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About 7,302 papers for “Codebase”
Clements, Jody, Goina, Cristian, Otsuna, Hideo et al. · Figshare · 2021
The NeuronBridge web application provides ad-hoc color depth search and browsing of color depth search results for LM and EM data published by the FlyLight and FlyEM projects. This software is made available under the license and usage agreement found within the codebase.
Cited by 7Open access
FALASCHI, Alessandro · IRIS Research product catalog (Sapienza University of Rome) · 2007
OpenCDN aims to hierarchically chain a set of Streaming Servers, for deliver of Live Streaming contents to millions of viewers. Development is based on the Apple Darwin Streaming Server (DSS) and Linux, but porting to (Helix, WM) is possible.
Cited by 0
Sicheng Zhou · Zenodo (CERN European Organization for Nuclear Research) · 2026
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Sicheng Zhou · Zenodo (CERN European Organization for Nuclear Research) · 2026
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Santana-Cruz, Rene Francisco, Moreno, Martin, Aguilar-Torres, Daniel et al. · Figshare · 2025
Version: 20250616-------------------------------------------------------------------------The MATLAB codebase includes:<br> - main.m It runs the full process: from reading signals to classifying devices.<br> - GetSig.m It loads signal files in.txt format and saves them as .mat files in the folder "Signals".<br> - GetFT.m It applies a bandpass filter to each signal and saves the results in the folder "FP".<br> - GetDataBase.m It calculates kurtosis, skewness, and variance for each filtered signal. Different normalization methods for the signals can be choosen.<br> - SVMClassifier.m It trains a Support Vector Machine with the extracted features and evaluates performance.<br> - RandomForestClassifier.m It trains a Random Forest model using the same features and evaluates its accuracy.<br> - GetDir.m / GetCount.m These utility functions that help manage folders and signal naming. Note: Make sure that the work folder must be considered in the path.<br>
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Kourosh Arasteh, Carolyn Fisher, Steven Magana-Zook · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2025
Analysis scripts to analyze Mass spectral datasets for fentaML project publication
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Gerhardt, Kenji, Ruiz-Perez, Carlos, Konstantinidis, Konstantinos et al. · Figshare · 2025
FastAAI is an ultrafast estimator of whole-genome aggregate amino acid identity (AAI). FastAAI utilizes tetramer similarities across matched pairs of marker genes to estimate AAI in a computationally simple manner, achieving orders of magnitude speedup over alignment and phylogenetic approaches with a minimal lack of accuracy.
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Yujia Hu, Bing Ye · Zenodo (CERN European Organization for Nuclear Research) · 2026
Codebase of LabGym2.2.2
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Paul Garvey · Apress eBooks · 2025
This chapter discusses and evaluates the project codebase in terms of the various programming languages utilized to build the core product and the corresponding performance of the development team executing the development process. A project codebase is a complete set of source code files for a software project, including everything needed to build, execute, and maintain an application. In the blockchain world, a project's codebase is typically hosted on platforms like GitHub, a key element that helps development teams and the public collaborate to maximize a project's quality and overall potential.
