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53 results for “Git”
Introduction to Ancient Metagenomics Textbook (Edition 2025): Introduction to Git(Hub)
<p>Data and conda software environment file for the chapter 'Introduction to Git(Hub)' of the SPAAM Community's textbook: Introduction to Ancient Metagenomics (https://www.spaam-community.org/intro-to-ancient-metagenomics-book).</p>
Kanban tasks opening and close dates differences between git and Jira
<p>Dataset contains about three years of records from a software development company.</p> <p>Dataset contains the task start and end date registered in Jira software and the open and merged dates for those same tasks extracted from git records.</p> <p>This dataset compares those opening and close dates and shows the differences</p>
Datasets associated with the git repo: https://github.com/maikeh7/Surrogate_Assisted_GLM
<p>These datasets are necessary for carrying out analyses in https://github.com/maikeh7/Surrogate_Assisted_GLM</p>
Text-fig. 2. Soft-tissue imprints and traces of bioerosion on Middle Ordovician cephalopods from Estonia. a: GIT 819-1, Tragoceras falcatum (SCHLOTHEIM, 1820), drag bands; b: GIT 819-1, T. falcatum, pseudosutures; c: GIT 819-2, Estonioceras sp., drag bands; d: GIT 819-3, cf. Anthoceras vaginatum (SCHLOTHEIM, 1820), drag bands; e: GIT 819-4, cf. Orthoceras regulare SCHLOTHEIM, 1820, drag bands; f: Pits on the body chamber of GIT 819-1, T. falcatum. Specimens oriented with aperture downwards. Scale bars 1 mm. in Conch Structures, Soft-Tissue Imprints And Taphonomy Of The Middle Ordovician Cephalopod Tragoceras Falcatum From Estonia
Text-fig. 2. Soft-tissue imprints and traces of bioerosion on Middle Ordovician cephalopods from Estonia. a: GIT 819-1, Tragoceras falcatum (SCHLOTHEIM, 1820), drag bands; b: GIT 819-1, T. falcatum, pseudosutures; c: GIT 819-2, Estonioceras sp., drag bands; d: GIT 819-3, cf. Anthoceras vaginatum (SCHLOTHEIM, 1820), drag bands; e: GIT 819-4, cf. Orthoceras regulare SCHLOTHEIM, 1820, drag bands; f: Pits on the body chamber of GIT 819-1, T. falcatum. Specimens oriented with aperture downwards. Scale bars 1 mm.
Text-fig. 1. Megastriae and post mortem epicoles on Tragoceras falcatum (SCHLOTHEIM, 1820). Arrows and M1–M3 indicate megastriae, bryozoan colonies are indicated by B1 and B2. a: GIT 819-1, left lateral view; b: body chamber of GIT 819-1, dorsal view; c: body chamber of GIT 819-1, left lateral view; d: GIT 819-1, right lateral view; e: PIMUZ 37299, right lateral view; f: detail of the body chamber of GIT 819-1, right lateral view, encrusted by bryozoans; g: bryozoan colony with Trypanites borings growing on an older bryozoan crust GIT 819-1. Specimens oriented with aperture downwards. Scale bars 10 mm. in Conch Structures, Soft-Tissue Imprints And Taphonomy Of The Middle Ordovician Cephalopod Tragoceras Falcatum From Estonia
Text-fig. 1. Megastriae and post mortem epicoles on Tragoceras falcatum (SCHLOTHEIM, 1820). Arrows and M1–M3 indicate megastriae, bryozoan colonies are indicated by B1 and B2. a: GIT 819-1, left lateral view; b: body chamber of GIT 819-1, dorsal view; c: body chamber of GIT 819-1, left lateral view; d: GIT 819-1, right lateral view; e: PIMUZ 37299, right lateral view; f: detail of the body chamber of GIT 819-1, right lateral view, encrusted by bryozoans; g: bryozoan colony with Trypanites borings growing on an older bryozoan crust GIT 819-1. Specimens oriented with aperture downwards. Scale bars 10 mm.
