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1,973 results for “loop”
Synthetic and real EEG datasets for closed-loop neuroscience
<p>The dataset is made primarily for the task of real-time low latency filtering of the EEG data in the closed loop neuroscience experiments and for EEG forecasting task. The dataset consists of a real data and 5 options of the synthetic data of varying difficulty.</p><p>The real dataset consists of 25 people involved into the P4 alpha neurofeedback training. Its total size is about 16.3 hours. A more detailed instruction for this file is provided in the file Real dataset instructions.txt.</p><p>Synthetic data is generated in 5 different ways: sine wave with white noise, sine wave with pink noise, narrow-band filtered pink noise sample with pink noise, state-space model with white noise and state-space model with pink noise. Each of these datasets has about 34.5 hours of data. It is generated similarly to (Wodeyar et al, 2021). A more detailed instruction for the synthetic dataset can be found in the file Synthetic datasets instructions.txt.<br> </p><p>In LowLatencyEEGFiltering.zip one can find a code for the models used in our paper for low-latency filtering with this data.</p><p>NOTE: Code is also published in the following GitHub repository: https://github.com/ivsemenkov/LowLatencyEEGFiltering</p><p> </p><p>If you use our data or code please cite: https://www.doi.org/10.1088/1741-2552/acf7f3</p>
Data - Low-Noise Phase-Sensitive Optical Parametric Amplifier with Lossless Local Pump Generation using a Digital Dither Optical Phase-Locked Loop
<p>This dataset contains measurement data and processing code for the results published in "Low-Noise Phase-Sensitive Optical Parametric Amplifier with Lossless Local Pump Generation using a Digital Dither Optical Phase-Locked Loop". The Pyrpl code change used in the work is also attached.</p> <p>This work was funded by the Swedish Research Council (grant VR-2015-00535).</p>
Small Group and Survey Datasets: "Fostering Metacognition and Feedback Loops in a Summer Undergraduate Research Program: A Pilot Study"
<p>These datasets accompany the paper "Fostering Metacognition and Feedback Loops in a Summer Undergraduate Research Program: A Pilot Study" by Chad Curtis PhD and Kaytlin Gomez.</p> <p>The first dataset consists of free responses from n=12 students researchers during a Small Group Metacognitive Practice (SGMP) intervention conducted during the 2023 INBRE Summer Undergraduate Research Fellowship (iSURF) at Nevada State University. The files include:</p> <ul> <li><strong>transcription.docx</strong>: Transcriptions of both individual (n=12) and small group (n=4) responses from the SGMP session.</li> <li><strong>codingR1.xlsx</strong>: Codings used for the thematic analysis, as coded by the primary investigator.</li> <li><strong>codingR2.xlsx: </strong>Codings used for the thematic analysis, as coded by the co-author.</li> <li><strong>KrippendorffAlpha.xlsx</strong>: Inter-coder reliability calculations using Krippendorff's alpha.</li> </ul> <p>The survey data was collected from n=11 participants at the end of the summer research program. The files include:</p> <ul> <li><strong>surveyData.csv</strong>: Anonymized responses to survey questions regarding the SGMP intervention.</li> </ul> <p>This study was approved by the Nevada State University Review Board (Protocol #2305-0346). All procedures in the study were conducted in accordance with approved protocols. Written informed consent was obtained from the subjects for their anonymized information to be published with the article.</p>
Human-AI Collaboration: A tool to enable AI model generation with human-in-the-loop
