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Tandem repeat catalog of the human genome generated from long-read assemblies
<p>Allele sequences of polymorphic loci (VCF) and README for all version 2 (2.0 + 2.1) files</p>
Generative artificial intelligence predicts human performance
<p>Research data for a study that used generative artificial intelligence (i.e., ChatGPT with the GPT-4 and the Google Gemini 2.0 Flash models) to predict human performance in a language-based memory task. In particular, we studied the effects of context on the relatedness and memorability of garden-path sentences. </p>
Supplementary materials for "Improving diffusion-based protein backbone generation with global-geometry-aware latent encoding"
<h1>Info</h1> <p>This dataset contains the supplementary materials for "Improving diffusion-based protein backbone generation with global-geometry-aware latent encoding". </p> <p>For <strong>source code </strong>and <strong>detailed instructions on usage, </strong>please refer to our <a href="https://github.com/meneshail/TopoDiff/tree/main" target="_blank" rel="noopener">github</a> .</p> <h1>Supplementary data</h1> <h2>weights.tar.gz</h2> <p>The trained model weights used in the paper.</p> <h2>dataset.zip</h2> <p>CATH-60 Dataset used in the paper. In the notebook directory of our <a href="https://github.com/meneshail/TopoDiff/tree/main" target="_blank" rel="noopener">github</a> , we provide an example on encoding and visualize it with our trained encoder.</p> <h2>design.zip</h2> <p>The 21 novel mainly-beta designs selected for experiment validation. Along with the generated backbone, we also provide the prediction results from AlphaFold and ESMFold.</p> <h2>benchmark_sample.zip</h2> <p>Sampled backbones used for all benchmark experiment (All methods and variants included).</p> <h2>evaluation.tar.gz</h2> <p>Precomputed CATH reference data for coverage metric computation. Need to be downloaded for using evaluation scripts. </p>
Can Developers Prompt? A Controlled Experiment for Code Documentation Generation [Replication Package]
<h2>Artifact Summary</h2> <p>This repository contains the replication package for the paper 'Can Developers Prompt? A Controlled Experiment for Code Documentation Generation,' presented at the <em><a href="https://conf.researchr.org/home/icsme-2024" target="_blank" rel="noopener">40th IEEE International Conference on Software Maintenance and Evolution (ICSME'24)</a></em>.</p> <p>The purpose of the package is to facilitate the verification and reproduction of the study results. It provides all data of the controlled experiment, the developed <em>Visual Studio Code (VS Code)</em> extension, as well as the slides of the conference presentations.</p> <h2>Paper Abstract</h2> <p>Large language models (LLMs) bear great potential for automating tedious development tasks such as creating and maintaining code documentation. However, it is unclear to what extent developers can effectively prompt LLMs to create concise and useful documentation. We report on a controlled experiment with 20 professionals and 30 computer science students tasked with code documentation generation for two Python functions. The experimental group freely entered ad-hoc prompts in a ChatGPT-like extension of Visual Studio Code, while the control group executed a predefined few-shot prompt. Our results reveal that professionals and students were unaware of or unable to apply prompt engineering techniques. Especially students perceived the documentation produced from ad-hoc prompts as significantly less readable, less concise, and less helpful than documentation from prepared prompts. Some professionals produced higher quality documentation by just including the keyword Docstring in their ad-hoc prompts. While students desired more support in formulating prompts, professionals appreciated the flexibility of ad-hoc prompting. Participants in both groups rarely assessed the output as perfect. Instead, they understood the tools as support to iteratively refine the documentation. Further research is needed to understand which prompting skills and preferences developers have and which support they need for certain tasks.</p> <h2>References</h2> <p>The published paper is available on <a href="https://doi.org/10.1109/ICSME58944.2024.00058" target="_blank" rel="noopener">IEEE Xplore</a> and the preprint on <a href="https://doi.org/10.48550/arXiv.2408.00686" target="_blank" rel="noopener">arXiv</a>.</p>
A collection of AI generated images visualising various RDM aspects
