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.
12
datasets available to search
ShareScore release 0.9.0
Dataset results
12 results for “Neural variability”
Are Neural Bug Detectors Comparable to Software Developers on Variable Misuse Bugs?
<p>Artifact for "Are Neural Bug Detectors Comparable to Software Developers on Variable Misuse Bugs?"</p> <p><strong>Abstract:</strong> </p> <p>Debugging, that is, identifying and fixing bugs in software, is a central part of software development. Developers are therefore often confronted with the task of deciding whether a given code snippet contains a bug, and if yes, where. Recently, data-driven methods have been employed to learn this task of bug detection, resulting (amongst others) in so called neural bug detectors. Neural bug detectors are trained on millions of buggy and correct code snippets.</p> <p>Given the “neural learning” procedure, it seems likely that neu- ral bug detectors – on the specific task of finding bugs – have a performance similar to human software developers. For this work, we set out to substantiate or refute such a hypothesis. We report on the results of an empirical study with over 100 software developers, targeting the comparison of humans and neural bug detectors. As detection task, we chose a specific form of bugs (variable misuse bugs) for which neural bug detectors have recently made significant progress. Our study shows that despite the fact that neural bug detectors see millions of such misuse bugs during training, software developers – when conducting bug detection as a majority decision – are slightly better than neural bug detectors on this class of bugs. Altogether, we find a large overlap in the performance, both for classifying code as buggy and for localizing the buggy line in the code. In comparison to developers, one of the two evaluated neural bug detectors, however, raises a higher number of false alarms in our study.</p> <p><strong>Content:</strong> The artifact includes the following components:</p> <ul> <li> <p><strong>Web UI:</strong> The developer survey was performed online in the browser of the participants. For this, we created a custom web interface tailored for our study task. We included both the implementation of the frontend (website) and backend implementation (buisness logic and database) in this artifact. Therefore, it is not only possible to replicate our survey with same interface and a new group of participants but it is also possible to extend the interface for future studies. </p> </li> <li> <p><strong>Neural bug detectors: </strong>We evaluate the performance of the developers against two neural bug detectors. In this artifact, we include the bug detectors (implementation + trained models) and the evaluation script used for producing our results. Besides the replication of our bug detector evaluation, the detectors can also be used in future projects for detecting variable misuse bugs in Java methods.</p> </li> <li> <p><strong>Analysis scripts</strong>: After collecting the raw results from the developers and neural bug detectors, we performed several analysis to gain insights how developers and bug detectors compare on the variable misuse task. We include all analysis steps in form of Jupyter notebooks in the artifact. With this, it is possible to reproduce all the figures of our paper. </p> </li> </ul> <p>In addition, we also provide further artifacts that were successfully evaluated at ASE 2022:</p> <p><strong>ASE 2022 Artifact: </strong><a href="https://doi.org/10.5281/zenodo.6958242">10.5281/zenodo.6958242</a></p> <p><strong>Virtual machine: </strong><a href="https://doi.org/10.5281/zenodo.6957849">10.5281/zenodo.6957849</a></p>
Data from: Convolutional neural networks trained on internal variability predict forced response of TOA radiation by learning the pattern effect
Open the record for dataset details and reuse information.
