The rise of MinIO object storage

MinIO presented at SFD21 a couple of weeks back (see videos here). They had a great session, as always with Jonathan and AB leading the charge. We’ve had a couple of GreyBeardsOnStorage podcasts with AB as well (listen and see GreyBeards talk open source S3… and GreyBeards talk Data Persistence …). We first talked with MinIO last year at SFD 19 where AB made a great impression on the bloggers (see videos here)

Their customers run the gamut from startups to F500. AB said that ~58% of the F500 have MinIO installed and over 8% of the F500 have added capacity over the last year. AB said they have a big presence in Finance, e.g., the 10 largest banks run MinIO, also the auto and Space/Defense sectors have adopted their product.

One reason for the later two sectors (auto & space/defense) is the size of MinIO’s binary, 50MB. And my guess for why the rest of those customers have adopted MinIO is because it’s S3 API compatible, it’s open source, and it’s relatively inexpensive.

Object storage trends

Customers running in the cloud have a love-hate relationship with object storage, they love that it scales but hate what it costs. There are numerous on prem object storage alternatives from traditional and non-traditional storage vendors, but most are deployed on appliances.

With appliances, customers have to order, wait for delivery, rack-configure-set up and after maybe weeks to months finally they have object storage on prem. But with MinIO a purely software, open source solution, it can be tried by merely downloading a couple of (Docker) containers and deployed/activated in under an hour..

As mentioned above, MinIO is API compatible with AWS S3 which helps with adoption. Moreover, now that it’s an integral part of VMware (see their new Data Persistence Platform), it can be enabled in seconds on your standard enterprise VMware cluster with Tanzu.

The other trend is that the edge needs storage, and lots of it. The main drivers of massive edge storage requirement are TelCos deploying 5G and auto industry’s self-driving cars. But this is just a start, industrial IoT will be generating reams of sensor log data at the edge, it will need to be stored somewhere. And what better place to store all this data, but on object storage. Furthermore, all this is driving more adoption of object storage, with MinIO picking up the lion’s share of deployments.

In addition, MinIO recently ported their software to run on ARM. AB said this was to support the expanding hobbyist and developers community driving edge innovation.

And then there was Kubernetes. Everyone in the industry (with the possible exception of Google) is surprised by the adoption of K8S. Google essentially gifted ~$1Bs in R&D on how to scale apps to the world of IT, and now any startup, anywhere, can scale with as well as Google can. And scaling is the “killer app” for the SW industry.

But performance isn’t bad either

Jonathan made mention of MinIO performance (see MinIO 24 node disk and MinIo 32 node NVMe SSD reports) benchmarks. Their disk data shows avg read and write performance of 16.3GB/s and 9.4GB/s, respectively and their NVMe SSD average read and write performance of 183.2GB/s and 171.3GB/s, respectively. The disk numbers are very good for object storage, but the SSD numbers are spectacular.

It turns out that modern, cloud native apps don’t need quick access to data as much as high data throughput. Modern apps have moved to a processing data in memory rather than off of storage, which means they move (large) chunks of data to memory and crunch on it there, and then spit it back out to storage This type of operating mode seems to scale better (in the cloud at least) than having a high priced storage system servicing a blizzard of IO requests from everywhere.

Other vendors had offered SSD object storage before but it never took off. But nowadays, with NVMe SSDs, MinIO is seeing starting to see healthcare, finance, and any AI/ML workloads all deploying NVMe SSD object storage. Yes for large storage repositories, (object storage’s traditional strongpoint), ie, 5PB to 100PB, disk can’t be beat but where blistering high throughput, is needed, NVMe SSD object storage is the way to go.

Open source vs. open core

AB mentioned that MinIO business model is 100% open source vs. many other vendors that use open source but whose business model is open core. The distinction is that open core vendors use open source as base functionality and then build proprietary, charged for, software features/functions on top of this.

But open source vendors, like MinIO offer all their functionality under an open source license (Apache SM License V2.0, GNU AGPL v3 Open Source license and other FOSS licenses), but if you want to use it commercially, build products with it embedded inside, or have enterprise class support, one purchases a commercial license.

