Showing posts with label Open Movements. Show all posts
Showing posts with label Open Movements. Show all posts

Friday, February 2, 2018

Open science: Sharing is caring, but is privacy theft? by David Mehler and Kevin Weiner; PLOS Neuro Community Blog, January 31, 2018

Emilie Reas, PLOS Neuro Community Blog; Open science: Sharing is caring, but is privacy theft? by David Mehler and Kevin Weiner

"As we are actively figuring out the balance between transparency and collaboration in research, we thought it was worth reaching out to six of our colleagues who have thought extensively about OS. We hope that additional scientists will weigh in with further insight regarding this balance not only in human brain mapping, but also in other scientific fields.
Specifically, we asked them: What are the main challenges in moving toward Open Science and how can we meet them? Here are their responses:
Change is coming. Before we continue, let’s define some terms for potential readers: Open Science is an umbrella term that can mean different things to different people. Open access research allows everyone to learn from scientific work (particularly that paid for by the tax payer). Open educational resources mean we don’t re-invent the wheel when we teach others about our work. Open source materials are ones that allow you to see inside, and improve, the black box. Open dataallows researchers to verify our work, and conduct analyses that could not be carried out by one group alone.
Open Science also means open to everyone. We can use the power of curious non-experts through Citizen Science projects. The Open Neuroimaging Laboratory was a finalist for the Open Science Prizeand sought to “lower the barriers for researchers, students, and citizen scientists to help scientific discovery”. We can look to other neuroscience projects such as Eye Wire and FoldIt for inspiration in the future.
Finally, Open Science means open for all. Whose voices are not currently represented well in our field of study? Who is not advancing to tenured positions? How do we ensure that researchers in the developing world are able to contribute to our quest to understand the human brain? All of the open practices above facilitate the inclusion of under-represented minorities, but it will require ongoing focus and consideration to create an equitable community. That’s my biggest challenge: addressing my implicit (and explicit) biases to ensure we have bigger, better and more diverse ideas in the future.
I would like to live in a world where helping to advance the boundary of scientific knowledge is rewarded through new findings and by confirming (or not) already published results irrespective of who owns the data.”"

Saturday, March 12, 2016

Analytics key to agencies in big data explosion; FedScoop, 3/10/16

Billy Mitchell, FedScoop; Analytics key to agencies in big data explosion:
Lots of leading edge info and thought-provoking commentary from an impressive array of speakers at FedScoop and Hitachi's 3/10/16 Social Innovation Summit I attended at the Newseum in D.C. Good overview of Summit by FedScoop's Billy Mitchell:
"The federal government has seen an explosion of data at its disposal and has needed powerful analytics tools to put it to use, federal IT officials and industry executives said.
A single statistic drove the bulk of the conversation at Thursday’s Hitachi Data Systems Social Innovation Summit, produced by FedScoop: By 2020, analysts predict there will be more than 30 billion network-connected digital devices globally, all producing unprecedented volumes of data in a concept called the Internet of Things.
“Those devices, whether it be the phones we use, the cars we drive in, the medical devices used to keep us healthy, the buildings we work in, the ships and airplanes that protect our country, they’re all generating data, and it’s a question of how do we take that data and really put it to use?” said Mike Tanner, president and CEO of federal for Hitachi Data Systems...
While that data brings with it endless opportunities, it also complicates things, particularly because humans alone are unable to do much with such massive data sets."