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4,489 results for “transitivity”
Figure 6 in Sea-land transition drove terrestrial amphipod diversification in East Asia, with a description of a new species
Figure 6. Morinoia aosen sp. nov. male holotype, from Beijing. A, antennae I–II. B, uropods. C, gnathopod I. D, gnathopod II. E, uropod I. F, uropod II.
Figure 5 in Sea-land transition drove terrestrial amphipod diversification in East Asia, with a description of a new species
Figure 5. Morinoia aosen sp. nov. male holotype, from Beijing. A, upper lip. B, left mandible. C, right mandible. D–E, maxilla I. F, maxilla II. G, lower lip. H, maxilliped.
Data set for Ligand additivity relationships enable efficient exploration of transition metal chemical space
<p>Dataset of transition metal complexes curated in pickle files and comma delimited format, scripts for CSD curation, and computed DFT properties for associated manuscript.</p>
Data and code for "Combining ultrahigh index with exceptional nonlinearity in resonant transition metal dichalcogenide nanodisks."
<div># Data and code for "Combining ultrahigh index with exceptional nonlinearity in resonant transition metal dichalcogenide nanodisks."</div> <div> </div> <div>## Contents</div> <div> </div> <div>* `chi2-dft`: DFT calculations of the chi(2) nonlinear susceptibility tensor</div> <div>* `shg-exp`: explicit spectra of the SHG from a flake and resonant disks</div> <div> </div> <div>## Description of the data</div> <div> </div> <div>The data are organized as follows:</div> <div> </div> <div>* `chi2-dft/plot_chi2.{py,m}`: plotting scripts for python and matlab</div> <div>* `chi2-dft/shg-3R-MoS2-chi2.*`: binary dataset for plotting chi2 and plot thereof</div> <div>* `chi2-dft/nlo`: folder with scripts for data reproduction and structure file</div> <div>* `chi2-dft/nlo/shg-lg`: folder with chi2 tensor elements, numpy format; used for plotting with python</div> <div> </div> <div>* `shg-exp/disk-specs`: data and matlab script to plot explicit SHG spectra of the disks</div> <div>`shg-exp/disk-specs/D*_***nm_1s_03mW.asc/` - D* - disk number 1,2,3; ***nm - pump wavelength 810-910-1020nm; 1s - signal collection time in seconds;03mW - pump power in mW</div> <div>* `shg-exp/flake-specs`: data and matlab script to plot explicit SHG spectra and log-log power dependence of the thin flake</div> <div>`shg-exp/flake-specs/***nm_*.*mw_0.5s_H8_3R_MoS2.asc/` - ***nm - pump wavelength 800:10:1040nm; *.*mW - pump power in mW; 0.5s - signal collection time in seconds;</div> <div> </div> <div>## Reproduction of the data</div> <div> </div> <div>The DFT calculations were performed using GPAW (https://wiki.fysik.dtu.dk/gpaw/index.html) and the nonlinear optical response approach (https://wiki.fysik.dtu.dk/gpaw/tutorialsexercises/opticalresponse/nlopt/nlopt.html). The scripts are in a form suitable for cluster calculations and use global WALLTIME/STARTTIME parameters. Calculation of the SHG spectrum is set up such that one call of the script in a cluster environment will run one tensor element calculation and then exit. The calculations of the data consist of the following steps:</div> <div> </div> <div>1. Ground-state (gs) calculation:</div> <div> * `chi2-dft/nlo/gs.py` -- plane wave calculations for the `rlx-3R-MoS2-bulk.xyz` structure file using the plane wave mode, 500 eV cutoff, PBE xc-functional. Other parameters are in the `setting.py` file.</div> <div> * Generates the `gs.gpw` file.</div> <div>2. Calculation of unoccupied bands:</div> <div> * Requires finished ground-state calculation.</div> <div> * `chi2-dft/nlo/unocc.py`</div> <div> * This step is optional is the required number of bands calculated in the `gs` step is sufficient.</div> <div> * Generates the `unocc.gpw` file.</div> <div>3. Calculate the matrix elements:</div> <div> * Requires finished ground-state calculation (`gs.gpw`)</div> <div> or</div> <div> * unoccupied bands calculation (`unocc.gpw`)</div> <div> * `chi2-dft/nlo/mommat.py`</div> <div> * Generates the `mml.npz` file.