As presented at SFD21, but their website home page has updated numbers reflected below

The pure open source model has some natural advantages:

  1. It’s a great lead gen solution because anyone, worldwide, 7X24X365 can download the software and start using it, (see Docker Hub or MinIO’s download page
  2. It’s a great hiring pool. Anyone, who has contributed to the MinIO open source is potentially a great technical hire. MinIO stats says they have 685 contributors, 19 in just the last month for MinIO base code (see MinIO’s GitHub repo).
  3. It’s a great development organization. With ~20 commits a weekover the last year, there’s a lot going on to add functionality/fix bugs. But that’s the new world of software development. Given all this activity, release frequencies increase, ~4 releases a month ((see GitHub repo insights above).
  4. It’s a great testing pool with, ~480M Docker Pulls (using a Docker container to run a standard, already configured MinIO server, mc, console, etc.) and ~18K enterprises running their solution, that’s an awful lot of users. With open source a lot of eye’s or contributors make all problems visible, but what’s more typical, from my perspective, is the more users that deploy your product, the more bugs they find.

Indeed, with the VMware’s Data Persistence Platform, Tanzu customers can use MinIO’s object storage at the click of a button (or three).

Of course, open source has downsides too. Anyone can access packages directly (from GitHub repo and elsewhere) and use your software. And of course, they can clone, fork and modify your source code, to add any functionality they want to it. Historically, open source subscription licensing models don’t generate as much revenues as appliance purchases do. And finally, open source, because it’s created by geeks, is typically difficult to deploy, configure, and use.

But can they meet the requirements of an Enterprise world

Because most open source is difficult to use, the enterprise has generally shied away from it. But that’s where there’s been a lot of changes to MinIO.

MinIO always had a “mc” (minio [admin] client) that offered a number of administrative services via an API, programmatically controlled interface. but they have recently come out with a GUI offering, the minIO console, which has a similarly functionality to their mc APU. They demoed the console on their SFD21 sessions (see videos above).

Supporting 18K enterprise users, even if only 8% are using it a lot, can be a challenge, but supporting almost a half a billion docker pulls (even if only 1/4th of these is a complete minIO deployment) can be hell on earth. The surprising thing is that MinIO’s commercial license promises customers direct-to-engineer support.

At their SFD21 sessions, AB stated they were getting ~2.7 new (tickets) problems a day. I assume these are what’s just coming in from commercial licensed users and not the general public (using their open source licensed offerings). AB said their average resolution time for these tickets was under 15 minutes.

Enter SubNet, the MinIO Subscription Network and their secret (not open source?) weapon to scale enterprise class support. Their direct-to-engineer support model involves a much, more collaborative approach to solving customer problems then you typical enterprise support with level 1, 2 & 3 support engineers. They demoed SubNet briefly at SFD21, but it could deserve a much longer discussion/demostration.

What little we saw (at SFD21) was that it looked almost like slack-PM dialog between customer and engineer but with unlimited downloads and realtime interaction.

MinIO also supports a very active Slack discussion group with ~11K users. Here anyone can ask a question and it will get answered by anyone. MinIO’s Slack has 2 channels: (Ggeneral and GitHub for notifications). It seems like MinIO is using Slack as a crowdsourced level 1 support.

But in the long run, to continue to offer “direct-to-engineer” levels of support, may require adding a whole lot more engineers. But AB seems prepared to do just that.


MinIO is an interesting open source S3 API compatible, object storage solution that seems to run just about anywhere, is freely deployable with enterprise class support available (at a price) and has high throughput performance. What’s not to like.

Open source digital assistant

I’ve come by and purchased a number of digital assistants over the last couple of years from both Google and Amazon but not Apple. At first their novelty drove me to take advantage of them to do a number of things. But over time I started to only use them for music playing or jokes. But then I started to hear about some other concerns with the technology.

The problems with today’s vendor based, digital assistants

My and others main concern was their ability to listen into conversations in the home and workplace without being queried. Yes, there are controls on some of them to turn off the mic and thus any recordings. But these are not hardwired switches and as software may or may not work depending on the implementation. As such, there is no guarantee that they won’t still be recording audio feeds even with their mic (supposedly) turned off.

At one point I saw a news article where police had subpoenaed recordings of a digital assistant to use in a criminal case. Now I’m ok with use of this for specific, court approved, criminal cases but what’s to limit its use to such. And not all courts, or governments for that matter, are as protective of personal privacy as some.