Illustration of git commands
<p>Illustration of the most important git commands, created for Open Educational Ressources provided for the Center for Reproducible Science at the University of Zurich.</p> <p> </p>
Castlebanny Wind Farm, Co' Kilkenny: Gittings Nagle Meade bird survey datasets
<p>Bird survey datasets from surveys carried out by the Gittings Nagle Meade (GNM) survey team for the Castlebanny Wind Farm, Co. Kilkenny, Ireland, 2016-2018. For further details about these surveys see: Gittings, T. (2020b).<em> Castlebanny Wind Farm, Co. Kilkenny: Ornithological Desk Review and Survey Report</em>. Included as Appendix 1 of the Ornithology chapter in the Castlebanny Wind Farm Environmental Impact Assessment Report.</p>
452,000,000 public Git commits on GitHub (October 2016)
<p>What's inside</p> <p>part-000xx.lzo - LZO archives with the data (refer to "Format").</p> <p>part-000xx.lzo.index - LZO index files so that the archives are splittable in Hadoop.</p> <p>stats.csv.gz - GZIP-ed CSV file with some repository statistics related to the commits.</p> <p>Format</p> <p>part-000xx - text, one line per repository, every line is JSON with the following scheme:</p> <p>{ "r": "repository name", "c": [{ "h": "git hash", "a": "author's email hash", "t": "date and time commit was created", "m": "commit message" }, ...] }</p> <p>Date and time format is <em>mostly</em> Go language's time.Time.String(), I recommend to use dateutil.parse() to parse it with Python.</p> <p>Commit message contains explicit \r and \n symbols in order to be a single line.</p> <p>stats.csv has 4 columns: repository name, number of commits, number of contributors, average length of the commit messages.</p>
Code Comment Git Diff
<p>Course - SOEN 691<br><br>Updating Method-Level Comments using Generative AI</p> <p>This study explores the feasibility of using GitDiff to automatically update method-level comments with GenAI. For this, we leverage the information contained in GitDiff, i.e., a patch representing the changes between two states of the file, to help infer the modifications made to the code for updating the method-level comments. For our study as depicted by below figure, we evaluate the following two GenAI architectures:</p>
v073.0 scEiaD data and git deposit
<p>v073.0 scEiaD git repo and dataset deposit</p>
Automated Evolution of Feature Logging Statement Levels Using Git Histories and Degree of Interest
<p>Logging—used for system events and security breaches to more informational yet essential aspects of software features—is pervasive. Given the high transactionality of today's software, logging effectiveness can be reduced by information overload. Log levels help alleviate this problem by correlating a priority to logs that can be later filtered. As software evolves, however, levels of logs documenting surrounding feature implementations may also require modification as features once deemed important may have decreased in urgency and vice-versa. We present an automated approach that assists developers in evolving levels of such (feature) logs. The approach, based on mining Git histories and manipulating a degree of interest (DOI) model, transforms source code to revitalize feature log levels based on the "interestingness" of the surrounding code. Built upon JGit and Mylyn, the approach is implemented as an Eclipse IDE plug-in and evaluated on 18 Java projects with ~3 million lines of code and ~4K log statements. Our tool successfully analyzes 99.26% of logging statements, increases log level distributions by ~20%, identifies logs manually modified with a recall of ~80% and a level-direction match rate of ~87%, and increases the focus of logs in bug fix contexts ~83% of the time. Moreover, pull (patch) requests were integrated into large and popular open-source projects. The results indicate that the approach is promising in assisting developers in evolving feature log levels.</p>
On the Detection of Performance Regression Introducing Code Changes: Experience from the Git Project
<p>This is the dataset and source code of the paper: <strong>On the Detection of Performance Regression Introducing Code Changes: Experience from the Git Project</strong></p> <p>The STEP-1-AND-2 folder is for doig things on digitalocean</p> <p>The STEP-3 folder is for running lizard on each commit</p> <p>The STEP-4 folder is for basically everything else</p> <p>"messing-with-understand" is the code i used to run understand on every<br> commit</p> <p>"nn" is the code i used when toying with neural nets instead of perphecy</p>
Hypothetical Git workflows for simple scientific collaboration
<p>These diagrams illustrate three common workflow scenarios of a relatively simple empirical study with a maximum of three authors using Git/Github versioning tools. Their design was informed by Ram's (2013) body of work. The first scenario is the same as the three-author hypothesis proposed by Ram (2013).</p> <p>In the other two hypothetical scenarios, a single author's workflow is examined. In the first, he coordinates and manages changes with the remote repository from the start of the study, and in the second, he only does this when it comes time to publish the research's materials, data, and history of changes.</p> <p>Six .JPG files, in English (_EN) and Brazilian Portuguese (_PT), depict these three potential scenarios. The repository also contains a .PPTX file that can be edited if the reader wants to utilize any of the figures, either as a basis for additional diagrams or to change the figure's colors.</p>
Die "Git Parabel"
<p>A video explaining the basic concepts behind the distributed version control system Git using an analogy</p>
Nifedipine GITS and Amlodipine Besylate on Recovery of Blood Pressure Rhythm and Arterial Stiffness
ClinicalTrials.gov study NCT02940548. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
FOCUS (Nifedipine GITS's Effect on Central Pressure Assessed by Applanation Tonometry)
ClinicalTrials.gov study NCT01071122. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Assessment of GIT Vasculopathy in SSc and Its Multisystem Correlations
ClinicalTrials.gov study NCT07171437. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Single Dose Bioequivalence Study Comparing Nifedipine/Candesartan FDC (Fixed Dose Combination) With Loose Combination of Nifedipine GITS (Gastro-intestinal Therapeutic System) Plus Candesartan and Sin
ClinicalTrials.gov study NCT01227603. IPD Sharing: Not stated. Countries: 1. Publications: 1.
REducing Blood Pressure Variability in Essential Hypertension With RAmipril vErsus Nifedipine GITS Trial
ClinicalTrials.gov study NCT02499822. IPD Sharing: Not stated. Countries: 3. Publications: 11.
Study of Nifedipine GITS and Candesartan Combination Compared to Monotherapy in Patients With Essential Hypertension
ClinicalTrials.gov study NCT01303783. IPD Sharing: Not stated. Countries: 13. Publications: 2.
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.