the ChemFlow tool. The update addressed an overflow error when computing gaussian elimination that could result in incorrect invariants with certain model inputs. After verification, all models in this work were not affected by this bug and have the same set of invariants generated by both versions. We have updated the docker file to use the new code as well.</strong></p>
FAT or FiTT: Are anvil clouds or the tropopause temperature-invariant? (manuscript data)
<p>Cloud-resolving model output used in the manuscript "FAT or FiTT: Are anvil clouds or the tropopause temperature-invariant?" by Seeley et al. </p>
Artifact for "Are My Invariants Valid? A Learning Approach"
<p>This is the preliminary artifact for our paper on learning to judge the validity of invariants (specifically, method pre- and post-conditions), including data, code and output logs. Please see the README for details on the contents and how to run the code.</p>
Invariance of EI
Open the record for dataset details and reuse information.
Topologies, Checkpoints, and Configurations for the paper "GVI-RL: Graph-Invariant RL for Attack Paths Discovery using Vulnerabilities Embedded with Large Language Models"
<p>This repository consists of the <strong>files</strong> related to the <strong>paper</strong> "GVI-RL: Graph-Invariant RL for Attack Paths Discovery using Vulnerabilities Embedded with Large Language Models". In particular, this repository contains tensorboard logs, topologies, checkpoints, seeds, and results to ensure reproducibility of the results of the paper.</p> <p>The results included are related to the training/validation and hyper-parameters optimization of the outcome multi-label classifier, the GVI-RL agent, and the world model.<br>The data folder contains also the topologies used in the study, the vulnerabilities data used to generate them and the dataset for multi-label classification.</p> <p>The README.md file describes the folders' structure.</p>
Scale invariance in early embryonic development
<p>Data and analysis accompanying the paper:</p> <p>"Scale invariance in early embryonic development" </p> <p>Milos Nikolic, Victoria Antonetti, Feng Liu, Gentian Muhaxheri, Mariela D. Petkova, Martin Scheeler, Eric M. Smith, William Bialek and Thomas Gregor. </p> <p>arxiv.org/abs/2312.17684</p>
Multi-channel auto-encoders for learning domain invariant representations enabling superior classification of histopathology images
<p>A partially synthetic histopathology dataset containing image patches of colon tissue from 3 staining and scanning conditions.</p> <p>This dataset can be used to develop novel histopathology image analysis algorithms that are better able to generalise to novel data domains.</p> <p>See repo for more information.</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.