Open source digital assistant on the way

But with an open source version of a digital assistant, one where the user had complete programmatical control over its recording and use of audio data is another matter. I suppose this doesn’t necessarily help the technically challenged among us that can’t program our way out of a paper bag but even for those individuals, the fact that an open source version exists to protect privacy, could be construed as something much more secure than a company or vendor’s product.

All that made it very interesting when I saw an article recently about a project put together at Standford on an Open source challenger to popular virtual assistants”.

How to create a open source digital assistant

The main problem facing an open source digital assistant is the need for massive amounts of annotated training request data. This is one of the main reasons that commercial digital assistants often record conversations when not specifically requested.

But Stanford University who is responsible for creating the open source digital assistant above has managed to design and create a “rules based” system to help generate all the training data needed for a virtual assistant.

With all this automatically generated training data they can use it to train a digital assistant’s natural language processing neural network to understand what’s being asked and drive whatever action is being requested.

At the moment the digital assistant (and its conversation generator) has somewhat limited skills, or rather only works in a restricted set of domains such as restaurants, people, movies, books and music. For example, “identify a restaurant near me that has deep dish pizza and is rated greater than 4 on a 5 point scale”, “find me an mystery novel talking that is about magic”, or “who was the 22nd president of the USA”.

But as the digital assistant and its annotated, rules based conversation generator are both open source, anyone can contribute more skills code or add more conversational capabilities. Over time, if there’s enough participation, perhaps even someday perform all of the skills or capabilities of commercial digital assistants.

Introducing Almond and Stanford’s OVAL

Stanford work on this project is out of their OVAL (Open Virtual Assistant Lab). Their open source virtual assistant is called Almond.

Almond’s verbal generator is called Genie and uses compositional technology to generate conversations that are used to train their linguistic user interface (LUInet). Almond also uses ThingTalk a new declaritive program language to process responses to queries and requests. Finally, Almond makes use of Thingpedia, a repository of information about internet services and IoT devices to tell it how to interact with these systems.

Stanford Genie technology

The technology behind Genie is based on using source text statements to create templates that can generate sentences for any domain you wish to have Almond work in. If one is interested in expanding the Almond domains, they can create their own templates using the Genie toolkit.

One essentially provides a small set of input sentences that are converted into templates and used by Genie to understand how to parse all similar sentences. This enables Almond to “understand” what’s being requested of it

The set of input sentences can start small and be augmented or added to over time to handle more diverse or complex queries or requests. Their GitHub toolkit and Genie technology is described more fully in a paper Genie: A generator of natural language symantec parsers for virtual assistant commands

Stanford ThingTalk declarative language

ThingTalk is the programming language used to control what Almond can do for requests and queries. Essentially it’s a multi-part statement about what to do when a request comes along. The main parts in a ThingTalk statement include:

  1. When a particular action is supposed to be triggered.
  2. What service does the request need in order to perform its action.
  3. What action is requested

The “what service does a request need” are based on Open API calls (See ThingPedia below). The “what action is requested” can either be standard Almond actions or invoke other ThingPedia open source API calls, such as create a tweet, post on FB, send email etc.

For example, a ThingTalk statement looks like:

monitor @com.foxnews.get() => @com.slack.send();

Which monitors Fox news for any new news articles and sends them (the link) to your Slack channel.

Stanford Thingpedia

Thingpedia is an open source repository of structured information available on the Web and of API services available on the web. Structured information or data is the information behind calendars, contact databases, article repositories, etc. Any of which can be queried for information and some of which can be updated or have actions performed on them. API services are the way that those queries and actions are performed.

One page of the Thingpedia multi-page summary of services that are offered

The Thingpedia web page shows a number of services that already have Open source APIs defined and registered. For example, things like twitter, facebook, bing search, BBC news, gmail and a host of other services. More are being added all the time and these represent the domains that Almond can be used to act upon.

Some of these domains are more defined that others. But in any case any service that takes the form of an web based API can be added to Thingpedia.

Thingpedia as a standalone open source repository is valuable in and of itself regardless of its use by Almond. But Almond would be impossible without Thingpedia. Thingpedia wants to be the wikipedia of APIs.