</div> <div>4. Calculation of the SHG spectrum:</div> <div> * Requires the calculated matrix elements (`mml.npz` file).</div> <div> * `chi2-dft/nlo/shg.py` -- computes one chi2 tensor element from the `mml.npz` file. Designed to run in a HPC environment. Will look for the first nonexistent `abc` tensor element in folder and calculate it. Rerun the script to calculate additional ones or rewrite the script to calculate all required elements in one loop.</div> <div> </div>
Supplemental information of "Transitioning from food waste disposal to treatment and reuse can achieve meaningful reductions in food's climate and resource burdens"
<p>(1) Source data (Data 1) for bootstrapping analysis to compute means and 95% CIs of carbon footprint and product metrics of food waste treatments, as well as food waste landfill emissions</p> <p>(2) Source data (Data 2) for food waste management schemes of US, EU, and China for emission estimation as well as emission mitigation analyses</p> <p>(3) STATA output report</p> <p>(4) List of studies selected from a systematic literature review</p>
Harmonic scale factors of fundamental transitions for dispersion-corrected quantum chemical methods
<p>These two datasets contain quantum-chemical calculations, experimental data, and scripts for obtaining scale factors for harmonic vibrational frequencies for fundamental transitions. </p> <p>The first dataset (<span><a href="../api/records/11174948/draft/files/scaling_quantum_chemistry.tar.xz/content" target="_blank" rel="noopener noreferrer">scaling_quantum_chemistry.tar.xz</a></span>) contains quantum-chemical calculations for the training dataset of various molecules and three exemplary molecules: pyrene, protonated LeuEnk, and doubly protonated gramicidin S. The second dataset (<span><a href="../api/records/11174948/draft/files/scaling_fitting.zip/content" target="_blank" rel="noopener noreferrer">scaling_fitting.zip</a></span>) contains the data and scripts for fitting the scale factors. The training set comprises 441 molecules of various sizes (from diatomics to C60). </p>
Supplementary information for: A new tiny fossil penguin from the Late Oligocene of New Zealand and the morphofunctional transition of the penguin wing
<p>The Late Oligocene is a period of high penguin diversity, following major changes in the marine environment at the Eocene/Oligocene boundary and prior to the emergence of crown penguins in the Miocene. Historically, a large morphological gap existed between the most crownward <em>Platydyptes </em>among the Oligocene penguins from New Zealand and the Early Miocene stem penguins such as <em>Palaeospheniscus</em> and <em>Eretiscus </em>from South America. Here we describe a new species that contributes to filling this gap. <em>Pakudyptes</em> <em>hakataramea</em> gen. et sp. nov. is the earliest tiny penguin, overlapping in size with the smallest extant species, the little penguin <em>Eudyptula minor</em>. Its distinctive combination of a well-developed proximal end of the humerus and a rather archaic elbow joint provides clues to the evolution of penguin wings. Phylogenetic analysis indicates that penguin wings evolved rapidly from the Late Oligocene to the Early Miocene, together with the acquisition of morphofunctional and hydrodynamical characteristics that enable the excellent swimming ability of modern penguins. As an indicator of aquatic adaptation, bone microanatomy also shows a comparable structure to that of <em>Eudyptula</em>. The appearance of the smallest body size and the evolution of modern wings may have led to the ecological diversity of modern penguins, which confirms the importance of Zealandia in penguin evolution.</p>
Promoting conservation agriculture through push-pull technology as an agroecological transition.