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Shulman, Eldad, Campagnolo, Emma · Zenodo (CERN European Organization for Nuclear Research) · 2025
Path2Space Codebase Overview This repository contains the codebase for Path2Space, a model designed for predicting spatial transcriptomics (ST) from Hematoxylin and Eosin (H&E) stained slides. Included Content This repository includes: - `scripts/` : a repository containing all necessary source code- `tutorials/` : a repository containing a detailed tutorial to run Path2Space on a small example dataset- `input_data/` : a repository containing the necessary inputs to run the tutorial- `docs/` : a repository with an installation guide for all the required dependencies System Requirements Software Dependencies:The software requires Python (3.10.8) and the following libraries: - NumPy (1.24.4)- Pandas (2.2.2)- scikit-learn (1.5.1)- Matplotlib (3.7.2)- Seaborn (0.13.2)- OpenSlide (1.3.1; for TGGA slides)- OpenCV (4.6.0)- Pillow (9.2.0; for ST slides)- PyTorch (2.4.0+cu121) Operating Systems Tested On:- Linux 4.18.0-425.19.2.el8_7.x86_64 Hardware Requirements:No special hardware is required; the code runs on a standard desktop computer. For large datasets, a GPU is recommended for faster processing.The code was tested on a system with the following specifcations - Processor: x86_64- CPU Cores: 28 (Physical), 56 (Logical)- Total RAM: 251.50 GB- GPU: Tesla K802. Installation Guide Installation Instructions:Follow instructions in `docs/INSTALLATION_GUIDE.md` Estimated Installation Time:Typically, it takes about 10–15 minutes on a standard desktop computer. Demo Instructions to Run Demo:1. Navigate to the `tutorials/` folder.2. Use the provided sample dataset and the provided tutorials to test the feature extraction, regression, and prediction pipelines. - 1.STprediction_example.ipynb- 2.CellTypeModel_example.ipynb- 3.SPAND_example.ipynb Expected Output:- Example outputs and prediction results are stored in the `output_data/` folder. Instructions for Use Running on Your Data:1. Prepare your data following the format provided in the demo dataset (`input_data/` folder).2. Run the relevant scripts as described in the Usage section below. Reproduction Instructions:To reproduce all quantitative results described in the manuscript: 1. Use the full dataset specified in the Data Avalaibility section of the manuscript.2. Follow the same pipeline steps as outlined above. Usage 1.1. Feature Extraction:- Navigate to `1.ST_prediction/1.1.Feature_extraction`.- Run `1main_feature_extraction.py` for extracting features from H&E slide images. 1.2. Regression Tasks:- Navigate to `1.ST_prediction/1.2.Regression`.- Use `1main_regression.py` to train a regression model for ST prediction from H&E image features (output of 1.1). 1.3. Prediction Tasks:- Navigate to `1.ST_prediction/1.3.Prediction`.- Use `Prediction.py` to predict ST from features (output of 1.1) on a new dataset using a previously trained model (output of 1.2). 2.1. Cell Type Fraction Model Training:- Navigate to `2.Cell_type_fraction_model/`.- Use `2.1.main_cell_type_model.py` to train a cell type fraction prediction model from inferred ST values (output of 1.2 or 1.3). 2.2. Cell Type Fraction Prediction:- Navigate to `2.Cell_type_fraction_model/`.- Use `2.2.prediction.py` to predict cell type fractions from inferred ST values (output of 1.2 or 1.3) using a trained model (output of 2.1). 3. SPAND Analysis:- Open `SPAND/SPAND.py` to calculate SPAND for a given slide and a given gene from inferred ST values (output of 1.2 or 1.3). License and Terms of Use This model and its associated code have been filed for a provisional US patent (application no. 63/703,060, United States, 2024) and are permitted solely for non-commercial, academic research purposes. Commercial use, sale, or any form of monetization of the DEPLOY model is strictly prohibited without prior approval. Commercial entities interested in utilizing the model should contact the corresponding authors for authorization.
Cited by 0Open access
Aheer Sravon, Ibrahim Md, Devdyuti Mazumder et al. · Zenodo (CERN European Organization for Nuclear Research) · 2025
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Aheer Sravon, Ibrahim Md, Devdyuti Mazumder et al. · Zenodo (CERN European Organization for Nuclear Research) · 2025
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L'Esperance-Kerckhoff, Casper · Figshare · 2024
Webapp for SEISMICgraph. This software enables users to generate a selection of pre-designed plots based on data from RNA mutational profiling experiments.
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L'Esperance-Kerckhoff, Casper · Figshare · 2024
Webapp for SEISMICgraph. This software enables users to generate a selection of pre-designed plots based on data from RNA mutational profiling experiments.
Cited by 0Open access
Toby Weston · Apress eBooks · 2017
nce you have your newly converted Scala codebase, you'll be working with it for a while. Here are some practicalities you should be aware of and some general tips to help manage your codebase.