Variable allelic expression of imprinted genes at the Peg13, Trappc9, Ago2 cluster in single neural cells
<p>Fig 1 5' RACE - Sequence tracks and analysis of alternative Trappc9 transcriptional start sites</p> <p>Fig 2 and suppl fig 3- Brain and Kidney tissue or Neural stem cells isolated from the Hippocampus region of newborn mice generated from a C57BL/6 (female) and a Cast/EiJ (male) cross (and its reciprocal cross) was used to determine allelic bias expression. A SNP located within an exon of Peg13, Trappc9, Ago2, Chrac1 and Kcnk9 was identified and amplified via pyrosequencing PCR with the percentage of SNP identification used to determine allele expression percentages. Additionally, Pyrorun sequences of reverse transcribed RNA from Trappc9 expression in different tissues. A hybrid cross between C57BL/6 and JF1 mouse was used to generate hybrid pups that were used to determine allele specificity of Trappc9 expression in Kidney and brain tissues.</p> <p>Figs 3, 4 & 5- Single neural stem cells were isolated from the Hippocampus of newborn mice generated from a hybrid cross. Some of these cells were differentiated In vitro and either the NSC or differentiated neurons were lysed and underwent a reverse transcription. The newly formed cDNA was used as a template to amplify expressed Peg13, Trappc9 or Ago2 transcripts which was then sent for Sanger sequencing. A SNP located within the exon was used to determine whether the transcript from that cell was generated from the maternal or paternal allele.</p> <p>Fig 6- Brain-specific regulatory elements were cloned into a pGL [Luc] vector containing a Trappc9 promoter. These newly generated plasmids were then transfected into either primary neuron or fibroblast cultures alongside a Renilla vector for normalization using Lipofectamine as a transfection reagent. After 48 hours the cells were lysed and analyzed using a Glomax illuminator to determine their impact on Luciferase expression compared to that of the pGL vector containing just the Trappc9 promoter. Additionally, Plasmid vectors that were used for transfection of primary neurons to determine the impact of brain-specific regulatory elements on transcription. Plasmids can be visualized using the free software pdraw.32 downloaded from http://acaclone.com/download/install.htm</p> <p>Suppl fig 2- Single Neural stem cells and in vitro differentiated neurons isolated from Mouse Hippocampus tissue underwent reverse transcription and a qPCR reaction intended to amplify cDNA of genes associated with specific neural cell types as a method of detecting cell fate and whether this had an impact on allele-specific expression and differential methylation.</p> <p>Suppl fig 4 & 5- DNA isolated from Neural stem cells was bisulfite converted for downstream identification of methyl group presence. CpG islands located at or near the promoters of the Peg13, Trappc9, Ago2, Chrac1 and Kcnk9 genes were amplified and cloned into a TOPO vector. The cloned segments were Sanger sequenced and compared to the original non-bisulfite converted sequence using the Quantification for methylation analysis (QUMA) tool to determine which CG dinucleotides were methylated and which weren't. Pyroruns determining methylation frequency at the CpG islands of these genes can be found in a separate upload on Zenodo.</p>
Thalamocortical interactions shape hierarchical neural variability during stimulus perception dataset
<p>Dataset used in the Thalamocortical interactions shape hierarchical neural variability during stimulus perception article.</p> <p> </p> <p>Dataset contains neural activity recordings of a vibrotactile detection task recorded in four monkeys in the following areas: somatosensory thalamus (VPL), 3b and area 1 of the somatosensory cortex (S1)</p>
A neural network-based estimate of the seasonal variability of total alkalinity in the East China Sea shelf
<p>In order to estimate the seasonal variability of total alkalinity in the ECS shelf, an artificial neural network (ANN) model was developed using 5 cruise datasets from 2008 to 2018. The model used temperature, salinity, and dissolved oxygen to estimate A<sub>T</sub> with a root-mean-square error of ~7 umol kg<sup>-1</sup>, and was applied to fill missing alkalinity data for 8 cruises during 2013-2016. In addition, monthly water column A<sub>T</sub> for the period 2000-2016 was also obtained passing temperature, salinity, and dissolved oxygen from the Changjiang Biology Finite-Volume Coastal Ocean Model (FVCOM) Data. Spatial distributions, seasonal cycles and correlations of surface A<sub>T</sub> indicated that the seasonal fluctuation of the Changjiang River discharge is the major factor affecting seasonal variation of surface total alkalinity in the ECS shelf. The largest seasonal fluctuation of surface total alkalinity was found on the inner shelf near the Changjiang Estuary.</p>