Almond, putting it all together

Almond consists of mainly the Almond Agent, Engine and Thingpedia. The Agent is used by the various Almond implementions to parse and understand the request and access the ThinkTalk program statement. Almond Agent uses its LUInet natural language interpreter, interpret that request and to select the ThingTalk program for the request. Once the ThinkTalk program is identified, it uses the various Thingpedia APIs requested by the ThinkTalk statement to generate the proper API calls to the service being requested and generate any output that is requested.

Where can you run Almond

Almond is available currently as a web app, an Android App, a Gnome (Linux) desktop/laptop App, a CLI application or can be run on your Mac or Windows computers. You could of course create your own smart speaker to run Almond or perhaps hack a current smart speaker to do so.

One important consideration is that with the Android app, all your data and credentials are only stored on the phone. And will not go out into the cloud or elsewhere. I didn’t see similar statements about privacy protections for the web app or any of the other deployments. But as Almond is open source, you potentially have much greater control over where your data resides.


What I would really like is a smart speaker app running on a RPi with a microphones and a decent speaker attached, all in the package of a cube or cylinder.

I thought their videos on Almond were pretty cheesy but the technology is very interesting and could potentially make for an interesting competitor of today’s smar

Photo Credit(s):

All photos and graphics from Stanford Almond and OVAL Lab websites.

An Open Source Powered Leg

I read an article a couple of weeks back about an Open Source Bionic Leg, which was reporting on research began as a NSF funded project at the University of Michigan (UoM), with collaboration from Northwestern, University of Texas at Dallas and CMU. UofM has a website that provides everything you need to build your own open source leg (OSL) leg at

The challenge in human prosthetics these days is that all research is done in silos. Much of it is proprietary and only available within corporations but even university research has been hampered by the lack of a standard platform that could be used to develop new components and ideas on.

The real difficulty is defining the control logic (code). The OSL project is intended to resolve this lack of a platform by providing everything a researcher (hobbyist, or amputee) needs to build their own, at home or in the lab.

The website includes a parts lists and STEP files as well as an estimated cost ($28.5K) to build your own powered prosthetic leg. They also have a Excel spread sheet with all the parts listed, including part numbers and links to where they can be ordered (McMaster-Carr, SolidWorks, & Dephy)

They also show how to build a leg with a short youtube video of how to assemble the whole leg as well as details for each subassembly with separate how-to videos for each.

The open source leg makes use of code from FlexSEA (Flexible Scaleable Electronics Architecture) and Dephy. FlexSea was originally developed by Jean-Francois (JF) Duval while he was at MIT for his doctoral thesis. He has since joined Dephy a robotics design firm. The open source leg project uses FlexSea/Dephy code for its servo control mechanisms.

There is a GitHub Python, MatLab and C control library repo with all the code. The open source leg website also includes instructions, scripts and an image file which can be used to build your own RaspberryPi (4) controller for the leg.

The two (ankle and knee) servos are USB connected to the RPi. There are also other sensors such as the joint (servo-motor) encoders and a six axis load sensor I2C connected to the RPi. Each servo has its own 950mAh battery.

On the OSL website’s control page one can see these servos in action (with short youtube segments). They also provide instructions on how to use the open source control library to take the servo mechanisms through their paces.

Although on the OSL website’s control page I didn’t see anything which put the whole leg together to make use of it in a real world application. They did show on the Data page a youtube video with the OSL attached to a person and being used to walk up and down stairs, inclines and walking across a floor.


Seeing as how the OSL website included STEP and PDF files for all the (machined) parts which represent $15.6K of the $28.5K, if one really wanted to do this on the cheap, one could just 3D print these parts in plastic. It would obviously not suffice mechanically for real use, but it could provide a platform for testing and developing control logic. At some point one could upgrade some or all of the plastic 3D printed parts to something more durable for use in human trials.

Another option is to purchase multiple sets of parts. The OSL website also showed price estimates for purchasing two sets of ankle and knee parts. But I’d imagine if one was so inclined, a number of researchers (hobbyists or amputees) could get together and order multiple sets of parts for reduced prices.

It’s also possible, with a lot of work, that the open source leg could be redesigned to support an open source arm-hand mechanism. This is where having 3D printed plastic parts could be extremely useful in helping to redesign the leg into an arm-hand.

