OSS contribution takes time; I don't think anyone would contest that. Getting familiar with a project, finding out where you can fit into it, reading and responding to issues, testing and submitting patches, writing documentation. All of that requires a good deal of time.

how does misrepresentative information make it to the top of the search result pile—and what is missing in the current culture of software design and programming that got us here?

One quick trick for making it easier for debugging/understanding your threading macros is to put print statements in between some of the steps. The important thing to remember is that all the print functions in clojure return nil so you need to make a little helper function (I like to call mine ?) that prints and then returns the original input, like this: (defn ? [x] (prn x) x)

I don't think passing the entire http client is very idiomatic, but what is quite common is to pass the entire "environment" (aka runtime configuration) that your app needs through every function. In your case if the only variant is the URL then you could just pass the URL as the first parameter to get-data. This might seem cumbersome to someone used to OO programming but in functional programming it's quite standard. You might notice when looking at example code, tutorials, open source libraries etc. that almost all code that reads or writes to databases expects the DB connection information (or an entire db object) as a parameter to every single function. Another thing you often see is an entire "env" map being passed around which has all config in it such as endpoint URLs.

Something that I've found helps greatly with testing is that when you have code with lots of nested function calls you should try to refactor it into a flat, top level pipeline rather than a calling each function from inside its parent function. Luckily in clojure this is really easy to do with macros like -> and friends, and once you start embracing this approach you can enter a whole new world of transducers. What I mean by a top level pipeline is that for example instead of writing code like this: (defn remap-keys [data] ...some logic...) (defn process-data [data] (remap-keys (flatten data))) (defn get-data [] (let [data (http/get *url*)] (process-data data))) you should make each step its own pure function which receives and returns data, and join them all together at the top with a threading macro: (defn fetch-data [url] (http/get url)) (defn process-data [data] (flatten data)) (defn remap-keys [data] ...some logic...) (defn get-data [] (-> *url* fetch-data process-data remap-keys)) You code hasn't really changed, but now each function can be tested completely independently of the others, because each one is a pure data transform with no internal calls to any of your other functions. You can use the repl to run each step one at a time and in doing so also capture some mock data to use for your tests! Additionally you could make an e2e tests pipeline which runs the same code as get-data but just starts with a different URL, however I would not advise doing this in most cases, and prefer to pass it as a parameter (or at least dynamic var) when feasible.

One thing Component taught me was to think of the entire system like an Object. Specifically, there is state that needs to be managed. So I suggest you think about -main as initializing your system state. Your system needs an http client, so initialize it before you do anything else

This means that software that deals with Internet must be actively maintained. If it is not it will become more and more useless in practice over time, however much it remains theoretically correct, not because it has bugs or security holes as such but because the environment it was designed to work in no longer exists and thus the assumptions it was built on are now incorrect.

The Moodle project

Welcome to the DOAP wiki. DOAP is a project to create an XML/RDF vocabulary to describe software projects, and in particular open source projects.

In this singularity-free world, the future would be bleak for programmers. (Imagine having to cope with hundreds of years of legacy software!)

The Free Software Foundation[11][12] and the Open Knowledge Foundation approved CC0 as a recommended license to dedicate content and software to the public domain.

statsmodels11packag

Fully reproducible code is available in the form of jupyter notebooks (https://jupyter.org) with instructions to install all necessary software, download sequence data, assemble it, and run genomic analyses (https://github.com/dereneaton/Canarium-GBS) (DOI 10.5281/zenodo.1273357). For all genomic analyses in this study we used the ipyrad Python API and the ipyrad-analysis toolkit (https://github.com/dereneaton/ipyrad) which provides a set of wrappers around common genomic analysis tools to run highly parallelized analyses using simple Python scripts.

Source code of the model presented here is available on GitHub (https://github.com/lukasgeyrhofer/phagegrowth) (Payne et al., 2018) and its archived version is accessible through Zenodo: https://dx.doi.org/10.5281/zenodo.1038582 and https://github.com/elifesciences-publications/phagegrowth.

doi: 10.5281/zenodo.1038582RRID:SCR_004129Zenodo repository

I'll walk you through everything you need to know about logs, including what is log and how to use logs for data integration, real time processing, and system building.