<p>This publication contains images visualising various RDM aspects. These images were generated by the <a href="https://www.forschungsdaten.uni-bonn.de/en" target="_blank" rel="noopener">Research Data Service Center</a> team at the University of Bonn and are used in the workshop "Research Data Management: A Crash Course" conducted since 2021 by the Research Data Service Center. The slide deck is available as a related publication (see the related works section below for details).</p> <p>The images were generated with the help of <a href="https://help.openai.com/en/articles/8932459-creating-images-in-chatgpt">ChatGPT</a>. </p> <p>In this version, due to legal reasons, we changed the images.</p>
FragGT: Fragment-based Evolutionary Molecule Generation using Gene Types
<p>This directory contains data requires to run frag-gt (Meyers and Brown, 2023), a fragment-based evolutionary algorithm for generating optimal molecules released as part of the guacamol_baselines GitHub repository – https://github.com/BenevolentAI/guacamol_baselines.</p><p>Scripts for generating the data are available from github. The compressed data directory contains (A) Processed and filtered SMILES derived from ChEMBL v.33 produced by `download_chembl_smiles` and (B) fragment stores generated by `generate_fragstore` and `filter_fragstore` for both the above file in (A) and the original GuacaMol dataset.</p><p>smiles_files and fragstores were derived from molecule data downloaded from ChEMBL (https://www.ebi.ac.uk/chembl).</p><p>Liability: We do not represent and/or warrant that no third party rights exist which might prevent the use of the database or that no third party rights would be infringed by said use.</p><p>(data updated for frag-gt version 0.0.2)</p>
High-power intracavity single-cycle THz pulse generation using thin lithium niobate
<p>This dataset is accompanying the paper "High-power intracavity single-cycle THz pulse generation using thin lithium niobate"<br><br><strong>Autocorrelation.txt:</strong> second harmonic generation noncollinear autocorrelation trace data. (measurement device: Femtochrome FR-103XL)</p><p><strong>Spectrum.txt:</strong> optical spectrum (measurement device: APE wavescan)</p><p><strong>RF_1Mspan.txt:</strong> radio frequency spectrum with 1 MHz span (measurement device: ROHDE & SCHWARZ FPC1000)</p><p><strong>RF_1Gspan.txt:</strong> radio frequency spectrum with 1 GHz span (measurement device: ROHDE & SCHWARZ FPC1000)</p><p><strong>EOS_THz_raw.h5: </strong>electro-optic sampling raw data of the THz measurement in HDF-5 format (measurement device: ROHDE & SCHWARZ RTM3004)</p><p><strong>EOS_noise_raw.h5: </strong>electro-optic sampling raw data of the noise measurement in HDF-5 format (measurement device: ROHDE & SCHWARZ RTM3004)</p><p><strong>THz_time.csv:</strong> processed electro-optic sampling data of the THz measurement in time</p><p><strong>THz_freq.csv:</strong> processed electro-optic sampling data of the THz measurement in frequency</p><p><strong>Dark_time.csv:</strong> processed electro-optic sampling data of the noise measurement in time</p><p><strong>Dark_freq.csv:</strong> processed electro-optic sampling data of the noise measurement in frequency</p>
Next-generation 3D object detection and tracking for self-driving vehicles using object velocity
<p>The synthetic dataset was generated using KITTI-like specifications and annotations format. It is comprised by the training and testing sets, that include KITTI standard folders: label_2, image_2 and calib. Furthermore, there is a velodyne file for each of the following use cases:</p><ul><li>Point cloud 1: (x,y,z, (Float)Radial_Velocity): this point cloud has the relative radial velocity as an additional feature for each point. File: velodyne_radial_velocity;</li><li>Point cloud 2: (x,y,z,(Float)Absolute_Speed): in this point cloud, every point has the absolute speed of the object as the additional feature. File: velodyne_abs_speed;</li><li>Point cloud 3: (x,y,z,(Bool)Is_Moving): the additional feature of this point cloud is a Boolean value that is set to 1.0 if the object is moving; contrariwise, it is set to 0.0 for static objects. File: velodyne_is_moving;</li><li>Point cloud 4: (x,y,z,0): no additional feature information. If desired, requires post-processing to convert to (x,y,z) or changing the toolbox point cloud configuration to not consider the additional feature. File: velodyne_xyz;</li></ul><p>Additionally, the detections generated with the OpenPCDet toolbox and Second-IoU model are provided.</p><p>This work was made as part of a master thesis of Informatics Engineering in the University of Aveiro.</p>
Synthbuster: Towards Detection of Diffusion Model Generated Images