<p>Human-AI collaboration enables domain experts to contribute their expertise with the goal of enhancing the knowledge learned by the AI models from the patterns in the data. This enables the integration of domain-specific knowledge to enrich the data for further improvement of the models through retraining. The human-AI collaboration is composed of multiple sub-components and interfaces that enables communication with external systems such as data sources, model repositories, machine configurations and decision support systems.</p> <p>Human-AI Collaboration component is developed using Python programming language. The frontend is developed using Streamlit1. The backend is developed using python and the API is implemented using FastAPI2. The choice of the programming language was made because of its wide usage and vast user base. The frameworks Streamlit and FastAPI are chosen because of the rich features for functionality and documentation as well as suitability for data analysis tasks. The applications are packaged as docker images for deployment. The application runs as a web application served by nginx for reverseproxying and users can access it via client applications such as web browsers or REST clients like Postman.</p>
Dynamics of CTCF and cohesin mediated chromatin looping revealed by live-cell imaging
<p><strong>Overview</strong></p> <p>This repository contains all the raw and processed trajectory data associated with “paper title”. In this ReadMe file we provide the following information:</p> <ul> <li>The cell lines and conditions used in this study</li> <li>A summary of how the data was collected</li> <li>The structure of the chromosome locus tracking data</li> </ul> <p><strong>Cell lines and conditions</strong></p> <p>In total, the dataset covers 12 experimental conditions representing the following cell lines and treatment conditions:</p> <ul> <li>C36</li> <li>C65</li> <li>C27</li> <li>CTCF-AID (untreated)</li> <li>CTCF-AID (2 hours AID)</li> <li>CTCF-AID (4 hours AID)</li> <li>RAD21-AID (untreated)</li> <li>RAD21-AID (2 hours AID)</li> <li>RAD21-AID (4 hours AID)</li> <li>WAPL-AID (untreated)</li> <li>WAPL-AID (4 hours AID)</li> <li>WAPL-AID (6 hours AID)</li> </ul> <p> </p> <p><strong>Data and data processing</strong></p> <p>Trajectories were obtained from 3D timeseries of mouse embryonic stem cell colonies in the conditions listed above using a LSM900 Airyscan 2 Zeiss microscope. For each movie we recorded 365 frames of 49.69 µm x 49.69 µm (584 x 584 pixels, pixel size: 0.085 µm by 0.085 µm), separated by an interval of 20 seconds for a total of just over 2 hours. 3D images were composed of 30 z-stacks separated by 0.25 µm, for a total height of 7.25 µm. Imaging was performed in two colors allowing the tracking of two arrays of fluorophores on Chromosome 18 near the <em>Fbn2</em> gene. In all conditions, the fluorophore arrays were separated by 515 kb (except the C27 clone where separation was 10 kb).</p> <p>The 3D image time series were processed using ConnectTheDots: <a href="https://github.com/ahansenlab/connect_the_dots">https://github.com/ahansenlab/connect_the_dots</a> to obtain paired trajectories of chromosome loci over time. The trajectories have been corrected for chromatic shifts and aberrations.</p> <p>Data are provided in an “unfiltered” format (meaning that individual dot localizations were not quality control filtered) , or a filtered format (the same data set, but having undergone quality control). The filtered (quality controlled) trajectory data was used for all the quantitative analyses in the article “”.</p> <p>File names are formatted follows.</p> <ul> <li>Quality controlled data have the structure: {Clone_and_condition_name}.tagged_set.tsv</li> <li>Unfiltered data have the structure: {Clone_and_condition_name}.unfiltered.tagged_set.tsv</li> </ul> <p>For example, for RAD21-AID tagged clone, for imaging performed after two hours of protein degradation, the quality-controlled file name is: RAD21_2_hr.tagged_set.tsv. Please note that for all no-treatment conditions, we used “0 hours” as the tag. Thus, the RAD21 (untreated) becomes RAD21_0_hr.tagged_set.tsv.