Cited by 0
Mooers, Griffin · Zenodo (CERN European Organization for Nuclear Research) · 2023
Version 2.0
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Ireland, Addison, Kunze, John, Rauch, Christopher · Figshare · 2025
This is the code for the MatSci YAMZ Websitehttps://doi.org/10.6084/m9.figshare.30652007https://github.com/metadata-research/yamz<br>
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Peidli, Stefan, Green, Tessa, Gross, Torsten et al. · Zenodo (CERN European Organization for Nuclear Research) · 2023
Frozen codebase for scPerturb git repo https://github.com/sanderlab/scPerturb
Cited by 0Open access
The Bike Shed · Bulletin of Miscellaneous Information (Royal Gardens Kew) · 2024
Stephanie has a delightful and cute Ruby thing to share: Honeybadger, the error monitoring service, has created exceptionalcreatures.com, where they've illustrated and characterized various common Ruby errors into little monsters, and they're adorable. Meanwhile, Joel encourages folks to submit proposals for RailsConf.Together, Stephanie and Joel delve into the nuances of adapting to and working within new codebases, akin to aligning with a shared mental model or vision. They ponder several vital questions that every developer faces when encountering a new project: the balance between exploring a codebase to understand its structure and diving straight into tasks, the decision-making process behind adopting new patterns versus adhering to established ones, and the strategies teams can employ to assist developers who are familiarizing themselves with a new environment.Honeybadger's Exceptional CreaturesRailsConf CFP coaching sessionsHTTP CatsSupport and Maintenance EpisodeTranscript: JOËL: Hello and welcome to another episode of The Bike Shed, a weekly podcast from your friends at thoughtbot about developing great software. I'm Joel Quenneville. STEPHANIE: And I'm Stephanie Minn. And together, we're here to share a bit of what we've learned along the way. JOËL: So, Stephanie, what's new in your world? STEPHANIE: I have a delightful and cute Ruby thing to share I'd seen just in our internal company Slack. Honeybadger, the error monitoring service, has created a cute little webpage called exceptionalcreatures.com, where they've basically illustrated and characterized various common Ruby errors into little monsters [laughs], and I find them adorable. I think their goal is also to make it a really helpful resource for people encountering these kinds of errors, learning about them for the first time, and figuring how to triage or debug them. And I just think it's a really cool way of, like, making it super approachable, debugging and, you know, when you first encounter a scary error message, can be really overwhelming, and then Googling about it can also be equally [chuckles] overwhelming. So, I just really liked the whimsy that they kind of injected into something that could be really hard to learn about. Like, there are so many different error messages in Ruby and in Rails and whatever other libraries you're using. And so, that's kind of a...I think they've created a one-stop shop for, you know, figuring out how to move forward with common errors.And I also like that it's a bit of a collective effort. They're calling it, like, a bestiary for all the little creatures [laughs] that they've discovered. And I think you can, like, submit your own favorite Ruby error and any guidance you might have for someone trying to debug it. JOËL: That's adorable. It reminds me a little bit of HTTP status codes as cat memes site. It has that same energy. One thing that I think is really interesting is that because it's Honeybadger, they have stats on, like, frequency of these errors, and a lot of these ones are tied to...I think they're picking some of the most commonly surfaced errors. STEPHANIE: Yeah, there's little, like, ratings, too, for how frequently they occur, kind of just like, I don't know, Pokemon [laughs] [inaudible 02:31]. I think it's really neat that they're using something like a learning from their business or maybe even some, like, proprietary information and sharing it with the world so that we can learn from it. JOËL: I think one thing that's worth specifying as well is that these are specific exception classes that get raised. So, they're not just, like, random error strings that you see in the wild. They don't often have a whole lot of documentation around them, so it's nice to see a dedicated page for each and a little bit of maybe how this is used in the real world versus maybe how they were designed to be used. Maybe there's a line or two in the docs about, you know, core Ruby when a NoMethodError should be raised. How does NoMethodError actually get used, you know, in real life, and the exceptions that Honeybadger is capturing. That's really interesting to see.STEPHANIE: Yeah, I like how each page for the exception class, and I'm glad you made that distinction, is kind of, like, crowdsourced guidance and information from the community, so I think you could even, you know, contribute to it if you wanted. But yeah, just a fun, little website to bring you some delight when you're on your next head-smacking, debugging adventure [laughs]. JOËL: And I love that it brings some joy to the topic, but, honestly, I think it's a pretty good reference. I could see myself linking to this anytime I want to have a deeper discussion on exceptions. So, maybe there's a code review, and