<p>Dataset described in the paper "Synthbuster: Towards Detection of Diffusion Model Generated Images" (Quentin Bammey, 2023, <i>Open Journal of Signal Processing</i>)</p><p>This dataset contains synthetic, AI-generated images from 9 different models:</p><ul><li>DALL·E 2</li><li>DALL·E 3</li><li>Adobe Firefly</li><li>Midjourney v5</li><li>Stable Diffusion 1.3</li><li>Stable Diffusion 1.4</li><li>Stable Diffusion 2</li><li>Stable Diffusion XL</li><li>Glide</li></ul><p> </p><p>1000 images were generated per model. The images are loosely based on raise-1k images (Dang-Nguyen, Duc-Tien, et al. "Raise: A raw images dataset for digital image forensics." Proceedings of the 6th ACM multimedia systems conference. 2015.). For each image of the raise-1k dataset, a description was generated using the Midjourney /describe function and CLIP interrogator (https://github.com/pharmapsychotic/clip-interrogator/). Each of these prompts was manually edited to produce results as photorealistic as possible and remove living persons and artists names.</p><p> </p><p>In addition to this, parameters were randomly selected within reasonable values for methods requiring so.</p><p>The prompts and parameters used for each method can be found in the `prompts.csv` file.</p><p> </p><p>This dataset can be used to evaluate AI-generated image detection methods. We recommend matching the generated images with the real Raise-1k images, to evaluate whether the methods can distinguish the two of them. Raise-1k images are not included in the dataset, they can be downloaded separately at (http://loki.disi.unitn.it/RAISE/download.html).</p><p> </p><p>None of the images suffered degradations such as JPEG compression or resampling, which leaves room to add your own degradations to test robustness to various transformation in a controlled manner.</p><p> </p>
Data for: Multi-generational fitness effects of natural immigration indicate strong heterosis and epistatic breakdown in a wild bird population
<p><span>The fitness of immigrants and their descendants produced within recipient populations fundamentally underpins the genetic </span><span>and population dynamic</span><span> consequences of immigration. </span><span>I</span><span>mmigrants can </span><span>in principle </span><span>induce contrasting genetic effects on fitness across generations, reflecting multi-faceted additive, dominance, and epistatic effects. Y</span><span>et, full multi-generational and sex-specific fitness effects of regular immigration have not been quantified within naturally structured systems, precluding inference on underlying genetic architectures </span><span>and population outcomes</span><span>. We used four decades of song sparrow </span><span>(<em>Melospiza melodia</em>)</span> <span>life-history and pedigree data to quantify fitness of natural immigrants, natives, and their F1, F2, and backcross descendants, and test for evidence of non-additive genetic effects. Values of key fitness components (including adult lifetime reproductive success and zygote survival) of F1 offspring of immigrant-native matings substantially exceeded their parent mean, indicating strong heterosis. Meanwhile, F2 offspring of F1-F1 matings had notably low values, indicating surprisingly strong epistatic breakdown. Further, magnitudes of effects varied among fitness components, and</span> <span>differed between female</span><span>s</span><span> and male</span><span>s</span><span> descendants. These results demonstrate that strong non-additive genetic effects on fitness can arise within </span><span>weakly </span><span>structured </span><span>and fragmented </span><span>populations </span><span>experiencing </span><span>frequent </span><span>natural </span><span>immigration. </span><span>Such effects will substantially affect the net </span><span>degree of effective gene flow and resulting local genetic introgression and adaptation.</span></p>
Optimal planning of autonomous electric vehicles charging stations with photovoltaic generations and energy storage systems
<p>This database contains technical information on the 69-bus electrical distribution system. This system was tested in a mixed integer linear programming model for allocating autonomous electric vehicle charging stations equipped with photovoltaic generation and energy storage systems. Additionally, this document contains data related to charging stations, energy storage systems, and operational scenarios applied to the case studies.</p>
Fig. 2. Bayesian consensus tree generated from partial 28S in Relationships Of The Heteronchocleidids (Heteronchocleidus, Eutrianchoratus And Trianchoratus) As Inferred From Ribosomal Dna Nucleotide Sequence Data
Fig. 2. Bayesian consensus tree generated from partial 28S rDNA sequences (D1 domain) with Diplectanum spp. and Gyrodactylus spp. as outgroups. Values shown at each node refer to Bayesian (BI) posterior probabilities/maximum likelihood (ML) percentages of the bootstrap values with 100 replicates. Bootstrap values lower than 50 are given as dashes (-).