</p> <p> </p> <p><strong>Structure of Data</strong></p> <p>The trajectory data are provided as tab-separated text files consisting of 10 columns. The column headers are:</p> <ul> <li>id: a unique dot pair index</li> <li>t: the frame in which the dots were localized</li> <li>x: x-coordinate of the dot in the EGFP channel (units in µm)</li> <li>y: y-coordinate of the dot in the EGFP channel (units in µm)</li> <li>z: z-coordinate of the dot in the EGFP channel (units in µm)</li> <li>x2: x-coordinate of the dot in the mScarlet channel (units in µm)</li> <li>y2: y-coordinate of the dot in the mScarlet channel (units in µm)</li> <li>z2: z-coordinate of the dot in the mScarlet channel (units in µm)</li> <li>dist: 3D distance between the dots across channels (units in µm)</li> <li>movie_index: an identifier used to link the dot pair back to the raw image timeseries.</li> </ul>
Single molecule videos related to "MCM complexes are barriers that restrict cohesin-mediated loop extrusion" Part 2/3
<p>Videos of cohesin translocation and collisions between translocating cohesin and MCMs under physiological salt conditions collected with MicroManager 1.4 as tif image sequences. Vidoes of DNA stained with SYTOX Orange after collection of cohesin translocation are included as separate image sequences.</p>
Single molecule videos related to "MCM complexes are barriers that restrict cohesin-mediated loop extrusion" Part 1/3
<p>Videos of collisions between translocating cohesin and MCMs under high salt conditions collected with MicroManager 1.4 as tif image sequences. Vidoes of DNA stained with SYTOX Orange after collection of cohesin translocation are included as separate image sequences.</p>
Single molecule videos related to "MCM complexes are barriers that restrict cohesin-mediated loop extrusion" Part 3/3
<p>Videos of collisions between translocating cohesin and MCM containing the YDF motif under physiological salt conditions collected with MicroManager 1.4 as tif image sequences. Videos of DNA stained with SYTOX Orange after collection of cohesin translocation are included as separate image sequences.</p>
AI-TAM: a model to investigate user acceptance and collaborative intention in human-in-the-loop AI applications
<p>More and more frequently, digital applications make use of Artificial Intelligence (AI) capabilities<br> to provide advanced features; on the other hand, human-in-the-loop approaches are on the<br> rise to involve people in AI-powered pipelines for data collection, results validation and decision making.<br> Does the introduction of AI features affect user acceptance? Does the AI result quality<br> affect people’s willingness to use such applications? Does the additional user effort required in<br> human-in-the-loop mechanisms change the application adoption and use?<br> This study aims to provide a reference approach to answer those questions. We propose a model<br> that extends the Technology Acceptance Model (TAM) with further constructs explicitly related to<br> AI – user trust in AI and perceived quality of AI output, from explainable AI (XAI) literature – and<br> collaborative intention – willingness to contribute to AI pipelines.<br> We tested the proposed model with an application for car damage claim reporting with AI-powered<br> damage estimation for insurance customers. The results showed that the XAI related factors have<br> a strong and positive effect on behavioral intention, perceived usefulness, and ease of use of the<br> application. Moreover, there is a strong link between behavioral intention and collaborative intention,<br> indicating that indeed human-in-the-loop approaches can be successfully adopted in final user<br> applications.</p> <p>Users were invited to test the interactive prototype of the BumpOut application and to report the given car accident from start to finish. These are the two interactive prototypes experienced by users:</p> <ul> <li> <p><a href="https://bit.ly/bo-prototype-flawlessAI">FlawlessAI-Group prototype</a></p> </li> <li> <p><a href="https://bit.ly/bo-prototype-failingAI">FailingAI-Group prototype</a></p> </li> </ul> <p> </p> <p>This study is shared as a research object adopting the <a href="https://www.researchobject.org/ro-crate/1.0/">RO-Crate</a> specification.</p>