Acero F., Ackermann M., Ajello M. et al (Fermi-LAT) 2015 arXiv:1501.02003Preprint

The most up-to-date version of the open-source package NPTFit may be found at https://github.com/bsafdi/NPTFit and the latest documentation at http://nptfit.readthedocs.io. In addition, the version used in this paper has been archived at https://zenodo.org/record/380469#.WN_pSFPyvMV.

Foreman-Mackey D., Vousden W., Price-Whelan A. et al 2016 corner.py: corner.py v2.0.0, doi:10.5281/zenodo.53155

Barbary K. 2014 sncosmo Zenodo, 10.5281/zenodo.11938

https://doi.org/10.5281/zenodo.11938

Software: hotpants, PostgreSQL, realbogus (Bloom et al. 2012) scamp (Bertin 2006), sextractor (Bertin & Arnouts 1996), sncosmo (Barbary 2014), swarp (Bertin et al. 2002), matplotlib (Hunter 2007), numpy (Van Der Walt et al. 2011).

Software: Juypter notebook (http://jupyter.org), Jupyterlab (https://github.com/jupyterlab), VOspace (http://www.canfar.net/en/docs/storage), vos (https://pypi.python.org/pypi/vos), VirtualBox (https://www.virtualbox.org), JupyterHub      (https://jupyterhub.readthedocs.io/en/latest/), ipywidgets (https://ipywidgets.readthedocs.io), NuPyCEE (http://nugrid.github.io/NuPyCEE), NuGridSetExplorer (https://github.com/NuGrid/WENDI), hdf5 (https://www.hdfgroup.org), Cyberlaboratories cyberhubs (https://github.com/cyberlaboratories/cyberhubs), Cyberlaboratories astrohubs        (https://github.com/cyberlaboratories/astrohubs), Cyberhubs Docker repository (https://hub.docker.com/u/cyberhubs), Docker (https://www.docker.com), NOAO data lab (http://datalab.noao.edu), ansible (https://www.ansible.com), puppet (https://puppet.com), mesa_h5 (https://github.com/NuGrid/mesa_h5), Python (https://www.python.org), MESA (http://mesa.sourceforge.net), WENDI (http://wendi.nugridstars.org), OpenMP (http://www.openmp.org), MESA-SDK (http://www.astro.wisc.edu/~townsend/static.php?ref=mesasdk), MPI (https://www.open-mpi.org), gfortran (https://gcc.gnu.org/fortran), SuperLU (http://crd-legacy.lbl.gov/~xiaoye/SuperLU), OpenBLAS (http://www.openblas.net), mencoder (http://www.mplayerhq.hu).

You can not only apply it to classes, but also to software components and microservices.

With sentiment analysis software, set for trial use later this semester in a classroom at the University of St. Thomas, in Minnesota, instructors don’t need to ask. Instead, they can glance at their computer screen at a particular point or stretch of time in the session and observe an aggregate of the emotions students are displaying on their faces: happiness, anger, contempt, disgust, fear, neutrality, sadness and surprise.

picking one and sticking to it is far better than the chaos that ensues when everybody does their own thing

Brooks’ Law, which states “adding manpower to a late software project makes it later”

A computer simulation of refugees’ journeys as they flee major conflicts can correctly predict more than 75 percent of their destinations, and may become a vital tool for governments and NGOs to help better allocate humanitarian resources.

DVC makes your data science projects reproducible by automatically building data dependency graph (DAG). Your code and the dependencies could be easily shared by Git, and data - through cloud storage (AWS S3, GCP) in a single DVC environment.

The bug we ran into only affected certain releases of Postgres 9.2 and has been fixed for a long time now. However, we still find it worrisome that this class of bug can happen at all. A new version of Postgres could be released at any time that has a bug of this nature, and because of the way replication works, this issue has the potential to spread into all of the databases in a replication hierarchy.

NVivo software Version 11.3.2. (QSR International)

Singularity containers can be used to package entire scientific workflows, software and libraries, and even data.

Remember, PIMS were a hot market prior to Windows 3.0 - but most products were never ported to Windows because there wasn't enough revenue being made. This was because users would buy the hype, buy the product but weren't commited enough to get past the learning curve and dedicate time to maintaining data in it. So, most PIMs just became shelf-ware. And it wasn't because they weren't powerful or didn't have good interfaces - they did (as Grandview and Agenda easily demonstrate).