An urban traffic dataset composed of visible images and their semantic segmentation generated by the CARLA simulator
<p><strong>If you use this dataset please cite this paper: Rosende, S.B.; Gavilán, D.S.J.; Fernández-Andrés, J.; Sánchez-Soriano, J. An Urban Traffic Dataset Composed of Visible Images and Their Semantic Segmentation Generated by the CARLA Simulator. <em>Data</em> 2024, <em>9</em>, 4. <a href="https://doi.org/10.3390/data9010004">https://doi.org/10.3390/data9010004</a></strong></p> <p>A dataset of aerial urban traffic images and their semantic segmentation is presented to be used to train computer vision algorithms, among which those based on convolutional neural networks stand out. The images have been generated using the CARLA simulator (but would be like those that could be obtained with fixed aerial cameras or by using AUVs) in the field of intelligent transportation management. The presented dataset is available and accessible to improve the performance of vision and road traffic management systems, especially for the detection of incorrect or dangerous maneuvers.</p>
The supplemental data for the paper: "Methodology of generation of CFD meshes and 4D shape reconstruction of coronary arteries from patient-specific dynamic CT"
<p>The supplemental data for the paper: "Methodology of generation of CFD meshes and 4D shape reconstruction of coronary arteries from patient-specific dynamic CT"</p><p>A video file (minimum play resolution is HD to see the mesh) showing the movement of the LCA throughout the heart cycle and .STL files for 10--100% (increment of 10\%) of the heart cycle phase.</p>
Data for SI-Hg D2 validation report for the calibration of elemental mercury gas generators including information on repeatability, reproducibility and uncertainty evaluation at emission and ambient levels extended to the sub ng/m3 level
<p>In deliverable 2 of the SI-Hg project the first validation results of the SI-Hg calibration protocol are reported. Within the SI-Hg project a protocol for the metrological calibration of elemental mercury gas generators used in the field was developed. For the validation the output of two different mercury gas generators was calibrated according to the protocol. As metrological reference standard the primary mercury gas standard from the Van Swinden Laboratory (VSL) was used. The measurements described in the protocol could be performed during the validation and the data was processed using a script to determine the output of the candidate generator and the uncertainty of the mercury concentration. Based on the validation measurements and data processing several improvements for the calibration protocol were identified and were used to improve the calibration protocol. </p><p>In this repository data obtained during the validation is published. The files of the following comparisons between reference generator and candidate generator can be found in this repository:</p><ul><li>VSL vs VSL<ul><li>m1<ul><li>09022022 calibration mercury gas generator VSL vs VSL m1</li><li>VSL_vs_VSL_m1</li></ul></li><li>m2 <ul><li>05072022 calibration mercury gas generator VSL vs VSL m2</li><li>VSL_vs_VSL_m2</li></ul></li><li>m3<ul><li>07072022 calibration mercury gas generator VSL vs VSL m3</li><li>VSL_vs_VSL_m3</li></ul></li></ul></li><li>VSL vs PSA before modification<ul><li>m1<ul><li>15032022 calibration mercury gas generator VSL vs PSA fixed m1</li><li>single_point_VSL_vs_PSA_fixed_m1_4</li><li>single_point_VSL_vs_PSA_fixed_m1_6</li><li>single_point_VSL_vs_PSA_fixed_m1_8</li><li>single_point_VSL_vs_PSA_fixed_m1_12</li></ul></li><li>m2<ul><li>28032022 calibration mercury gas generator VSL vs PSA fixed m2</li><li>single_point_VSL_vs_PSA_fixed_m2_4</li><li>single_point_VSL_vs_PSA_fixed_m2_6</li><li>single_point_VSL_vs_PSA_fixed_m2_8</li><li>single_point_VSL_vs_PSA_fixed_m2_12</li></ul></li><li>m3 <ul><li>06042022 calibration mercury gas generator VSL vs PSA fixed