Loops length data
<p>Data of hydraulic geometry (compiled from literature) and mean length of the anabranches (from satellite imagery) in river loops constituted by simple bifurcation-confluence units.</p> <p>More information can be found in the Supplementary Information file of the paper: Ragno, N., Redolfi, M., & Tubino, M. (2022). Quasi-universal length scale of river anabranches. Geophysical Research Letters.</p> <p> </p>
MAL04 Causal loop diagrams for the Charente River basin and its coastal zone (France)
<p>This dataset includes the causal loop diagrams (CLDs) developed by the H2020 COASTAL project’s MAL #4 for the Charente River basin and its coastal zone. These CLDs represent the functioning of the territory in a systemic way, highlighting its main components and interactions among them. The CLDs are the result of multiple sectoral and multi-actor workshops during which stakeholders from different sectors discussed and collaborated to establish a common vision of the land-sea system. The CLDs concern the whole territory and some specific sectors.</p>
implementation of an in-line Kerr active cavity equipped with a loop mirror
<p>This dataset includes the measurements of the resonances collected at the through port of an active fiber cavity (in in-line configuration) of 5 meters of length based on a step index silica fiber and including a loop mirror and a fiberized mirror at the two ends of the cavity. The dataset includes also the measurement of the effective losses of the in-line active cavity vs. the intracavity power. </p>
Closed-loop experiment in the retina
<p><strong>Data from closed-loop experiment in the retina</strong></p> <p>See the related code on github:</p> <p>https://github.com/ChrisGll/RBM_TRBM</p> <p>This file explains the organization of data recorded in the closed-loop experiment performed by Christophe Gardella and used in the following articles:<br> - Closed-loop estimation of retinal network sensitivity reveals signature of efficient coding, Ferrari, Gardella, Marre and Mora, eNeuro, 2017: http://www.eneuro.org/content/early/2018/01/16/ENEURO.0166-17.2017<br> - Blindfold learning of an accurate neural metric, Gardella, Marre and Mora, PNAS, 2018: http://www.pnas.org/content/early/2018/03/09/1718710115.long<br> The stimulus consists in a series of 0.9 s snippets of bar trajectory. Each snippet is called a sequence. The bar has a smooth random motion, with each sequence trajectory beginning and ending at position 0, the center of the screen.</p> <p>In parentheses are the values specific to our data.</p> <p><strong>Notations :</strong><br> Scalar variables:<br> stim_rate: (=50) binning rate for the stimulus, in Hz</p> <p>Nne: (=60) number of neurons<br> Nseq: (=17034) number of sequences<br> Nb_seq: (=45) number of time bins per sequence</p> <p>Nreftj: (=2) number of reference trajectories<br> Npertdir: (=16) number of perturbation directions per reference trajectory</p> <p><br> Indices:<br> ne_i : index of neuron<br> b_i : index of time bin seq_i : index of sequence (from 1 to Nseq) reftj_i : index of reference trajectory (1 or Nreftj)<br> pertdir_i : index of perturbation direction (1 to Npertdir)</p> <p>In general, in the code:<br> ..._i stands for index:<br> ..._l stands for list<br> ..._il stands for list of indices</p> <p><strong>Stimulus:</strong><br> trajs: cell of size (Nseq,1) with<br> trajs{seq_i}: vector of size (1, Nb_seq) with trajs{seq_i}(b_i) the bar position in time bin b_i, in µm.</p> <p>rand_seq_il: list of indices of sequences corresponding to random bar trajectories</p> <p>Example: rand_seq_il(1) is the index of the first sequence corresponding to a random trajectory. trajs{rand_seq_il(1)} is the corresponding random trajectory.</p> <p>ref_seq_il: cell of size (1, Nreftj) with ref_seq_il{reftj_i} the list of indices of sequences corresponding to repetitions of reference trajectory reftj_i.