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915 results for “human development”
Raw data for "Development and characterization of a non-human primate model of disseminated synucleinopathy"
<p><span>In this study, the performance and biodistribution of the retrogradely-spreading AAV9-SynA53T vector was evaluated in the NHP brain. Conducted intraparenchymal deliveries of viral suspensions in the left putamen gave rise to a disseminated synucleinopathy in a circuit-specific basis.</span></p>
200 Annotated Developer Human Errors from GitHub
<p><em><strong>Software Engineers' Human Errors</strong></em></p> <p>This dataset contains 200 GitHub comments with manual human error annotations, released as part of the following publication:</p> <ul> <li>Benjamin S. Meyers. <a href="https://scholarworks.rit.edu/theses/11609/">Human Error Assessment in Software Engineering.</a> Rochester Institute of Technology. 2023.</li> </ul> <p><em><strong>Included Files</strong></em></p> <p>The "developer_human_errors.csv" file contains the full dataset of 200 software defect descriptions annotated with human error types (slips, lapses, mistakes) and T.H.E.S.E. categories.</p> <p><em><strong>CSV Fields</strong></em></p> <ul> <li><strong>ID</strong>: Unique identifier for the comment.</li> <li><strong>SOURCE</strong>: Whether this comment originates from a commit, issue, or pull request.</li> <li><strong>COMMENT_URL</strong>: The URL linking to the comment.</li> <li><strong>COMMENT_TEXT</strong>: The raw comment text.</li> <li><strong>HUMAN_ERROR_TYPE</strong>: Whether the software defect described is a slip, lapse, or mistake.</li> <li><strong>THESE_V4_ID</strong>: Manually assigned T.H.E.S.E. category with labels corresponding to Version 4 of T.H.E.S.E.</li> <li><strong>THESE_NAME</strong>: Name corresponding to manually assigned T.H.E.S.E. category.</li> </ul> <p><em><strong>Annotation Details</strong></em></p> <p>Human error types span slips, lapses, and mistakes from James Reason's Generic Error Modelling System (GEMS):</p> <ul> <li><strong>Slips</strong>: Failures of attention.</li> <li><strong>Lapses</strong>: Failures of memory.</li> <li><strong>Mistakes</strong>: Failures of planning.</li> </ul> <p>T.H.E.S.E. categories are summarized below:</p> <ul> <li>S01: Typos & Misspellings</li> <li>S02: Syntax Errors</li> <li>S03: Overlooking documented Information</li> <li>S04: Multitasking Errors</li> <li>S05: Hardware Interaction Errors</li> <li>S06: Overlooking Proposed Code Changes</li> <li>S07: Overlooking Existing Functionality</li> <li>S08: General Attentional Failure</li> <li>L01: Forgetting to Finish a Development Task</li> <li>L02: Forgetting to Fix a Defect</li> <li>L03: Forgetting to Remove Development Artifacts</li> <li>L04: Working with Outdated Source Code</li> <li>L05: Forgetting an Import Statement</li> <li>L06: Forgetting to Save Work</li> <li>L07: Forgetting Previous Development Discussion</li> <li>L08: General Memory Failure</li> <li>M01: Code Logic Errors</li> <li>M02: Incomplete Domain Knowledge</li> <li>M03: Wrong Assumption Errors</li> <li>M04: Internal Communication Errors</li> <li>M05: External Communication Errors</li> <li>M06: Solution Choice Errors</li> <li>M07: Time Management Errors</li> <li>M08: Inadequate Testing</li> <li>M09: Incorrect/Insufficient Configuration</li> <li>M10: Code Complexity Errors</li> <li>M11: Internationalization/String Encoding Errors</li> <li>M12: Inadequate Experience Errors</li> <li>M13: Insufficient Tooling Access Errors</li> <li>M14: Workflow Order Errors</li> <li>M15: General Planning Failure</li> </ul> <p><em><strong>Contact</strong></em></p> <p>Please contact Benjamin S. Meyers (<a href="mailto:bsm9339@rit.edu">email</a>) with questions about this data and its collection.</p> <p><em><strong>Acknowledgments</strong></em></p> <p>Collection of this data has been sponsored in part by the National Science Foundation (grant 1922169), by the NSA Science of Security Lablet program (grant H98230-17-D-0080/2018-0438-02), and by a Department of Defense DARPA SBIR program (grant 140D63-19-C-0018).</p>
Quantitative results of the analysis of human bioengineered tissues corresponding to the work "Development of novel squid gladius biomaterials for cornea tissue engineering"