Photo Credit(s):

Where should IoT data be processed – part 2

I wrote a post a while back on Where IOT data should be processed – part 1. We will get back to that post in a moment, but recently I read an article (How big data forced the hunt for ET intelligence to evolve) that mentioned after 20 years, they were shutting down SETI@home.

SETI@home was a crowdsourced computational network that took snippets of radio spectrum, sent them to 1000s of home computers to be analyzed during idle computer time, once processed the analysis was sent back to SETI@home. It was one of the first to use a crowdsourced approach to perform data processing. The data was collected at a radio telescope, sent to SETI@home and distributed from there.

6 Factors for IOT data processing

In my post I talked about 6 factors that should help determine where data is processed. Those 6 factors included

  • Data size which is a measure of the amount (GB, TB or PBs) of data that is being generated at an IOT node
  • Data pipe availability, which is all about the networking bandwidth that’s available at the IOT node. If we are talking some sort of low-bandwidth networking access then it probably makes sense to process the data more locally and send only results of processing up the stack.
  • Processing criticality which indicates how important is the processing of the data. If the processing could save a life then maybe it should be done as close as possible to where the data is generated. If the data processing is less critical it could perhaps be done at other nodes in an IOT network
  • Processing time and infrastructure cost which is all about what sort of computational resources are required to perform the processing and how much would it cost. If processing of the data is to undergo multiple passes or requires multi-core CPUs or GPUs, moving data off the IoT node and onto a more comprehensive server to process it, could make sense.
  • Compliance, governance and archive requirements, which discussed the potential need for all data to be available for regulatory audits and as such may need to be available at a central location anyway so why not perform processing there.
  • Data information funnel, which talked about the fact that an IoT network should be configured in layers and that each layer in the stack should probably be responsible for some portion of the data processing needed by the overall system, if nothing more than compressing the information before it is sent elsewhere.

Now that I review the list, the last, Data information funnel, factor really should be a function of the other factors rather than a separate factor.

In that blog post I promised to follow it up with some examples of the logic applied to real world problems. SETI is the first one I’ve seen in the literature

SETI’s IoT processing problem

Closeup front view of one antenna of the Allan Telescope Array, a radio telescope for combined radio astronomy and SETI (Search for Extraterrestrial Intelligence) research being built by the University of California at Berkeley, outside San Francisco. The first phase, consisting of 42 6 meter dish antennas like the one shown here, was completed in 2007. Eventually it will have 350 antennas. This type of antenna is called an offset Gregorian design. The incoming radio waves are reflected by the large parabolic dish onto a secondary concave parabolic reflector in front of the dish, and then into a feed horn. A metal shroud can be seen along the bottom of the secondary reflector which shields the antenna from ground noise. It covers the frequency range from 0.5 to 11.2 GHz.

The SETI researchers found that “The telescopes are now capable of producing so much data that it’s not possible to get that volume of data out to volunteers,” And “The discovery space is in these massive, massive data streams. And it’s just not efficient to distribute many terabits per second out to volunteers all over the world. It’s more efficient for that data processing to happen at the actual observatory.”

So they moved the data processing for the SETI IoT network from being distributed out to home computers throughout the world to being done at the (telescope) source where the data was originally generated.

This decision seems to rely on a couple of the factors above. Namely the pipe availability and data size factors. They had to move processing because no pipes existed to send Tb of data to 1000s of home computers. And finally, the processing time and infrastructure cost has come down so much, that it was just easier to do the processing onsite.

It doesn’t seem like processing criticality or compliance-governance-archive had any bearing on the decision.

So there’s the first example that seems to fit well into our data processing framework.


We ought to be able to come up with a formula that uses all these factors and comes up to with a yes or no as to whether to process the data on the node or not.

Photo Credit(s)

Google Docs as subversive technology

Read an article the other day in TechReview (How Google Docs became the social media of the resistance) about how Google Docs was being used to help coordinate and promote the resistance surrounding the recent Black Lives Matter movement.

The article points out that Google Docs are sharing resources around anti-racism, email templates, bail resources, pro-bono legal assistance, etc. to help inform and coordinate the movements actions and activities.

Social unrest, the killer app for Google Docs

Protests could be the killer app for shared Google Docs. Facebook and other social media sites are better used for documenting the real time interactions during protests, but coordinating, motivating and informing the protests and protestors is better accomplished using Google Docs, a simple web based, document editor and sharing service.