In the case of Computer Software (what are referred to in this Policy as Non-Traditional Works as defined in Section B.5) in which the University has invested Extraordinary Resources (defined in Section B.6), the Creator shall own the copyright to such Non-Traditional Work; provided, however, that the University is entitled to share in the revenues generated by licensing copyrighted works created and owned by members of the Northwestern Academic Community when all three of the following conditions are met:

). In the case of Computer Software created by members of the Northwestern University Academic Community in the course of their employment the Creators shall grant to the University a perpetual, non-exclusive, royalty-free right and license to use, perform, display, copy, reproduce, modify and create derivatives of such works for all traditional, customary or reasonable academic or research purposes of the University.

Rice Policy 333 “Patent and Software Policies.”

Copyrighted courseware and/or software that are not associated with traditional works as described above shall fall under and are subject to the Patent Policy

The rights to copyrightable software with an actual or projected market value in excess of $10,000 annually, except software included in computer-mediated courseware, shall be determined pursuant to the Board's Patent and Copyrightable Software12 Policy (II.A.8.b).

Common Astronomy Software Application package

RepeatMasker was developed using TRF version 4.0.4

For RMBlast ( NCBI Blast modified for use with RepeatMasker/RepeatModeler )

analyses using these ego-level measures can be done using statistical packages like SPSS

UCINET is NetDraw, a visualization tool that has advanced graphic properties.

We’ve designed our products to meet essential needs of journalism through effective online communities.

I don’t want the culture of open source to be organized around a legal definition. I want to zoom out and look at the broader ecosystem (of which the legal definition is one, essential node). A friendlier, more accessible term would make it easier to discuss topics like sustainability, collaboration, and people involved. Those aspects don’t need to be included in the official definition, but they still matter.I still like the term “public software” because it allows more people (including those new to, or unfamiliar with, open source, even if they use or benefit from it) to quickly understand what open source software is and how it should be protected. It doesn’t change the legal definition at all; if anything, it enforces it better, because we would want to define and protect public software exactly as we would any other public resource.

Not every committer has the rights to release or make high level decisions, so we can be much more liberal about giving out commit rights. That increases the committer base for code review and bug triage. As a wider range of expertise in the committer pool smaller changes are reviewed and adjusted without the intervention of the more technical contributors, who can spend their time on reviews only they can do.

The rise of agile coincided with the rise of startups using open source projects. At first, startups just incorporated those projects into their own software. But like any cultural exchange, open source began to affect how startups built products, too.

In short we move all of the new operators to a factory. We group all of the objects of similar lifetime into a single factory

By the way, if the point about being global is that a Singleton can be included in any file and used anywhere in a program, that’s true, but only bad if misused. The Singleton pattern is not inherently bad, it’s misuse is bad

If the singleton class is a “utility class” (e.g. FileUtils, StringUtils, etc.), and has no state, then I would say it’s okay. Such a class is not a liar, per se, because there is no unexpected state change or dependency

ccp.chargePurchaseToCard(100,c); since the logic for purchasing will be in the processor anyway, not the card. Cards don’t process anything

You shouldn’t really be doing this anyway – you should have composed them, possibly via IOC.

It would violate it if you stored a reference to context instead of the engine and later on referred tried to get the engine. For example: class Mechanic { Context context; Mechanic(Context context) { this.context = context; } bool CheckEngine() { return this.context.getEngine().checkIfBusted(); } }

Every time I have to write a test I have to create a graph of objects (the haystack) which no one really needs or cares for, and into this haystack I have to carefully place the needles (the real object of interests) so that the code under test can go and look for them. I have to create the Context just so when I construct the Mechanic it can reach in the Context and get what it realy needs, the Engine. But context is never something which is easy to create. Since context is a kitchen sink which knows just about everything else, its constructor is usually scary. So most of the time I can’t even instantiate Mechanic, not to mention run any tests on it. The testing game is over before it even started

His point seems to be that Constructor Injection can be an anti-pattern if applied too much, particularly if a consumer doesn't need a particular dependency in the majority of cases

The law of leaky abstractions means that whenever somebody comes up with a wizzy new code-generation tool that is supposed to make us all ever-so-efficient, you hear a lot of people saying "learn how to do it manually first, then use the wizzy tool to save time."