m3</li><li>single_point_VSL_vs_PSA_fixed_m3_4</li><li>single_point_VSL_vs_PSA_fixed_m3_6</li><li>single_point_VSL_vs_PSA_fixed_m3_8</li><li>single_point_VSL_vs_PSA_fixed_m3_12</li></ul></li><li>m4 <ul><li>12042022 calibration mercury gas generator VSL vs PSA fixed m4</li><li>single_point_VSL_vs_PSA_fixed_m4_4</li><li>single_point_VSL_vs_PSA_fixed_m4_6</li><li>single_point_VSL_vs_PSA_fixed_m4_8</li><li>single_point_VSL_vs_PSA_fixed_m4_12</li></ul></li><li>less tubing <ul><li>14042022 calibration mercury gas generator VSL vs PSA fixed less tubing</li><li>single_point_VSL_vs_PSA_fixed_less_tubing</li></ul></li><li>less tubing and air as complementary gas <ul><li>19042022 calibration mercury gas generator VSL vs PSA fixed less tubing in air</li><li>single_point_VSL_vs_PSA_fixed_less_tubing_air</li></ul></li></ul></li><li>VSL vs PSA after modification<ul><li>m1 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m1 20230324</li><li>PSA_fixed_air_m1_9</li><li>PSA_fixed_air_m1_11</li><li>PSA_fixed_air_m1_14</li></ul></li><li>m2 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m2 20230327</li><li>PSA_fixed_air_m2_9</li><li>PSA_fixed_air_m2_11</li><li>PSA_fixed_air_m2_14</li></ul></li><li>m3 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m3 20230329</li><li>PSA_fixed_air_m3_9</li><li>PSA_fixed_air_m3_11</li><li>PSA_fixed_air_m3_14</li></ul></li><li>m4 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m4 20230907</li><li>PSA_fixed_air_m4_9</li><li>PSA_fixed_air_m4_11</li><li>PSA_fixed_air_m4_14</li></ul></li><li>m5 air as complemantary gas <ul><li>Calibration PSA fixed mercury gas generator air m5 20230911</li><li>PSA_fixed_air_m5_9</li><li>PSA_fixed_air_m5_11</li><li>PSA_fixed_air_m5_14</li></ul></li><li>m1 nitrogen (N2) as complementary gas<ul><li>Calibration PSA fixed mercury gas generator nitrogen m1 20230330</li><li>PSA_fixed_N2_m1_9</li><li>PSA_fixed_N2_m1_11</li><li>PSA_fixed_N2_m1_14</li></ul></li><li>m2 N2 as complementary gas <ul><li>Calibration PSA fixed mercury gas generator nitrogen m2 20230331</li><li>PSA_fixed_N2_m2_9</li><li>PSA_fixed_N2_m2_11</li><li>PSA_fixed_N2_m2_14</li></ul></li><li>m3 N2 as complementary gas <ul><li>Calibration PSA fixed mercury gas generator nitrogen m3 20230405</li><li>PSA_fixed_N2_m3_9</li><li>PSA_fixed_N2_m3_11</li><li>PSA_fixed_N2_m3_14</li></ul></li><li>measurement at TUV<ul><li>PSA_Fixed_at_TUV</li></ul></li></ul></li></ul>
Data for PSA 10.536 Elemental Hg generator performance evaluation report
<p>During the SI-Hg performance evaluation of elemental mercury gas generators on the market three generators were tested, e.g., PSA 10.536 elemental Hg generator, bell-jar and Tekran Model 3425. Key characteristics were determined e.g.; the stabilisation period, short-term drift, precision, i.e., reproducibility and repeatability of the concentration generated, linearity, bias, sensitivity to sample gas pressure, sensitivity to surrounding temperature and sensitivity to electrical voltage. All three generators could be tested according to the calibration protocol developed within the project. The results obtained with the different gas generator clearly shows the importance of a metrological calibration. All three candidate generators show a different bias for the setpoint compared to the calibrated output. </p><p>The data obtained during the performance evaluation of the PSA 10.536 elemental Hg generator is published in this repository. The files of the following experiments can be found here:</p><ul><li>range1 m1<ul><li>Calibration_PSA_range1_m1_20221130</li><li>multi_point_calibration_PSA_range1_m1</li></ul></li><li>range1 m2<ul><li>Calibration_PSA_range1_m2_20221209</li><li>multi_point_calibration_PSA_range1_m2</li></ul></li><li>range1 m3<ul><li>Calibration_PSA_range1_m3_20221214</li><li>multi_point_calibration_PSA_range1_m3</li></ul></li><li>range1 m4<ul><li>Calibration_PSA_range1_m4_20230915</li><li>multi_point_calibration_PSA_range1_m4</li></ul></li><li>range1 