<p>Abstract <br>Low soil fertility, weeds, pests, and climatic change severely threaten crop productivity and agricultural <br>sustainability, especially in SSA. Despite decades of research finding adequate technical solutions for most <br>situations of food systems, the problem of low food productivity has persisted. In an effort to counter this, <br>intensive agricultural systems have been mooted including the high application of agrochemicals to control <br>weeds, and pests and increase production. However, these initiatives have not lasted beyond the project cycle <br>and they have instigated land degradation through unsustainable practices. As a solution, conservation <br>agricultural practices have been promoted among small-scale farmers. These practices focus on minimizing soil <br>disturbance, crop diversification, and cover cropping. Push-pull technology is an aspect of conservation <br>agriculture where intercropping a cereal crop with a repellent plant, such as desmodium and planting an <br>attractive trap plant, such as brachiaria or Napier grass as a border crop around this intercrop. This paper aimed <br>at reviewing existing literature to establish the linkage between conservation agriculture components, push-pull <br>technology, and a sustainable agroecological transition. A list of questions directed the discussion where push<br>pull technology has been proven to be an aspect that promotes conservation agriculture. It has been able to <br>increase crop yields, reduced tillage, established a cover crop on the farm, and further, PPT has a regenerative <br>aspect through the integration of livestock husbandry providing organic manure that and together with the <br>nitrogen fixation ability of the grass, improve soil fertility, conserved soil moisture, and reduce erosion. This <br>reduces the use of inorganic input, and machinery making farming economical for small-holder farmers. </p>
Dataset related to epilepsy phase 1 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).
<p>Dataset related to epilepsy phase 1 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).</p>
Dataset related to epilepsy phase 2 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).
<p>Dataset related to epilepsy phase 2 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).</p>
Water slowing down drives the occurrence of the low temperature dynamical transition in microgels
<p>Data presented in the article entitled <strong>Water slowing down drives the occurrence of the low temperature dynamical transition in microgels</strong>.</p>
Computational Screening of Transition Metal Complexes as Guests in the Ga4L6-12 Nanocage
<p>Optimized geometries, scripts, experimental data curated from the literature from complexes, and CSD filtering data for "Computational Screening of Putative Transition Metal Complexes as Guests in the Ga4L6-12 Nanocage." </p>
Supplementary materials for the manuscript "Run-away transition to turbulent strong-field dynamo"
<p>Supplementary materials for the manuscript "Run-away transition to turbulent strong-field dynamo" including simulation data and Jupyter notebooks to read them</p>
The phosphorus legacy offers opportunities for agro-ecological transition (France 1850–2075)
Open the record for dataset details and reuse information.
Data for "Hofstadter spectrum of Chern bands in twisted transition metal dichalcogenides"
<p>Data used to generate each Figure is in files denoted fig....hdf5. <br>These files contain results for multiple parameter values. The group "data" can be read into a pandas dataframe, providing an index and scalar data.<br>Vector valued data is stored in individual datasets, indexed by the index in "data" along 0-th axis.<br>For an example readout, see "example.py".</p> <p>See README.txt for notes on individual files.</p>
Assessing Potential Groundwater Storage Capacity for Sustainable Groundwater Management in the Transitioning Post-Subsidence Metropolitan Area
<p>Citation: <br>Lin, S.‐H., Hu, J.‐C., & Wang, S.‐J. (2024). Assessing potential groundwater storage capacity for sustainable groundwater management in the transitioning post‐subsidence metropolitan area. Water Resources Research, 60, e2023WR036951. https://doi.org/10.1029/2023WR036951</p>
Dataset related to ADHD phase 3 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).
<p>Dataset related to ADHD phase 3 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).</p>
Dataset related to diabetes phase 3 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).
<p>Dataset related to diabetes phase 3 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).</p>
Dataset related to epilepsy phase 3 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).