In pre-internet days, I suppose all this would have been done on hand copied, typeset printed, carbon copied or photocopied theses/phamplets/fliers/printouts. For example, Luther’s list of grievances nailed to the cathedral door, Common Sense pamphlet during the USA revolutionary war to countless fliers during the 60’s protests, all these used the technology of the day to promote protest and revolution.

Nowadays all it takes is a shared Google Doc and a Google (drive) account.

Google Docs are everywhere

The high school that one of my kids went to uses Google Docs for sharing and submitting homework assignments.

Google Docs are shareable because they are hosted on Google Drives. Docs is just one component of the Google (G-)suite of web based apps that includes Google Sheets (spreadsheets), Google Slides (presentations) and Google Drives (object storage).

Moreover, any Google Doc, Sheet or Slide file can be shared and edited by anyone. And Google services like Docs, Sheets, and Slides are useable anonymously, Anyone onlin, can make a change to a shareable/editable doc, sheet, or slide and their changes are automatically saved to the google drive file.

Another thing is that any Google Doc can be shared with just a URL. And they can also be made read-only (or uneditable) by their owner at any time. And of course any Google Doc is backed up automatically by Google drive services.

Owners of documents can revert to previous versions of a Doc file. So if someone incorrectly (or maliciously) changes a doc, the originator can revert it back to a prior version.

Why not use a Wiki

I would think a Wiki would be better to use to coordinate, motivate and inform a protest. Once a Wiki is setup and started, it can be much easier to navigate, as easy to update, and can become a central repository of all information about a movement/protest.

But it takes a lot more effort and IT-web knowledge to set up a Wiki. And it has to have it’s own web address.

Another problem with a Wiki, is that it can become a central point which can be more easily attacked or disturbed. And Wiki edit wars are pretty common, so they too are not immune to malicious behavior.

But with 10s to 100s of Google Docs, spread across user a similar number of user Google drives, Google Docs are a much more distributed resource, less prone to single point of attack. And they can be created and edited almost on a whim. And the only thing it takes is a Google log in and Google drive.


Photo copiers were a controlled technology in the old Soviet Union and even today facebook and twitter are restricted in China and other authoritarian states.

But Google Doc’s seems to have become a much more ubiquitous tool and have become the latest technology, to aid, abet and support social resistance.

Photo credit(s):

New science used to combat COVID-19 disease

Read an article last week in Science Magazine (A completely new culture on doing research… ) on how the way science is done to combat disease has changed the last few years.

In the olden days (~3-5 years ago), disease outbreaks would generate a slew of research papers to be written, submitted for publication and there they would sit, until peer-reviewed, after which they might get published for the world to see for the first time. Estimates I’ve seen say that the scientific research publishing process takes anywhere from one month (very fast) to 4-8 months, assuming no major revisions are required.

With the emergence of the Zika virus and recent Ebola outbreaks, more and more biological research papers have become available through pre-print servers. These are web-sites which accept any research before publication (pre-print), posting the research for all to see, comment and understand.

Open science via pre-print

Most of these pre-print servers focus on specific areas of science. For example bioRxiv is a pre-print server focused on Biology and medRxiv is for health sciences. On the other hand, arXiv is a pre-print server for “physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.” These are just a sampling of what’s available today.

In the past, scientific journals would not accept research that had been published before. But this slowly change as well. Now most scientific journals have policies gol pre-print publication and will also publish them if they deem it worthwhile, (see wikipedia article List of academic journals by pre-print policies).

As of today (9 March 2020) ,on biorXiv there are 423 papers with keyword=”coronavirus” and 52 papers with the keyword COVID-19, some of these may be the same. The newest (Substrate specificity profiling of SARS-CoV-2 Mpro protease provides basis for anti-COVID-19 drug design) was published on 3/7/2020. The last sentence in their abstract says “The results of our work provide a structural framework for the design of inhibitors as antiviral agents or diagnostic tests.” The oldest on bioRxiv is dated 23 January 2020. Similarly, there are 326 papers on medRxiv with the keyword “coronavirus”, the newest published 5 March 2020.