<p>This dataset corresponds to the quantitative data generated in the work entitled "Development of novel squid gladius biomaterials for cornea tissue engineering".</p> <p>Cornea tissue engineering is strictly dependent on the development of biomaterials fulfilling the strict biocompatibility, biomechanical and optical requirements of this organ. In this work, we have generated novel biomaterials from the squid gladius (SG) and their application in cornea tissue engineering was evaluated. Results revealed that the native SG (N-SG) was biocompatible in laboratory animals, although a local inflammatory reaction was driven by the material. Cellularized biomaterials (C-SG) demonstrated that the SG provides an adequate substrate for cell attachment and growth, and corneal epithelial cells cultured on this biomaterial were able to express crystallin alpha, a marker for this type of cells. Biomechanical analyses showed that N-SG biomaterials have higher Young modulus and lower traction deformation than control native corneas (CTR), and C-SG showed similar Young modulus than CTR. Analysis of the optical properties of these samples revealed that the diffuse transmittance of N-SG and C-SG were higher than CTR, with the diffuse reflectance showing the opposite behavior. These results confirm the putative usefulness of this abundant marine-derived biomaterial that can be obtained as a byproduct of the fishing industry.</p>
Data from: Public human microbiome data are dominated by highly developed countries
<p>Supplementary tables and datasets associated with the <em>PLOS Biology </em>publication "Public human microbiome data are dominated by highly developed countries." See README.txt for a description of the files and the data fields they contain.</p> <p>Update, 8 Apr 2022: The "figures.md" file described in the readme was inadvertently left out of the initial upload. It has been added here.</p>
The Human Developing Cerebral Cortex Is Characterized by an Elevated De Novo Expression of Long Noncoding RNAs in Excitatory Neurons
<p>This project contains the annotated transcriptomes in GTF format used in the manuscript "The Human Developing Cerebral Cortex Is Characterized by an Elevated De Novo Expression of Long Noncoding RNAs in Excitatory Neurons" DOI: <a href="https://doi.org/10.1093/molbev/msae123">https://doi.org/10.1093/molbev/msae123</a></p>
Dataset for the article "Development of an integrated socio-hydrological modeling framework for assessing the impacts of shelter location arrangement and human behaviors on flood evacuation processes"
<p>This dataset include the data needed to create the socio-hydrological model to simulate human evacuation processes via a transportation network before a flood hits the residential area. Source code, in JAVA, for generating households in the agent-based model are also provided. </p>
Aberrant development of pancreatic beta cells derived from human iPSCs with FOXA2 deficiency
<p><strong>Project manager(s)</strong><strong>: </strong>Essam M. Abdelalim</p> <p>Induced pluripotent stem cells (iPSCs) were generated from a patient with a heterozygous deletion of the short arm of chromosome 20 at bands p11.22 to p11.21 (~969 kb deletion), which contains only one gene, <em>FOXA2 </em>(<em>FOXA2<sup>+/-</sup></em>iPSCs) as well as healthy controls (Ctr1 iPSCs and Ctr2 iPSCs). <em>FOXA2<sup>+/-</sup></em>iPSCs were differentiated into different stages of beta cell development to understand the role of FOXA2 during pancreatic beta cell development as described in the article entitled "<strong>Aberrant development of pancreatic beta cells derived from human iPSCs with <em>FOXA2</em> deficiency" by Elsayed et al</strong>. The dataset represents RNA-seq data generated from pancreatic progenitors (PP2) and endocrine progenitors (EPs) derived from Ctr1 iPSCs, Ctr2 iPSCs, and three clones of <em>FOXA2<sup>+/-</sup></em>iPSCs. </p> <p>The file name is: Sample name _overall sample number_read direction_001 where:</p> <p>- PP2-Ctr 1: pancreatic progenitors (PP2) derived from Ctr1 iPSCs (healthy control 1)</p> <p>- PP2-Ctr 2: pancreatic progenitors (PP2) derived from Ctr2 iPSCs (healthy control 2)</p> <p>- PP2-FOX1, PP2-FOX2, and PP2-FOX3: pancreatic progenitors (PP2) derived from three different clones of patient-derived <em>FOXA2<sup>+/-</sup></em>iPSCs.</p> <p>- EP-Ctr1: endocrine progenitors (Eps) derived from Ctr1 iPSCs (healthy control 1)</p> <p>- EP-Ctr2: endocrine progenitors (Eps) derived from Ctr2 iPSCs (healthy control 2)</p> <p>- EP-FOX R1, EP-FOX R2, and EP-FOX R3: endocrine progenitors (EPs) derived from three different clones of patient-derived <em>FOXA2<sup>+/-</sup></em>iPSCs.</p> <p>- The RNA-Seq data were generated from two Ctr-iPSC lines and three FOXA2<sup>+/-</sup>iPSC lines.</p> <p>- Read direction: R1 (Forward), R2 (Reverse).</p>