<p>Dataset related to epilepsy phase 3 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).</p>
Data for "Phase transition in Random Circuit Sampling"
<p><strong>Purpose</strong></p> <p>This dataset defines the Random Quantum Circuits (RQCs) used in our paper "Phase transition in Random Circuit Sampling" and lists the bitstrings observed in the experimental executions of the circuits on the Sycamore processor. See [1] for more details about the experiment. This data upload is modeled after that of [5].</p> <p><strong>Background</strong></p> <p><strong>Circuit parameters</strong></p> <p>RQCs posted here are uniquely identified<br>using the following parameters:</p> <ul> <li> `n`: number of qubits (69, 70),</li> <li> `m`: number of cycles (04, 06, 08, ..., 28, 30),</li> <li> `s`: seed for the pseudo-random number generator (000, 001, ..., 595),</li> <li> `patches`: the number of patches,</li> <li> `p`: sequence of coupler activation patterns (`ABCD`, `ABCDCDAB`),</li> <li> `num_sq`: the number of distinct single-qubit gates (3, 8),</li> <li> the date on which the data was collected, included in yymmdd format in the filename, and</li> <li> `phase_match`: included in the file name if phase matching was performed.</li> </ul> <p>See Figure S25 in [2] and Figure 3 of [1] for the coupler activation patterns and Figure 4 of [1] for illustrations of the patches. Also see code snippets below for visualizing the patches and activation patterns from the provided circuits.<br>When `num_sq` is 8, the single-qubit gates are chosen randomly from \(Z^p X^{1/2} Z^{-p}\), with \(p \in \{-1, -3/4, -1/2, -1/4, 0, 1/4, 1/2, 3/4 \}\), whereas when `num_sq` is 3, they are chosen randomly from among \(\sqrt X\), \(\sqrt Y\), and \(\sqrt W\). Phase matching is described in Appendix C.1 of [1]. Note that circuits which share the same seed `s` share the same initial gate sequence.</p> <p><strong>Content description</strong></p> <p>For each RQC there are four files in the dataset:<br> * original RQC specification in QSIM format, named `circuit_\*.qsim`,<br> * derived RQC specificaton as python code using cirq, named `circuit_\*.py`,<br> * derived RQC specification in QASM format, named `circuit_\*.qasm`,<br> * bitstrings observed in experiments, named `measurements_\*.txt`.</p> <p>The asterisk \* in the names above stands for a string specifying the parameters identifying a RQC. For example,<br>`circuit_n70_m24_s00_patches3_pABCD_num_sq8_221014_phase_match.qasm` contains the definition of the 70-qubit, 24-cycle RQC with PRNG seed 0, 3 patches, a simplifiable sequence of coupler activation patterns (i.e. ABCD), and 8 distinct single-qubit gates, taken on October 14, 2022, with phase matching, in the QASM format.</p> <p>Files are grouped by parameters `n`, `m`, and `patches` and into compressed tarballs. For example, tarball `n69_m04_patches2.tar.gz` contains all RQCs and measurement files for circuits with 69 qubits, 4 cycles, and 2 patches.</p> <p><strong>Circuit file formats</strong></p> <p><strong>QSIM format</strong></p> <p>First line specifies the number of qubits n. Each subsequent line specifies a single gate and consists of moment number, gate name and one or two qubits as a number in 0..n-1 optionally followed by gate parameters.</p> <p>The circuits use the following gates:<br> * `x_1_2`: parameter-free, single-qubit pi/2 rotation around the X axis of the Bloch sphere, see equation (45) in section VII of [2],<br> * `y_1_2`: parameter-free, single-qubit pi/2 rotation around the Y axis of the Bloch sphere, see equation (46) in section VII of [2],<br> * `hz_1_2`: parameter-free, single-qubit pi/2 rotation around the X+Y axis of the Bloch sphere, see equation (47) in section VII of [2],<br> * `rz`: single-qubit rotation around the Z axis of the Bloch sphere through the angle specified in radians by the gate's sole parameter,<br> * `fsim`: two-qubit gate corresponding to the composition of the iSWAP and CPHASE gates and taking two parameters in radians: theta (the negative iSWAP angle) and phi (the CPHASE angle), see equation (48) in section VII of [2].