A famous example of this is that some SQL servers are dramatically faster if you specify "where a=b and b=c and a=c" than if you only specify "where a=b and b=c" even though the result set is the same. You're not supposed to have to care about the procedure, only the specification. But sometimes the abstraction leaks and causes horrible performance

TCP attempts to provide a complete abstraction of an underlying unreliable network, but sometimes, the network leaks through the abstraction and you feel the things that the abstraction can't quite protect you from. This is but one example of what I've dubbed the Law of Leaky Abstractions

Isolate the dependencies Write the test first Draw the boundary Implement the logic

“the free software movement does this.” And again, I have to say: not quite. 

Convivial tools should be accessible — free, even.

The computer programming the child.”

As such, scrum adopts an empirical approach—accepting that the problem cannot be fully understood or defined, focusing instead on maximizing the team's ability to deliver quickly, to respond to emerging requirements and to adapt to evolving technologies and changes in market conditions.

the study of innovation shows that everything hinges on the hard work of taking a promising idea and making it work — technically, legally, financially, culturally, ecologically. Constraints are great enablers of innovation.

But there’s a downside to the hackathon hype, and our research on designing workplace projects for innovation and learning reveals why. Innovation is usually a lurching journey of discovery and problem solving. Innovation is an iterative, often slow-moving process that requires patience and discipline. Hackathons, with their feverish pace, lack of parameters and winner-take-all culture, discourage this process. We could find few examples of hackathons that have directly led to market success.

what if projects were designed to combine a hacking mindset with rigorous examination of the data and experience they glean? This would reward smart failures that reveal new insights and equip leaders with the information needed to rescale, pivot or axe their projects.

Zoomerang

While the display is appealing and easy to read, it is not customizable

Polldaddy for iOS was a great option for this type of assessment. The layout and design are optimized for the iPad’s screen, and survey results are loaded offline. Be-cause an Internet connection is not required to administer a survey, the researcher has more flexibility in location of survey administration.

Google Forms

unlike Adobe DPS, Inkling, or some other would-be competitors…it’s free.

if the group should decide to fork Moodle together

alliance of Moodle service providers that currently collaborate on Moodle-related projects of mutual interest

Yet there is a growing recognition that provisions must also be made for the data-analysis software that supports the conclusions.

The four freedoms don’t limit us as creators — they open possibilities for us as creators and consumers. When you apply them to software, you get Linux, Webkit/Chrome, and WordPress. When you apply them to medicine, you get the Open Genomics Engine, which is accelerating cancer research and bringing us closer to personalized treatment. When you apply them to companies, you get radically geographically distributed, results-based organizations like Automattic. When you apply them to events you get TEDx, Barcamp, and WordCamp. When you apply them to knowledge, you get Wikipedia.

as of December 2013, 21% of websites are powered by WordPress. One-fifth of the web is built with a tool that anyone can use, change, or improve, whenever and however they want (even more when you count other open source projects

B2 was ultimately abandoned by its creator. If I’d been using it under a proprietary license, that would have been the end — for me, and all its other users. But because we had freedoms 2 and 3, Mike Little and I were able to use the software as a foundation

I’ve spent a third of my life building software based on Stallman’s four freedoms, and I’ve been astonished by the results. WordPress wouldn’t be here if it weren’t for those freedoms, and it couldn’t have evolved the way it has. WordPress was based on a program called B2/cafelog that predated it by two years. I was using B2 because it had freedoms 0 and 1

Without feedback, there are three options: I can believe, without evidence, that I am an awesome programmer.  I can believe, without evidence, that I am a terrible programmer and quit to go do something else.  Or finally, I can believe, without evidence, that I am a terrible programmer somehow successfully pretending to be an awesome programmer.

Familiarity with one another’s communication style also helps them respond to each other quickly, and we know from Csikszentmihalyi’s research that immediate feedback is critical to flow.

It’s free. 

Apache Labs is a place for innovation where committers of the foundation can experiment with new ideas.

all-volunteer