</p> <p>pert_seq_il: cell of size (Npertdir, Nreftj) with<br> pert_seq_il{pertdir_i, reftj_i}: list of indices of sequences corresponding to a perturbation of reference trajectory reftj_i in direction pertdir_i. Indices are sorted by increasing perturbation amplitude.</p> <p>Example: All sequence trajectories are either random, a trajectory, or a perturbation. So the intersection between rand_seq_il, any ref_seq_il{reftj_i} or any pert_seq_il{pertdir_i, reftj_i} is always be empty.</p> <p>Example: The union between rand_seq_il, all ref_seq_il{reftj_i} and all pert_seq_il{pertdir_i, reftj_i} is the complete list of indices 1:Nseq.</p> <p>pert_amp_l: cell of size (Npertdir, Nreftj) with<br> pert_amp_l{pertdir_i, reftj_i}: list of amplitudes of corresponding perturbations. pert_amp_l{pertdir_i, reftj_i}(n) is the amplitude of the perturbation in sequence pert_seq_il{pertdir_i, reftj_i}(n).</p> <p><strong>Responses:</strong><br> spkb_rate : (=50) binning rate for the responses, in Hz sparse_spkb_all: cell of size (Nseq,1) with<br> sparse_spkb_all{seq_i}: sparse matrix of size (Nne, Nb_seq): binned response during sequence seq_i. sparse_spkb_all{seq_i}(ne_i,b_i)=1 if neuron ne_i spiked at least once in time bin b_i.</p> <p>sparse_spkb_all is the representation of responses used for computing the linear discriminability and distances with the RBM and TRBM metrics.</p> <p>In order to compute distances, one usually only considers a subset of the Nb_seq time bins of the sequence. We note:<br> b_il_s: (=23:37) list of indices of time bins used for distance computation.</p> <p>If one needs un-binned responses (for some metrics such as the van Rossum metric), spike times can be found in the variable spkt_all:</p> <p>spkt_all: cell of size (Nseq,1) with<br> spkt_all{seq_i}: cell of size (Nne, 1) with<br> spkt_all{seq_i}{ne_i}: list of spike times (in UNIT) of neuron ne_i in sequence seq_i. The list is empty if there is no spike. These times are in s, and are relative, with t=0 the beginning of the sequence.</p> <p>pert_lindiscrim_l: cell of size (Npertdir, Nreftj) with<br> pert_lindiscrim_l{pertdir_i, reftj_i} the linear discriminability of responses to the perturbation of reference trajectory reftj_i in direction pertdir_i. pert_lindiscrim_l{pertdir_i, reftj_i}(n) is the linear discriminability of responses in sequence pert_seq_il{pertdir_i, reftj_i}(n).</p> <p> </p>
High-resolution water budget estimates over the Po basin: progress towards digital replicas (OL): open loop without irrigation
<p>NASA LIS output with water budget variables at 0.7 km^2 resolution over the Po river basin (Italy) for 2015-2023. Netcdf files for 8 + 2 experiments, described in De Lannoy et al. (2024, JAMES). Because of storage limitations, this upload only contains the baseline open loop without irrigation (OL), i.e. 1 of the 8 experiments with ERA5. The other 7 experiments are on a separate zenodo link (see below).</p> <p><strong>8 experiments forced with ERA5 meteorology</strong></p> <p>po_ol_hymap_noirr: (OL) open loop simulation, no irrigation modeling <br>po_ol_hymap_irr: (OL*) open loop simulation, with irrigation modeling --> 10.5281/zenodo.13754454</p> <p>po_da_hymap_gamma_noirr: (DAg) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating, no irrigation modeling --> 10.5281/zenodo.13754454<br>po_da_hymap_gamma_irr: (DAg*) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating, with irrigation modeling --> 10.5281/zenodo.13754454</p> <p>po_da_hymap_snd_noirr: (DAs) data assimilation of Sentinel-1 snow depth retrievals, no irrigation modeling --> 10.5281/zenodo.13754454<br>po_da_hymap_snd_irr: (DAs*) data assimilation of Sentinel-1 snow depth retrievals, with irrigation modeling --> 10.5281/zenodo.13754454</p> <p>po_da_hymap_gamma_snd_noirr: (DAgs) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating and assimilation of snow depth retrievals, no irrigation modeling --> 10.5281/zenodo.13754454<br>po_da_hymap_gamma_snd_irr: (DAgs*) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating and assimilation of snow depth retrievals, with irrigation modeling --> 10.5281/zenodo.13754454</p> <p><strong>2 experiments forced with MERRA2 meteorology </strong></p> <p><strong>--> These are not provided on Zenodo, because we hit the maximum storage limit. Feel free to reach out to the authors and ask for these data.<br></strong></p> <p>po_ol_hymap_noirr_M2: open loop simulation, no irrigation modeling<br>po_ol_hymap_irr_M2: open loop simulation, with irrigation modeling</p>