Pre-print research is getting out in the open much sooner than ever before. But the downside, is that pre-print papers may have serious mistakes or omissions in them as they are not peer-reviewed. So the cost of rapid openness is the possibility that some research may be outright wrong, badly done, or lead researchers down blind alleys.

However, the upside is any bad research can be vetted sooner, if it’s open to the world. We see similar problems with open source software, some of it can be buggy or outright failure prone. But having it be open, and if it’s popular, many people will see the problems (or bugs) and fixes will be rapidly created to solve them. With pre-print research, the comment list associated with a pre-print can be long and often will identify problems in the research.

Open science through open journals

In addition to pre-print servers , we are also starting to see the increasing use of open scientific journals such as PLOS to publish formal research.

PLOS has a number of open journals focused on specific arenas of research, such as PLOS Biology, PLOS Pathogyns, PLOS Medicine, etc.

Researchers or their institutions have to pay a nominal fee to publish in PLOS. But all PLOS publications are fully expert, peer-reviewed. But unlike research from say Nature, IEEE or other scientific journals, PLOS papers are free to anyone, and are widely available. (However, I just saw that SpringerNature is making all their coronavirus research free).

Open science via open data(sets)

Another aspect of scientific research that has undergone change of late is the sharing and publication of data used in the research.

Nature has a list of recommended data repositories. All these data repositories seem to be hosted by FAIRsharing at the University of Oxford and run by their Data Readiness Group. They list 1349 databases of which the vast majority (1250) are for the natural sciences with over 1380 standards used for data to be registered with FAIRsharing.

We’ve discussed similar data repositories in the past (please see Data banks, data deposits and data withdrawals, UK BioBank, Big open data leads to citizen science, etc). Having a place to store data used in research papers makes it easier to understand and replicate science.

Collaboration software

The other change to research activities is the use of collaborative software such as Slack. Researchers at UW Madison were already using Slack to collaborate on research but when Coronavirus went public, they Slack could help here too. So they created a group (or channel) under their Slack site called “Wu-han Clan” and invited 69 researchers from around the world. The day after they created it they held their first teleconference.

Other collaboration software exists today but Slack seems most popular. We use Slack for communications in our robotics club, blogging group, a couple of companies we work with, etc. Each has a number of invite-only channels, where channel members can post text, (data) files, links and just about anything else of interest to the channel.

Although I have not been invited to participate in Wu-han Clan (yet), I assume they usee Slack to discuss and vet (pre-print) research, discuss research needs, and other ways to avert the pandemic.


So there you have it. Coronavirus scientific research is happening at warp speed compared to diseases of yore. Technologies to support this sped up research have all emerged over the last five to 10 years but are now being put to use more than ever before. Such technological advancement should lead to faster diagnosis, lower worldwide infection/mortality rates and a quicker medical solution.

Photo Credit(s):

Crowdresearch, crowdsourced academic research

Read an article in Stanford Research, Crowdsourced research gives experience to global participants that discussed an activity in Stanford and other top tier research institutions to try to get global participation in academic research. The process is discussed more fully in a scientific paper (PDF here) by researchers from Stanford, MIT Media Lab, Cornell Tech and UC Santa Cruz.

They chose three projects:

  • A HCI (human computer interaction) project to design, engineer and build a new paid crowd sourcing marketplace (like Amazon’s Mechanical Turk).
  • A visual image recognition project to improve on current visual classification techniques/algorithms.
  • A data science project to design and build the world’s largest wisdom of the crowds experiment.

Why crowdsource academic research?

The intent of crowdsourced research is to provide top tier academic research experience to persons which have no access to top research organizations.

Participating universities obtain more technically diverse researchers, larger research teams, larger research projects, and a geographically dispersed research community.

Collaborators win valuable academic research experience, research community contacts, and potential authorship of research papers as well as potential recommendation letters (for future work or academic placement),

How does crowdresearch work?

It’s almost an open source and agile development applied to academic research. The work week starts with the principal investigator (PI) and research assistants (RAs) going over last week’s milestone deliveries to see which to pursue further next week. The crowdresearch uses a REDDIT like posting and up/down voting to determine which milestone deliverables are most important. The PI and RAs review this prioritized list to select a few to continue to investigate over the next week.

