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About 7,302 papers for “Codebase”
Rokicki, Konrad, Bruns, Christopher M., Goina, Cristian et al. · Janelia Research Repository · 2019
The Janelia Workstation is a neuroscience discovery platform for processing, analysis, annotation, and sharing of large-scale 3d microscopy data. The Workstation supports the FlyLight and MouseLight team projects at Janelia Research Campus. This software is made available under the license and usage agreement found within the codebase. <br>
Cited by 1Open access
Subedi, Nischal · Zenodo (CERN European Organization for Nuclear Research) · 2025
Cited by 0Open access
Chen, Kun, Houpc · Zenodo (CERN European Organization for Nuclear Research) · 2019
A general purpose calculator for Feynman diagrams
Cited by 0Open access
Jagatap, Akshay · Figshare · 2021
Codebase to generate the figures for "Neurally-constrained modeling of human gaze strategies in a change blindness task"https://doi.org/10.1101/663989<br><br>All figures are uploaded in both pdf and tif formats. <br>DA_ChangeImagePairs_1, DA_ChangeImagePairs_2 have the Change-Image pairs used for the change-blindness experiment. The yellow squares indicate the location of change. <br>DA_FixationDuration has the distribution of Fixation Duration for all the subjects pooled across images. <br>DA_SaccadeAmplitude has the distribution of Saccade Amplitude for all the subjects pooled across images. <br>DA_FirstChangeFix has the distribution of Time to First Change Fixation for all the subjects pooled across images. <br>DA_LastChangeFix has the distribution of Time to Detect Change for all the subjects pooled across images.<br><br>Also contains the SI Videos:1. SI Video 1 Holyrood.mp42. SI Video 2 Illiterati.mp4
Cited by 0Open access
Jakob MacDonald, Kyoichi Nakamura · Zenodo (CERN European Organization for Nuclear Research) · 2024
HashimaXR Unreal Engine Codebase The complete Unreal Engine codebase developed for the HashimaXR Project (www.thehashimaxrproject.org), exported as a compressed archive. Contains all project files, 3D assets, environment design, lighting configurations, and developmental builds for an unreleased XR reconstruction of Hashima Island (Gunkanjima), Japan. About the Project HashimaXR was a collaborative extended reality (XR) project developed between 2021 and 2024 that aimed to reconstruct Hashima Island's coal-mining community as it existed in the early 1970s. The project sought to foreground everyday life while creating space for histories that official heritage narratives have marginalized. Although the project achieved sophisticated spatial reconstruction and narrative design, it did not reach public release. Contents This archive includes: - Unreal Engine Project Files – Complete UE project structure, including configuration files and project settings- 3D Environment Assets – Detailed reconstructions of Hashima's residential buildings, including the iconic Building 30, seawalls, stairs, corridors, and public spaces- Building Information Modelling (BIM) Integration – Architectural data contributed by local collaborator Dr Kyoichi Nakamura, based on his 2007–2008 Project D3 CAH research- Lighting and Weather Systems – Atmospheric configurations for different times of day and weather conditions- Soundscape Assets – Audio files and spatial audio configurations- Prototype Builds – Developmental versions used for demonstrations, academic workshops, and stakeholder presentations Technical Foundation The Unreal Engine codebase represents the project team's view of the first fully integrated 3D immersive representation of Hashima's entire surface environment, completed in early summer 2023. The reconstruction was built on foundational Building Information Modelling data originally developed by Nakamura Kyoichi for the NPO Nagasaki Prefectural Science and Industry Promotion Organization's Project D3 CAH (2007–2008), subsequently extended by the HashimaXR team to include industrial facilities on the island's eastern half. Access Restrictions The files associated with this record are available under Restricted access. Requests will be approved only for noncommercial scholarly research and verification purposes, and access is granted on the condition that requesters agree to the HashimaXR Project Archive Restricted Data Use Terms. In brief, recipients must not redistribute the files or post them publicly, must not attempt to re-identify any individual whose personal data has been removed or replaced, and must not upload the materials to external AI systems or third-party processing services. Approved users must store the files on secure systems with access limited to the approved user(s) and must delete all copies within one (1) month of access being granted (or earlier upon completion of the approved purpose or upon depositor request). Scholarly use must cite this record’s DOI and