Closed-loop auditory stimulation targeting alpha and theta oscillations during REM sleep induces phase-dependent power and frequency changes
<p>This repository contains raw data, sleep scoring, and data to create the figures for the paper:</p> <p><strong>"Closed-loop auditory stimulation targeting alpha and theta oscillations during REM sleep induces phase-dependent power and frequency changes"</strong></p> <p>by Valeria Jaramillo, Henry Hebron, Sara Wong, Giuseppe Atzori, Ullrich Bartsch, Derk-Jan Dijk*, Ines R. Violante* (* contributed equally).</p> <p>Journal article has been published in SLEEP and can be found here: <a href="https://doi.org/10.1093/sleep/zsae193">https://doi.org/10.1093/sleep/zsae193</a></p> <p>Code can be found here: <a href="https://gitlab.surrey.ac.uk/nemo/RSN">https://gitlab.surrey.ac.uk/nemo/RSN</a></p> <p>Please cite as indicated under 'Citation' on this page.</p> <p>More information on the datafiles can be found in the README.</p>
Polymer simulations guide the detection and quantification of chromatin loop extrusion by imaging
<p><strong>Dataset description</strong></p> <p>This dataset contains coordinates of the two anchors of a 150 kb simulated loop (static imaging) and anchor-anchor distances of a 150 kb loop in simulations where the extruder was allowed to unbind from the polymer (dynamic imaging).</p> <p><strong>Dataset description - Static imaging</strong></p> <p>This dataset contains coordinates of the two anchors of a 150 kb simulated loop.</p> <p>The subfolder 'Free' contains coordinates from a polymer not submitted to loop extrusion (1,000 independent simulations). The subfolder 'Loop' contains loop anchor coordinates in a polymer submitted to loop extrusion (4,000 independent simulations). In simulations with extrusion, the polymer chain was simulated such as it went through 3 different states : i) Open state (absence of loops) ii) Extruding state where the loop size increases with time (anchor-anchor distance decreases) and iii) Closed state corresponding to a stable loop with the two anchors in contact.</p> <p> </p> <p><strong>Structure of data - Static imaging</strong></p> <p>In each .txt file, the first three columns correspond to the XYZ coordinates of the anchor (in µm), the fourth column indicates the state label (0=Open, 1=Extruding, 2=Closed). Each row corresponds to a simulation timepoint (2991 timepoints in polymers submitted to loop extrusion).</p> <p>The simulation ID is indicated at the end of each .txt file.</p> <p>The two anchors of the loop are bead #275 and bead #324.</p> <p> </p> <p><strong>Structure of data - Dynamic imaging</strong></p> <p>Each .txt file contains the anchor-anchor distance (in µm) as function of time for approximately 10,000 independent simulations. Each column is an independent simulation. Each row is a simulation timepoint (0.3 s / simulation unit). The different .txt files correspond to different localization errors, indicated in µm in XY and Z.</p> <p>'list_deb_closed_10000.p' is a dictionary whose keys are the simulation ID (corresponding to the columns of the .txt files), and the values are the frame at which extrusion begins.</p> <p>'list_end_closed_10000.p' is a dictionary whose keys are the simulation ID (corresponding to the columns of the .txt files), and the values are the frame at which extrusion ends.</p>
The energy landscape for R-loop formation by the CRISPR-Cas Cascade complex - Minimal Dataset
<p>Minimal Dataset for "The energy landscape for R-loop formation by the CRISPR-Cas Cascade complex", published at <a href="https://www.nature.com/nsmb/">NSMB</a>.</p>
Synth-Forc Loop Data.