The PI holds an hour long video conference (using Google Hangouts On Air Youtube live stream service). On the conference call all collaborators can view the stream but only a select few are on camera. The PI and the researchers responsible for the important milestone research of the past week discuss their findings and the rest of the collaborators on the team can participate over Slack. The video conference is archived and available  to be watched offline.

At the end of the meeting, the PI identifies next weeks milestones and potentially directly responsible investigators (DRIs) to work on them.

The DRIs and other collaborators choose how to apportion the work for the next week and work commences. Collaboration can be fostered and monitored via Slack and if necessary, more Google live stream meetings.

If collaborators need help understanding some technology, technique, or too, the PI, RAs or DRIs can provide a mini video course on the topic or can point to other information used to get the researchers up to speed. Collaborators can ask questions and receive answers through Slack.

When it’s time to write the paper, they used Google Docs with change tracking to manage the writing process.

The team also maintained a Wiki on the overall project to help new and current members get up to speed on what’s going on. The Wiki would also list the week’s milestones, video archives, project history/information, milestone deliverables, etc.

At the end of the week, researchers and DRIs would supply a mini post to describe their work and link to their milestone deliverables so that everyone could review their results.

Who gets credit for crowdresearch?

Each week, everyone on the project is allocated 100 credits and apportions these credits to other participants the weeks activities. The credits are  used to drive a page-rank credit assignment algorithm to determine an aggregate credit score for each researcher on the project.

Check out the paper linked above for more information on the credit algorithm. They tried to defeat (credit) link rings and other obvious approaches to stealing credit.

At the end of the project, the PI, DRIs and RAs determine a credit clip level for paper authorship. Paper authors are listed in credit order and the remaining, non-author collaborators are listed in an acknowledgements section of the paper.

The PIs can also use the credit level to determine how much of a recommendation letter to provide for researchers

Tools for crowdresearch

The tools needed to collaborate on crowdresearch are cheap and readily available to anyone.

  • Google Docs, Hangouts, Gmail are all freely available, although you may need to purchase more Drive space to host the work on the project.
  • Wiki software is freely available as well from multiple sources including Wikipedia (MediaWiki).
  • Slack is readily available for a low cost, but other open source alternatives exist, if that’s a problem.
  • Github code repository is also readily available for a reasonable cost but  there may be alternatives that use Google Drive storage for the repo.
  • Web hosting is needed to host the online Wiki, media and other assets.

Initial projects were chosen in computer science, so outside of the above tools, they could depend on open source. Other projects will need to consider how much experimental apparatus, how to fund these apparatus purchases, and how a global researchers can best make use of these.

My crowdresearch projects

Some potential commercial crowdresearch projects where we could use aggregate credit score and perhaps other measures of participation to apportion revenue, if any.

  • NVMe storage system using a light weight storage server supporting NVMe over fabric access to hybrid NVMe SSD – capacity disk storage.
  • Proof of Stake (PoS) Ethereum pooling software using Linux servers to create a pool for PoS ETH mining.
  • Bipedal, dual armed, dual handed, five-fingered assisted care robot to supply assistance and care to elders and disabled people throughout the world.

Non-commercial projects, where we would use aggregate credit score to apportion attribution and any potential remuneration.

  • A fully (100%?) mechanical rover able to survive, rove around, perform  scientific analysis, receive/transmit data and possibly, effect repairs from within extreme environments such as the surface of Venus, Jupiter and Chernoble/Fukishima Daiichi reactor cores.
  • Zero propellent interplanetary tug able to rapidly transport rovers, satellites, probes, etc. to any place within the solar system and deploy theme properly.
  • A Venusian manned base habitat including the design, build process and ongoing support for the initial habitat and any expansion over time, such that the habitat can last 25 years.

Any collaborators across the world, interested in collaborating on any of these projects, do let me know, here via comments. Please supply some way to contact you and any skills you’re interested in developing or already have that can help the project(s).

I would be glad to take on PI role for the most popular project(s), if I get sufficient response (no idea what this would be). And  I’d be happy to purchase the Drive, GitHub, Slack and web hosting accounts needed to startup and continue to fruition the most popular project(s). And if there’s any, more domain experienced PIs interested in taking any of these projects do let me know.  


Picture Credit(s): Crowd by Espen Sundve;

Videoblogger Video Conference by Markus Sandy;

Researchers Night 2014 by Department of Computer Science, NTNU;