m5<ul><li>Calibration_PSA_range1_m5_20230919</li><li>multi_point_calibration_PSA_range1_m5</li></ul></li><li>range2 m1<ul><li>Calibration_PSA_range2 m1 20221006</li><li>multi_point_calibration_PSA_range2_m1</li></ul></li><li>range2 m2<ul><li>Calibration_PSA_range2 m2 20221011</li><li>multi_point_calibration_PSA_range2_m2</li></ul></li><li>range2 m3<ul><li>Calibration_PSA_range2 m3 20221012</li><li>multi_point_calibration_PSA_range2_m3</li></ul></li><li>short-term drift<ul><li>m1<ul><li>Calibration mercury gas generator range1 short term drift 20221012 deel 1</li><li>PSA_short_term_drift_M1</li></ul></li><li>m2<ul><li>Calibration mercury gas generator range1 short term drift 20221012 deel 2</li><li>PSA_short_term_drift_M2</li></ul></li><li>m3<ul><li>Calibration mercury gas generator range1 short term drift 20221012 deel 3</li><li>PSA_short_term_drift_M3</li></ul></li><li>m4<ul><li>Calibration mercury gas generator range1 short term drift 20221012 deel 4</li><li>PSA_short_term_drift_M4</li></ul></li></ul></li><li>stability<ul><li>Calibration mercury gas generator range2 stability 20220811</li><li>Calibration mercury gas generator stability 20221018</li></ul></li></ul>
Generative deep learning for hydrological forecasting: CVAE-75 basins from CANOPEX_v1
<p>Data associated with https://doi.org/10.1016/j.jhydrol.2023.130498</p>
FoldingDiff generated structures (n=780, main results) and associated metadata
<p>Backbone structures generated by FoldingDiff spanning lengths [50, 128). Each length has 10 randomly sampled structures for a total of 780 backbone structures. These were used to derive all results in our manuscript's main results section. In addition to structures in .pse format, we provide an excel table with the following sheets:</p><ul><li>Table containing metadata for each of the aforementioned generated structures. Metadata includes scTM designability scores using ProteinMPNN + OmegaFold and using ProteinMPNN + AlphaFold2, maximum training set TM score (similarity), structure length, and number of sheets/helices present as annotated by P-SEA.</li><li>Table containing Gauss integral embeddings for each of the 780 backbones generated by FoldingDiff</li><li>Table containing Gauss integral embeddings for select test set structures between 50 and 128 residues in length. These were used to compare and contextualize structures/embeddings from FoldingDiff.</li></ul>
Distribution grid data generated by ding0
<p>Distribution grid data generated with ding0 in the <a href="https://ego-n.org/" target="_blank" rel="noopener">eGo^n project</a>.<br>Data from pre-release v0.3.0-alpha using branch <em>ding0_run/2023_04_06</em>, head: <a href="https://github.com/openego/ding0/tree/9fe5f1c3785ccb2f5afa56fe25e4f2290df76e5c" target="_blank" rel="noopener">9fe5f1c3785ccb2f5afa56fe25e4f2290df76e5c</a>.</p> <p>Input data from eGon-data, branch <a href="https://github.com/openego/eGon-data/tree/continuous-integration/run-everything-2022-11-10" target="_blank" rel="noopener">run-everything-2022-11-10</a>.</p> <p>See <code>README.md</code> for details.</p> <p> </p> <p> </p>
Generation of Random laser from Dye-Derived Red-Emitting Carbon Dots
<p>Carbon dots are carbon-based nanoparticles renowned for their intense light-emitting capabilities covering the whole visible light range. Here, we overcome these problems by solvothermally synthesizing carbon dots starting from Neutral Red, a common red-emitting dye, as a molecular precursor. The obtained nanoparticles are highly luminescent in the red region, with a quantum yield comparable to that of the starting dye. Most importantly, the nanoparticle carbogenic matrix protects the Neutral Red molecules from photobleaching under ultraviolet excitation while preventing aggregation-induced quenching, thus allowing solid-state emission inside PVA. Finally, the dye-based carbon dots demonstrate stable and efficient random lasing emission in the red region.</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.