include the following acknowledgment statement: “Restricted-access materials from the HashimaXR Project Archive were consulted under depositor-approved conditions for scholarly verification. The underlying correspondence remains restricted to minimize disclosure and identifiability risks.” Related Archives This deposit is part of the HashimaXR Project Archive:- Game Design Documents (restricted access): https://doi.org/10.5281/zenodo.18021294 Project Team Co-Principal Investigators: Dr Christopher Gerteis (SOAS University of London), Dr Kyoichi Nakamura (一宇一級建築士事務所), and Dr Bill Mihalopoulos (SOAS University of London, Japan Research Centre). Unreal Engine development: Adam Kay and Jakob MacDonald. Core project members included: Gareth Mattey, Dr Hiroko Hashimoto, and Katherine Strachan. Project website: https://www.thehashimaxrproject.org
Cited by 0Open access
Li, Youjia · Figshare · 2024
This repository contains the code for Hybrid-LLM-GNN transfer learning framework to predict materials properties using concatenated embeddings extracted from ALIGNN and BERT models. The code provides the following functions:Generate string representations of provided crystal samples using NLP tools: Robocystallographer and ChemNLPUse a pre-trained BERT/MatBERT langugae model to extract context-aware word embeddings from text descriptions of a given crystal structureConcatenate LLM embeddings with ALIGNN embeddings obtained from feature-extraction-based ALIGNNTLPost-analysis script for model performance analysis and text-based model explanation<br>
Cited by 0Open access
Li, Youjia · Figshare · 2024
This repository contains the code for Hybrid-LLM-GNN transfer learning framework to predict materials properties using concatenated embeddings extracted from ALIGNN and BERT models. The code provides the following functions:Generate string representations of provided crystal samples using NLP tools: Robocystallographer and ChemNLPUse a pre-trained BERT/MatBERT langugae model to extract context-aware word embeddings from text descriptions of a given crystal structureConcatenate LLM embeddings with ALIGNN embeddings obtained from feature-extraction-based ALIGNNTLPost-analysis script for model performance analysis and text-based model explanation<br>
Cited by 0Open access
Jagatap, Akshay · Figshare · 2024
Codebase to generate the figures for "Neurally-constrained modeling of human gaze strategies in a change blindness task"https://doi.org/10.1101/663989<br><br>All figures are uploaded in both pdf and tif formats. <br>DA_ChangeImagePairs_1, DA_ChangeImagePairs_2 have the Change-Image pairs used for the change-blindness experiment. The yellow squares indicate the location of change. <br>DA_FixationDuration has the distribution of Fixation Duration for all the subjects pooled across images. <br>DA_SaccadeAmplitude has the distribution of Saccade Amplitude for all the subjects pooled across images. <br>DA_FirstChangeFix has the distribution of Time to First Change Fixation for all the subjects pooled across images. <br>DA_LastChangeFix has the distribution of Time to Detect Change for all the subjects pooled across images.<br><br>Also contains the SI Videos:1. SI Video 1 Holyrood.mp42. SI Video 2 Illiterati.mp4
Cited by 0Open access
Nicolas Sadoune, Lode Pollet · SciPost Physics Codebases · 2022
We present a novel and open-source implementation of the worm algorithm, which is an algorithm to simulate Bose-Hubbard and sign-positive spin models using a path-integral representation of the partition function. The code can deal with arbitrary lattice structures and assumes spin-exchange terms, or bosonic hopping amplitudes, between nearest-neighbor sites, and local or nearest-neighbor interactions of the density-density type. We explicitly demonstrate the near-linear scaling of the algorithm with respect to the system volume and the inverse temperature and analyze the autocorrelation times in the vicinity of a U(1) <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>U</mml:mi><mml:mo stretchy="false" form="prefix">(</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy="false" form="postfix">)</mml:mo></mml:mrow></mml:math> second order phase transition. The code is written in such a way that extensions to other lattice models as well as closely-related sign-positive models can be done straightforwardly on top of the provided framework.
Cited by 2Open access
Shibaji7 · Zenodo (CERN European Organization for Nuclear Research) · 2020
Data and code repository for Inosopheric Sluggishness project using SuperDARN and Riometer data
Cited by 0Open access
sdmitrovic, Michele Cosi · Zenodo (CERN European Organization for Nuclear Research) · 2025
Releasing this repository to generate DOI for AMT manuscript.
Cited by 0Open access
jsschlosser, sdmitrovic · Zenodo (CERN European Organization for Nuclear Research) · 2025
Publishing ISARA codebase for AMT manuscript.