maybe I want to suggest that we raise a different error than the one that we're doing. I could see myself in that GitHub comment being like, "Oh, instead of, you know, raising an exception here, why don't we instead raise a NoMethodError or something like that?" And then link to the bestiary page. STEPHANIE: So, Joel, what's new in your world? JOËL: So, just recently, RailsConf announced their call for proposals. It's a fairly short period this year, only about three-ish weeks long. So, I've been really encouraging colleagues to submit and trying to be a resource for people who are interested in speaking at conferences. We did a Q&A session with a fellow thoughtboter, Aji Slater, who's also a former RailsConf speaker, about what makes for a good talk, what is it like to submit to a call for proposals, you know, kind of everything from the process from having an idea all the way to stage presence and delivering. And there's a lot of great questions that got asked and some good discussion that happened there.STEPHANIE: Nice. Yeah, I think I have noticed that you are doing a lot more to help, especially first-time speakers give their first conference talk this year. And I'm wondering if there's anything you've learned or any hopes and dreams you have for kind of the amount of time you're investing into supporting others. JOËL: What I'd like to see is a lot of people submitting proposals; that's always a great thing. And, a proposal, even if it doesn't get accepted, is a thing that you can resubmit. And so, having gone through the effort of building a proposal and especially getting it maybe peer-reviewed by some colleagues to polish your idea, I think is already just a really great exercise, and it's one that you can shop around. It's one that you can maybe convert into a blog post if you need to. You can convert that into some kind of podcast appearance. So, I think it's a great way to take an idea you're excited about and focus it, even if you can't get into RailsConf.STEPHANIE: I really like that metric for success. It reminds me of a writer friend I have who actually was a guest on the show, Nicole Zhu. She submits a lot of short stories to magazines and applications to writing fellowships, and she celebrates every rejection. I think at the end of the year, she, like, celebrates herself for having received, you know, like, 15 rejections or something that year because that meant that she just went for it and, you know, did the hard part of doing the work, putting yourself out there. And that is just as important, you know, if not more than whatever achievement or goal or the idea of having something accepted.JOËL: Yeah, I have to admit; rejection hurts. It's not a fun thing to go through. But I think even if you sort of make it to that final stage of having written a proposal and it gets rejected, you get a lot of value out of that journey sort of regardless of whether you get accepted or not. So, I encourage more people to do that. To any of our listeners who are interested, the RailsConf call for proposals goes through February 13th, 2024. So, if you are listening before then and are inspired, I recommend submitting. If you're unsure of what makes for a good CFP, RailsConf is currently offering coaching sessions to help craft better proposals. They have one on February 5th, one on February 6th, and one on February 7th, so those are also options to look into if this is maybe your first time and you're not sure. There's a signup form. We'll link to it in the show notes.STEPHANIE: So, another update I have that I'm excited to get into for the rest of the episode is my recent work on our support and maintenance team, which I've talked about on the show before. But for any listeners who don't know, it's a kind of sub-team at thoughtbot that is focused on helping maintain multiple client projects at a time. But, at this point, you know, there's not as much active feature development, but the work is focused on keeping the codebase up to date, making any dependency upgrades, fixing any bugs that come up, and general support. So, clients have a team to kind of address those things as they come up. And when I had last talked about it on the podcast, I was really excited because it was a bit of a different way of working. I felt like it was very novel to be, you know, have a lot of different projects and domains to be getting into. And knowing that I was working on this team, like, short-term and, you know, it may not be me in the future continuing what I might have started during my rotation, I thought it was really interesting to be optimizing towards, like, completion of a task. And that had kind of changed my workflow a bit and my process.JOËL: So, now that you've been doing work on the support and maintenance team for a while and you've kind of maybe gotten more comfortable with it, how are you generally feeling about this idea of sort of jumping into new codebases all the time? STEPHANIE: It is both fun and more challenging than I thought it would be. I tend to actually really enjoy that period of joining a new team or a project and exploring, you know, a codebase and getting up to speed, and that's something that we do a lot as consultant
Cited by 0
Ireland, Addison, Kunze, John, Rauch, Christopher · Figshare · 2025
This is the code for the MatSci YAMZ Websitehttps://doi.org/10.6084/m9.figshare.30652007https://github.com/metadata-research/yamz<br>
Cited by 0Open access