Data supporting the manuscript "Sexually divergent development of depression-related brain networks during healthy human adolescence"
<p>This data supports the manuscript "Sexually divergent development of depression-related brain networks during healthy human adolescence" by Dorfschmidt et al. Part of these <a href="https://doi.org/10.6084/m9.figshare.11551602">data</a> were initially released by Váša et al. (2020) as part of their <a href="https://doi.org/10.1073/pnas.1906144117">manuscript</a>. Please cite them when using these data. </p> <p> </p>
Identification and characterization of ATOH7-regulated target genes and pathways in human neuroretinal development
<p>The files presented here represent the original data collected during the study presented in the Cells (MDPI) publication "Identification and characterization of ATOH7-regulated target genes and pathways in human neuroretinal development" (<a href="https://doi.org/10.3390/cells13131142">https://doi.org/10.3390/cells13131142</a>). The data include:</p> <ul> <li>scRNA sequencing data (matrix, features and barcodes)</li> <li>RNA sequencing data (fastq)</li> <li>CUT&RUN sequencing data (raw data: fastq; coverage: bigWig)</li> <li>RNA sequencing alignments, with custom built reference (RNA_alignments)</li> </ul>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 2. Human body sizes for men/women.
<p>We propose an efficient, simple and robust human body feature extraction based on the front and side images of a human body. Description of anthropometric data - men/women: Dataset based on an experiment is used to test the system data describing the anthropometric features of men, includes 12 sizes of the human body, which are presented in figure 2. </p>
Developer-Centric Test Amplification: The Interplay Between Automatic Generation and Human Exploration --- Appendix
<p>This online appendix contains the accumulated code occurences during the interviews performed to evaluate our developer-centric test amplification approach and the TestCube plugin. In addition, it documents the inter-rater-reliability analysis we performed.</p>
Supporting Data for Human Factors in Developing Automated Vehicles:A Requirements Engineering Perspective
<p>This data set complements our manuscript in submission with the title:</p> <p>"Human Factors in Developing Automated Vehicles: A Requirements Engineering Perspective"</p> <p>We provide two files:</p> <p>a) the interview guide</p> <p>b) an overview that maps from themes to example quotes and codes derived from particular interview subjects</p>
Data for "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms"
<p>This repository contains the data and external data used by teams in the Kaggle competition "HuBMAP+HPA - Hacking the Human Body" and is part of the paper "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms".</p> <p>The directories contain:</p> <p><strong>data.zip:</strong> The training and test data, including metadata, used in the Kaggle competition "HuBMAP + HPA - Hacking the Human Body".</p> <p><strong>Team_1.zip: </strong>External data used by the first place winning solution.</p> <p><strong>Team_2.zip: </strong>External data used by the second place winning solution.</p>
Trained Models for "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms"
<p>This repository contains the trained model weights for the baseline model and the winning solutions in the Kaggle competition "HuBMAP+HPA - Hacking the Human Body", and is part of the paper "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms".</p> <p>The directory contains:</p> <p><strong>trained_model_1_weights.zip: </strong>Trained model weights for first place solution (Team 1).</p> <p><strong>trained_model_2_weights.zip:</strong> Trained model weights for second place solution (Team 2).</p> <p><strong>trained_model_3_weights.zip: </strong>Trained model weights for third place solution (Team 3).</p> <p><strong>trained_model_weights_baseline.zip:</strong> Trained model weights for the baseline model.</p>
Co-development of gut microbial metabolism and visual neural circuitry over human infancy
Open the record for dataset details and reuse information.