Cited by 0Open access
Li, Youjia · Figshare · 2024
This repository contains the code for Hybrid-LLM-GNN transfer learning framework to predict materials properties using concatenated embeddings extracted from ALIGNN and BERT models. The code provides the following functions:Generate string representations of provided crystal samples using NLP tools: Robocystallographer and ChemNLPUse a pre-trained BERT/MatBERT langugae model to extract context-aware word embeddings from text descriptions of a given crystal structureConcatenate LLM embeddings with ALIGNN embeddings obtained from feature-extraction-based ALIGNNTLPost-analysis script for model performance analysis and text-based model explanation<br>
Cited by 0Open access
Jagatap, Akshay · Figshare · 2024
Codebase to generate the figures for "Neurally-constrained modeling of human gaze strategies in a change blindness task"https://doi.org/10.1101/663989<br><br>All figures are uploaded in both pdf and tif formats. <br>DA_ChangeImagePairs_1, DA_ChangeImagePairs_2 have the Change-Image pairs used for the change-blindness experiment. The yellow squares indicate the location of change. <br>DA_FixationDuration has the distribution of Fixation Duration for all the subjects pooled across images. <br>DA_SaccadeAmplitude has the distribution of Saccade Amplitude for all the subjects pooled across images. <br>DA_FirstChangeFix has the distribution of Time to First Change Fixation for all the subjects pooled across images. <br>DA_LastChangeFix has the distribution of Time to Detect Change for all the subjects pooled across images.<br><br>Also contains the SI Videos:1. SI Video 1 Holyrood.mp42. SI Video 2 Illiterati.mp4
Cited by 0Open access
Jakob MacDonald, Kyoichi Nakamura · Zenodo (CERN European Organization for Nuclear Research) · 2024
HashimaXR Unreal Engine Codebase The complete Unreal Engine codebase developed for the HashimaXR Project (www.thehashimaxrproject.org), exported as a compressed archive. Contains all project files, 3D assets, environment design, lighting configurations, and developmental builds for an unreleased XR reconstruction of Hashima Island (Gunkanjima), Japan. About the Project HashimaXR was a collaborative extended reality (XR) project developed between 2021 and 2024 that aimed to reconstruct Hashima Island's coal-mining community as it existed in the early 1970s. The project sought to foreground everyday life while creating space for histories that official heritage narratives have marginalized. Although the project achieved sophisticated spatial reconstruction and narrative design, it did not reach public release. Contents This archive includes: - Unreal Engine Project Files – Complete UE project structure, including configuration files and project settings- 3D Environment Assets – Detailed reconstructions of Hashima's residential buildings, including the iconic Building 30, seawalls, stairs, corridors, and public spaces- Building Information Modelling (BIM) Integration – Architectural data contributed by local collaborator Dr Kyoichi Nakamura, based on his 2007–2008 Project D3 CAH research- Lighting and Weather Systems – Atmospheric configurations for different times of day and weather conditions- Soundscape Assets – Audio files and spatial audio configurations- Prototype Builds – Developmental versions used for demonstrations, academic workshops, and stakeholder presentations Technical Foundation The Unreal Engine codebase represents the project team's view of the first fully integrated 3D immersive representation of Hashima's entire surface environment, completed in early summer 2023. The reconstruction was built on foundational Building Information Modelling data originally developed by Nakamura Kyoichi for the NPO Nagasaki Prefectural Science and Industry Promotion Organization's Project D3 CAH (2007–2008), subsequently extended by the HashimaXR team to include industrial facilities on the island's eastern half. Access Restrictions The files associated with this record are available under Restricted access. Requests will be approved only for noncommercial scholarly research and verification purposes, and access is granted on the condition that requesters agree to the HashimaXR Project Archive Restricted Data Use Terms. In brief, recipients must not redistribute the files or post them publicly, must not attempt to re-identify any individual whose personal data has been removed or replaced, and must not upload the materials to external AI systems or third-party processing services. Approved users must store the files on secure systems with access limited to the approved user(s) and must delete all copies within one (1) month of access being granted (or earlier upon completion of the approved purpose or upon depositor request). Scholarly use must cite this record’s DOI and include the following acknowledgment statement: “Restricted-access materials from the HashimaXR Project Archive were consulted under depositor-approved conditions for scholarly verification. The underlying correspondence remains restricted to minimize disclosure and identifiability risks.” Related Archives This deposit is part of the HashimaXR Project Archive:- Game Design Documents (restricted access): https://doi.org/10.5281/zenodo.18021294 Project Team Co-Principal Investigators: Dr Christopher Gerteis (SOAS University of London), Dr Kyoichi Nakamura (一宇一級建築士事務所), and Dr Bill Mihalopoulos (SOAS University of London, Japan Research Centre). Unreal Engine development: Adam Kay and Jakob MacDonald. Core project members included: Gareth Mattey, Dr Hiroko Hashimoto, and Katherine Strachan. Project website: https://www.thehashimaxrproject.org
Cited by 0Open access
Kiss, Börge · Zenodo (CERN European Organization for Nuclear Research) · 2023
The codebase of the first VedaWeb application, an online research platform for resources on the Rigveda
Cited by 0Open access
Ponce, Sebastien · CERN Document Server (European Organization for Nuclear Research) · 2021
<!--HTML-->Parallel and Optimised Scientific Software - lecture 3\n\n- Measuring performance, tools and key indicators\n- Improving memory handling\n- The nightmare of thread safety\n- Code modernization and low level optimizations\n- Data structures for efficient computation in modern C++
Cited by 0
sdmitrovic, Michele Cosi · Zenodo (CERN European Organization for Nuclear Research) · 2025
Releasing this repository to generate DOI for AMT manuscript.
Cited by 0Open access
Micah Allen, Davide Poggiali, Kirstie Whitaker et al. · Figshare · 2018
This is the first release of the RainCloudPlots codebase and tutorials. It accompanies the PeerJ preprint <strong>Raincloud plots: a multi-platform tool for robust data visualization</strong>. Allen M, Poggiali D, Whitaker K, Marshall TR, Kievit R. (2018) Raincloud plots: a multi-platform tool for robust data visualization. PeerJ Preprints 6:e27137v1 https://doi.org/10.7287/peerj.preprints.27137v1
Cited by 12Open access
Jagatap, Akshay · Figshare · 2020
Codebase to generate the figures for "Neurally-constrained modeling of human gaze strategies in a change blindness task"https://doi.org/10.1101/663989<br><br>Also contains the SI Videos:1. SI Video 1 Holyrood.mp42. SI Video 2 Illiterati.mp4
Cited by 0Open access