Meta-analysis of scRNA-seq Co-expression in Human Neural Organoids Reveals High Variability in Recapitulating Primary Tissue
<p>Contains all code and data for Werner and Gillis, Meta-analysis of scRNA-seq Co-expression in Human Neural Organoids Reveals High Variability in Recapitulating Primary Tissue, 2024. </p> <p>Additionally, the code and data for this paper can be found at https://github.com/JonathanMWerner/meta_organoid_analysis with an easy to view github markdown file containing all the code used to generate all figure panel plots at https://github.com/JonathanMWerner/meta_organoid_analysis/blob/main/figure_plots_with_data_code.md.</p> <p>Due to file size limits on github, there are several data files not available on github, but are available here on zenodo in the data_for_plots.zip file, see below:</p> <pre>umap_embeddings_Fig2A.Rdata<br>cross_dataset_aggregated_exp_metaMarker_all_fetal_SuppFig1B_Fig2E.Rdata<br>organoid_egad_results_ranked_6_26_24_Fig3D.Rdata<br>fetal_egad_results_ranked_6_26_24_Fig3D.Rdata<br>org_eigenvec_matrices_SuppFig3CD.Rdata</pre> <p><br>The R package developed for this paper is available at https://github.com/JonathanMWerner/preservedCoexp</p>
Single trial variability in neural activity during a working memory task reveals multiple distinct information processing sequences
<p>0-,1-,2-back behavioral and preprocesses EEG data for <em>Single trial variability in neural activity during a working memory task reveals multiple distinct information processing sequences</em></p>
Investigating Neural Response Variability as a Single-patient Predictor of Successful CBT in Clinical Psychiatry
ClinicalTrials.gov study NCT04191811. IPD Sharing: Not stated. Countries: 1. Publications: 11.
Behavioral and Neural Responses to External Alterations of Speech Variability
ClinicalTrials.gov study NCT05286658. IPD Sharing: YES. Countries: 1. Publications: 0.
Benchmark datasets used in "Classification of Periodic Variables with Cyclic-Permutation Invariant Neural Networks"
<p>To aid the reproducibility of the results in the paper “Classification of Periodic Variables with Cyclic-Permutation Invariant Neural Networks,” we make our aggregation of the following data available. Code used to load the data and generate the results can be found at <a href="https://github.com/kmzzhang/periodicnetwork">https://github.com/kmzzhang/periodicnetwork</a>. These datasets have been constructed from publicly available data sources. If you use these datasets, please cite the original papers [1, 2, 3], in addition to ours [TBD]. Others might find this data useful for testing time-series inference techniques.</p> <p>[1] Jayasinghe, T. et al. The ASAS-SN catalogue of variable stars I: The Serendipitous Survey. Monthly Notices of the Royal Astronomical Society 477, 3145–3163 (2018). URL <a href="https://academic.oup.com/mnras/article/477/3/3145/4961151">https://academic.oup.com/mnras/article/477/3/3145/4961151</a>.<br> [2] Alcock, C. et al. The MACHO Project LMC Variable Star Inventory.II.LMC RR Lyrae Stars- Pulsational Characteristics and Indications of a Global Youth of the LMC. The Astronomical Journal 111, 1146 (1996). URL <a href="http://adsabs.harvard.edu/abs/">http://adsabs.harvard.edu/abs/</a>1996AJ....111.1146A.<br> [3] Udalski, A. The Optical Gravitational Lensing Experiment. Real Time Data Analysis Systems in the OGLE-III Survey. Acta Astronomica 53, 291–305 (2003). URL http: <a href="https://bloomlab-berkeley.slack.com//adsabs.harvard.edu/abs/2003AcA....53..291U">//adsabs.harvard.edu/abs/2003AcA....53..291U</a>.</p>
Dataset - Solving Seismic Wave Equation on Variable Velocity Models with Fourier Neural Operator
<p>The directory contains velocity models from OpenFWI dataset collection. These velocity models are the inputs of the FNO-based sovlers in the paper <strong><em>Solving Seismic Wave Equation on Variable Velocity Models with Fourier Neural Operator</em></strong>.</p>
Binges and Neural Variability
ClinicalTrials.gov study NCT04184856. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.