Data from: Responses of sympatric canids to human development revealed through citizen science
<p>Measuring wildlife responses to anthropogenic activities often requires long-term, large-scale datasets that are difficult to collect. This is particularly true for rare or cryptic species, which includes many mammalian carnivores. Citizen science, in which members of the public participate in scientific work, can facilitate collection of large datasets while increasing public awareness of wildlife research and conservation. Hunters provide unique benefits for citizen science given their knowledge and interest in outdoor activities. We examined how anthropogenic changes to land cover impacted relative abundance of two sympatric canids, coyote (<i>Canis latrans</i>) and red fox (<i>Vulpes vulpes</i>) at a large spatial scale. In order to assess how land cover affected canids at this scale, we used citizen science data from bow hunter sighting logs collected throughout New York State, USA, during 2004–2017. We found that the two species had contrasting responses to development, with red foxes positively correlated and coyotes negatively correlated with the percentage of low-density development. Red foxes also responded positively to agriculture, but less so when agricultural habitat was fragmented. Agriculture provides food and denning resources for red foxes, whereas coyotes may select forested areas for denning. Though coyotes and red foxes compete in areas of sympatry, we did not find a relationship between species abundance, likely a consequence of the coarse spatial resolution used. Red foxes may be able to coexist with coyotes by altering their diets and habitat use, or by maintaining territories in small areas between coyote territories. Our study shows the value of citizen science, and particularly hunters, in collection of long-term data across large areas (i.e., the entire state of New York) that otherwise would unlikely be obtained.</p>
Human Development Index for China 1949-2019
<p>1949-2019 China Human Development Index (HDI) from Li et al. (2021). It is the ultimate criteria for the United Nations to assess a country's development. It can be used in natural disasters, reinsurance and other fields to normalize some time-varying data. </p> <p>Citation: "Y. Li, D. Xin, Z. Zhang, A rapid-response earthquake fatality estimation model for mainland China, Int. J. Disast. Risk Re. 66 (2021) 102618. https://doi.org/https://doi.org/10.1016/j.ijdrr.2021.102618".</p>
Supporting Data for Human Factors in Developing Automated Vehicles:A Requirements Engineering Perspective
<p>This data set complements our manuscript in submission with the title:</p> <p>"Human Factors in Developing Automated Vehicles: A Requirements Engineering Perspective"</p> <p>We provide two files:</p> <p>a) the interview guide</p> <p>b) an overview that maps from themes to example quotes and codes derived from particular interview subjects</p>
Dataset: Inverse responses of species richness and niche specialization to human development
<p class="CxSpFirst">Humans impact biodiversity by altering land use and introducing nonnative species. Yet the extent to which coexistence processes, such as competition and niche shifts, mediate these relationships is not clear. This dataset was used in a study that aims to compare how human development influences wetland plant diversity by examining patterns of species richness, niche specialization, and nonnative species occurrences along a human development gradient.</p> <p class="CxSpFirst">This dataset can be used to analyzed species richness and niche specialization (a measure of the range of human development extents over which a species occurs) patterns from species occurrence data across 1582 wetlands in Alberta, Canada. Associations between human development extent and species richness, niche specialization, and nonnative species can be tested using linear mixed models. Also, nonmetric multidimensional scaling ordination can be applied from raw data (see usage notes) to examine whether community composition differed among wetlands surrounded by different human development extents.</p> <p class="CxSpFirst">Note that human development data are accessible only through a data sharing agreement with ABMI. See the readme document for more details on how to obtain assess to these data.</p> <p class="CxSpFirst">Results of these analyses can be found in the corresponding publication: Inverse responses of species richness and niche specialization to human development, Journal of Biogeography. https://doi.org/10.1111/jbi.14240</p> <ul> </ul>
Data for: Development of a method for the measurement of human scent samples using comprehensive two-dimensional gas chromatography with mass detection
<p>This dataset was used for development of a method for the measurement of human scent samples using comprehensive two-dimensional gas chromatography with mass detection [<a href="https://doi.org/10.1016/j.forsciint.2016.09.011">https://doi.org/10.1016/j.forsciint.2016.09.011</a>].</p> <p>The dataset contains chromatograms of a model mixture of human scent and chromatograms of the human scent of one volunteer measured on different column setups. Each sample was processed in ChromaToF(version 4.72.0.0) by LECO corp. The processing step was executed at the signal-to-noise (SN) ratio levels 100, 300, and 500 (human scent samples chromatograms).</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.