</p> <p>Note that the two-qubit gates executed in our experiments on Sycamore belong to the five-parameter family of two-qubit gates that preserve the number of 0 and 1 states of the qubits. Each such gate can be decomposed into one fsim gate and four rz gates. Therefore, one cycle consisting of one application of<br>single-qubit gates and one application of two-qubit gates is represented in the file using four moments. The first moment contains `x_1_2`, `y_1_2` and `hz_1_2` gates. The other three moments use `rz` and `fsim` gates to describe the two-qubit gates used in the experiments. See section VII in [2] for more details about the Sycamore gates and their decomposition.</p> <p>Qubits are specified as numbers in 0..n-1 and hence do not directly indicate qubit location on the device.</p> <p><strong>Python/cirq format</strong></p> <p>Each python file defines two variables: QUBIT_ORDER and CIRCUIT. The former is a python list object containing `cirq.GridQubit` objects initialized with the<br>row and column of each qubit on the device. The latter is a `cirq.Circuit` object initialized with all gate operations contained in the circuit. The files have been tested using cirq version 1.2.0.dev20230613162638.</p> <p>The following code snippet illustrates how one can use cirq and our circuit definitions to compute output state amplitudes:</p> <pre><code>$ python -i circuit_n70_m24_s00_patches3_pABCD_num_sq8_221014_phase_match.py >>> cirq.final_wavefunction(CIRCUIT, qubit_order=QUBIT_ORDER) array([ 0.00263724+0.00337646j, 0.0009332 +0.00111853j, -0.0007809 +0.00386362j, ..., 0.00574739-0.00027827j, -0.00254766+0.00299345j, 0.00396056+0.00312335j], dtype=complex64)</code></pre> <p> </p> <p>See [3] for more details about cirq.</p> <p><strong>QASM format</strong></p> <p>Each QASM file has been generated using cirq and specifies the RQC decomposed into CNOT and single-qubit gates. The files have been generated using cirq.</p> <p>See [4] for more details about the format.</p> <p><strong>Measurements file format</strong></p> <p>Each line contains the bitstring obtained in a single execution of the RQC on Sycamore. The first, left-most position corresponds to the qubit 0 in QSIM format and the first qubit in the `QUBIT_ORDER` list in the python/cirq files.</p> <p><strong>To visualize the patches</strong></p> <p>After loading `CIRCUIT` from the appropriate `.py` file, the following code snippet can be used to visualize the patches:</p> <pre><code>import cirq import matplotlib.pyplot as plt pairs = set() for moment in CIRCUIT: for op in moment.operations: q = op.qubits if len(q) > 1: assert len(q) == 2 pairs.add(tuple(sorted(q))) d = {p:1 for p in pairs} heatmap = cirq.TwoQubitInteractionHeatmap(d) _, ax = plt.subplots(figsize=(8, 8)) _ = heatmap.plot(ax)</code></pre> <p> </p> <p><strong>Visualize the activation patterns</strong></p> <p>The activation pattern sequence can also be visualized in a similar manner. After loading `CIRCUIT` from the appropriate `.py` pyle, the following code snippet can be used to visualize the activation patterns:</p> <pre><code>import cirq import matplotlib.pyplot as plt def has_two_qubit_gates(moment): has = False for op in moment.operations: if len(op.qubits) > 1: has = True break return has def plot_pairs(moment): pairs = set() for op in moment.operations: q = op.qubits if len(q) > 1: assert len(q) == 2 pairs.add( tuple(sorted(q)) ) d = {p:1 for p in pairs} heatmap = cirq.TwoQubitInteractionHeatmap(d) _, ax = plt.subplots(figsize=(8, 8)) _ = heatmap.plot(ax) return ax moments = [_ for _ in CIRCUIT if has_two_qubit_gates(_)] moment_to_visualize = moments[0] # iterate through this manually plot_pairs(moment_to_visualize)</code></pre> <p> </p> <p><strong>Content listing</strong></p> <p>The dataset includes the following tarball files:</p> <pre><code>n69_m04_patches2.tar.gz (80 files) n69_m04_patches3.tar.gz (80 files) n69_m06_patches2.tar.gz (80 files) n69_m06_patches3.tar.gz (80 files) n69_m08_patches2.tar.gz (80 files) n69_m08_patches3.tar.gz (80 files) n69_m10_patches2.tar.gz (80 files) n69_m10_patches3.tar.gz (80 files) n69_m12_patches2.tar.gz (80 files) n69_m12_patches3.tar.gz (80 files) n69_m14_patches2.tar.gz (80 files) n69_m14_patches3.tar.gz (80 files) n69_m16_patches2.tar.gz (80 files) n69_m16_patches3.tar.gz (80 files) n69_m18_patches2.tar.gz (80 files) n69_m18_patches3.tar.gz (80 files) n69_m20_patches2.tar.gz (80 files) n69_m20_patches3.tar.gz (80 files) n69_m22_patches2.tar.gz (80 files) n69_m22_patches3.tar.gz (80 files) n69_m24_patches1.tar.gz (4 files) n69_m24_patches2.tar.gz (80 files) n69_m24_patches3.tar.gz (80 files) n69_m26_patches2.tar.gz (80 files) n69_m26_patches3.tar.gz (80 files) n69_m28_patches2.tar.gz (80 files) n69_m28_patches3.tar.gz (80 files) n69_m30_patches2.tar.gz (80 files) n69_m30_patches3.tar.gz (80 files) n70_m16_patches2.tar.gz (960 files) n70_m16_patches3.tar.gz (1040 files) n70_m16_patches9.tar.gz (160 files) n70_m18_patches2.tar.gz (960 files) n70_m18_patches3.tar.gz (1040 files) n70_m18_patches9.tar.gz (160 files) n70_m20_patches2.tar.gz (960 files) n70_m20_patches3.tar.gz (1040 files) n70_m20_patches9.tar.gz (160 files) n70_m22_patches2.tar.gz (960 files) n70_m22_patches3.tar.gz (1040 files) n70_m22_patches9.tar.gz (160 files) n70_m24_patches1.tar.gz (56 files) n70_m24_patches2.tar.gz (960 files) n70_m24_patches3.tar.gz (1040 files) n70_m24_patches9.tar.gz (160 files) n70_m26_patches1.tar.gz (4 files) n70_m26_patches2.tar.gz (880 files) n70_m26_patches3.tar.gz (960 files) n70_m26_patches9.tar.gz (160 files)<br>67-qubit data (described below)</code></pre> <p> </p> <p>Additionally, the file <code>Figures 2 and 3 data.zip</code> contains processed data (9 files in Pickle format) that are shown in Figures 2 and 3 of the paper.</p> <p>The 67-qubit data files, <code>n67_full.zip</code>, <code>n67_patches2.zip</code>, and <code>n67_patches3.zip</code> are formatted differently. The circuits are savd in .json format and can be loaded with `cirq.read_json()`. The data files are in .npy format and can be opened with numpy.load(). The number of cycles (`m` elsewhere) is indicated after `d` in the filename. The contents of these zip files can be previewed with the "preview" button.</p> <p> </p> <p> </p> <p><strong>References</strong></p> <p>[1] Google AI Quantum and collaborators, "Phase transition in Random Circuit Sampling". <a href="https://arxiv.org/abs/2304.11119">arXiv:2304.11119</a></p> <p>[2] Google AI Quantum and collaborators, Supplementary information for “Quantum supremacy using a programmable superconducting processor”. <a href="https://arxiv.org/abs/1910.11333">arXiv:1910.11333</a></p> <p>[3] Cirq: A Python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits, <a href="https://github.com/quantumlib/Cirq">https://github.com/quantumlib/Cirq</a>.</p> <p>[4] Cross, Andrew W.; Bishop, Lev S.; Smolin, John A.; Gambetta, Jay M. "Open Quantum Assembly Language", <a href="https://arxiv.org/abs/1707.03429">arXiv:1707.03429</a></p> <p>[5] Martinis, John M. et al. (2022), "Quantum supremacy using a programmable superconducting processor", Dryad, Dataset, <a href="https://doi.org/10.5061/dryad.k6t1rj8">https://doi.org/10.5061/dryad.k6t1rj8</a></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.