<p>This is an sqlite-lite binary file containing room temperature magnetic hysteresis loops and minor loops for a set of first order reversal curves (FORC)s. The data is generated using MERRILL (https://www.rockmag.org). It can be viewed using the command line sqlite (see https://www.sqlite.org). The file contains a single table, called 'all_loops' with the following fields:</p> <p>id - a unique integer id for each row in the</p> <p>geometry - a unique name of a geometry in this case 'oblate' and 'prolate' indicating an oblate truncated tetrahedron and a prolate truncated tetrahedron.</p> <p>temperature - the temperature at which micromagnetic models were run in order to generate the data.</p> <p>aspect_ratio - the aspect ratio of the geometries.</p> <p>size - the size of each geometry - in nanometre, using a convention of equivalend spherical volume diameter.</p> <p>Br - the reversal field of a minor loop.</p> <p>B - the applied applied field.</p> <p>M - the magnetisation that results from applying B (starting from Br).</p> <p>SatMag - the saturation magnetisation value of magnetite at the given temperature.</p> <p>In order for the file to be consistent, it is recommended that each field step B is uniform across all geometries, sizes and aspect ratios.</p>
EXTREMA: Random autonomous interplanetary mission scenarios for EXTREMA Simulation Hub (ESH) hardware-in-the-loop simulations
<p>EXTREMA (short for Engineering Extremely Rare Events in Astrodynamics for Deep-Space Missions in Autonomy) enables self-driving spacecraft, challenging the current paradigm under which spacecraft are piloted in the interplanetary space [1]. Deep-space guidance, navigation, and control applied in a complex scenario is the subject of EXTREMA, which wants to engineer ballistic capture in a totally autonomous fashion. EXTREMA is erected on three pillars. Pillar 1 is on autonomous navigation. Pillar 2 involves autonomous guidance and control. Pillar 3 deals with autonomous ballistic capture. The outcomes from each one of the three pillars is validated with a tailored experiment featuring the model of the associated CubeSat subsystems. Integrated experiments involving all the components of a CubeSat GNC system are carried on in the EXTREMA Simulation Hub, an integrated facility simulating an interplanetary transfer with a hardware-in-the-loop setup. The project has been awarded a European Research Council (ERC) Consolidator Grant in 2019.</p> <p>The data set is made of 1000 (even more in some releases) random interplanetary mission scenarios used for hardware-in-the-loop simulations performed in the EXTREMA simulation Hub (ESH). Each scenario comes with the following information: generation seed, initial epoch, initial state (Keplerian elements and Cartesian coordinates), initial attitude (quaternion and direct cosine matrix), initial mass, number of revolution, time of flight, target final epoch, target final state (Keplerian elements and Cartesian Coordinates), and other supplementary data. Scenarios are organized in directories each containing at least the following files:</p> <ul> <li>'esh_ic.json': random scenario information saved in .json format;</li> <li>'esh_ic.mat': random scenario information saved in .mat format.</li> </ul> <p>Some releases provide only a limited set of data in the 'esh_ic.json' file. For those, the full set of information is found in the 'esh_ic_full.json' file.</p> <p>For additional information about the EXTREMA project visit the page <a href="http://extrema.polimi.it/">extrema.polimi.it</a>.</p> <p><strong>References</strong><br> [1] Di Domenico G., et al. "The ERC-Funded EXTREMA Project: Achieving Self-Driving Interplanetary CubeSats." <em>Modeling and Optimization in Space Engineering: New Concepts and Approaches</em>. Cham: Springer International Publishing, 2022. 167-199. DOI: <a href="https://doi.org/10.1007/978-3-031-24812-2_6">10.1007/978-3-031-24812-2_6</a>.</p>
Itasca Biological Station Wilderness Loop Breeding Bird Survey, MN (1979 - 2025)
This dataset is part of an ongoing effort that began in 1979 at the University of Minnesota Itasca Biological Station to conduct a springtime annual survey of breeding birds around the Wilderness Loop drive in Itasca State Park. Following traditional USGS Breeding Bird Survey methods, Field Ornithology students conduct 3-minute point counts along an established route with 18 stops approximately 0.5 miles apart in distance. Students then recorded the species and number of birds seen and heard within a 0.25 radius.
ScienceDex guides
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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.