DataKind公益大数据科学项目扩张至印度，都柏林，爱尔兰，新加坡，华盛顿等城市

越来越多来自世界各地的数据科学家希望将他们的技能用于帮助经费有限的非营利组织而不仅仅是帮助企业销售产品。

DataKind，一个致力于在善意的数据科学家和寻求公益活动帮助的团队之间建立联系的非营利组织，今天宣布将超越发源地纽约和根据地英国，将世界上实际数据科学的资本大幅扩张到班加罗尔，印度，都柏林，爱尔兰，新加坡，华盛顿和旧金山。

“这是突破性的一步，”DataKind创始人兼执行董事Jack Porway 在接受VentureBeat的采访时表示。 “现在，让我们创建这些可以推动该公益数据的尝试前行的其他中心。”

今年4月,DataKind召集想要成立地方分会的科学家，来自60多个城市的近一百人有所回应，Porway说。 结果是在三个大洲共有六个分会。

这种增长表明，随着出现更多对产品开发提出建议或在商业环境中分析数据后提出策略的数据科学家，他们大多数想要使用他们那些超越企业目标的技能。

也出现了其他一些促成和管理这种善意工作的项目。

Y Combinator支持的非营利Bays Impact已经开始运行数据科学家与热心公益事业的组织一起工作的团队，几所大学也已经参与进来。美国首席科学家Rayid Ghani创建的芝加哥大学社会公益数据科学团队最近得到了谷歌执行董事长Eric Schmidt的支持 。

甚至现在在互联网上也有一个让有社会意识的数据科学家们展示他们的工作的地方，这就是所谓的公益数据。

但成立于2011年的DataKind具有从其他程序中吸取动力的特点。他在具有统计和计算机视觉背景的Porway后出现，并开始思考如何运用技术修补。

他“看着我朋友说‘嘿,是否有人想以非营利或社会目的尝试攻击或使用我们的数据科学技能？”他说。 他写了一篇以此为主题的博客并引起了广泛的关注，甚至引起白宫的注意，他说。三年后,这个组织已经真正全球化了并且和Amnesty国际组织，格莱珉基金会，纽约联合广场，世界银行一起工作。

DataKind设在旧金山是非常明智的，似乎人均数据科学家要多于比其他任何城市。该城市有大量的数据科学创业，初创公司招聘数据科学家，并提供该领域的培训。

有人可能会认为所有有社会倾向的数据科学家已经和其他社会公益数据科学团队建立联系了，但显然不是这样的。

”特别是在Bay中我们看到很多感兴趣并且有能力的志愿者并不是隶属于其他团体之一，” Netflix数据科学与工程总监、DataKind在旧金山的四个分会领导人之一的Paul Ellwood告诉Venturebeat。 “我们尽可能不互相调拨人员或侵入志愿服务社区。 我认为与不同类型的志愿者可以有做出伟大工作的足够机会，我们不应该担心互相偷窃。”

并且团体也不和非营利组织争夺，Ellwood补充道。

Ellwood和其他旧金山分会领导人——the Hive Think Tank的Pashû Pathina Christensen, Salesforce.com的Katharine Matsumoto以及独立顾问Aman Ahuja—他们不想谈论最终可能会在Bay中进行的项目的类型。他们正在与几个组织谈判，Matsumoto说。但是Ellwood却是知道分会的工作将导致导致什么结果。

“我认为非营利组织通常相对资源匮乏，并且数据科学是一个真正可帮助您认清应朝哪里努力”他说。 “你可以识别比其他机会有更高回报的机会。我认为通过使用这些项目，我们可以帮助非营利性组织将资源用在可以获得最大化影响的地方并识别那些可能浪费资源的领域”。

Ellwood希望分会于其他五国国家的工作可以创造其他地方的非营利性组织可以利用的技术。

“这可能会导致一个资源包，教育性非营利组织可以识别高潜力的学生或者选择你的地区，”他说。 “我们德想法不是仅与Bay 中的个别非营利组织建立点对点联系，而是寻求应用更广泛并真正帮助非营利组织有效地应用这些技术的解决方案”。

英语原文：

DataKind’s do-good data-science projects arrive in 5 more cities

More data scientists around the world are looking to apply their skills to helping budget-strapped nonprofits rather than just help businesses sell product.

DataKind, a nonprofit that’s devoted to connecting well-meaning data scientists with groups seeking pro-bono help, is announcing a substantial expansion today beyond its New York roots and its United Kingdom outpost. It is expanding to Bangalore, India; Dublin, Ireland; Singapore; Washington, and San Francisco, the de-facto data science capital of the world.

“This marks a really big step forward,” Jake Porway, DataKind’s founder and executive director, said in an interview with VentureBeat. “Now let’s create these other hubs that can keep this data-for-good effort moving.”

In April, DataKind put out a call for applications for data scientists who wanted to form local chapters. Almost 100 people from more than 60 cities responded, Porway said. The result: a total of six chapters across three continents.

The growth suggests that as more data scientists emerge to make suggestions about product development or strategy after analyzing data in a business setting, a substantial number of them want to use their know-how for causes that go beyond corporate objectives.

Other programs have emerged to enable and manage that sort of well-meaning work.

Y Combinator-backed nonprofit Bayes Impact has started running fellowships for data scientists to work with civic-minded groups, and several universities have jumped aboard. And Data Science for Social Good, a University of Chicago fellowship former Obama for America chief scientist Rayid Ghani created, recently picked up backing from Google executive chairman Eric Schmidt.

Heck, there’s even a place on the Internet now for socially conscious data scientists to show off their work. It’s called Data for Good.

But DataKind, established in 2011, has the distinction of gaining momentum before these other programs. It arose after Porway, who has a background in statistics and computer vision, started wondering how his technical tinkering could be applied.

He was “just looking to my friends and saying, ‘Hey, does anybody want to, you know, try to hack or use our data science skills with a nonprofit or a social purpose?”‘ he said. He wrote a blog post on the subject that got plenty of attention, even from the White House, he said. Three years later, the organization has become truly global. And the organization has worked with Amnesty International, the Grameen Foundation, the New York City Department of Parks & Recreation, and the World Bank.

It’s especially sensible for DataKind to have a presence specifically in San Francisco, which seems to have more data scientists per capita than any other city. The city boasts plenty of data science startups, startups hiring data scientists, and training programs for the field.

One might think all the data scientists with social inclinations would already have gotten themselves associated with other data-science-for-social-good groups by now. Not so, apparently.

“In the Bay Area in particular, we have seen no shortage of interest in volunteers who are capable and are not already affiliated with one of those other groups,” Paul Ellwood, director of data science and engineering at Netflix and one of four DataKind San Francisco chapter leaders, told VentureBeat. “As much as possible we try and not cannibalize from each other or poach volunteering communities. I think there is enough opportunity for great work to be done with distinct sets of volunteers that we shouldn’t worry about stealing from each other.”

And the groups aren’t fighting over nonprofits, either, Ellwood added.

Ellwood and the other San Francisco chapter leaders — Pashû Pathina Christensen of the Hive Think Tank, Katharine Matsumoto of Salesforce.com, and independent consultant Aman Ahuja — didn’t want to talk about the types of projects they might end up working on in the Bay Area. They’re in talks with several organizations, Matsumoto said.

But Ellwood did have an idea about what the chapter’s work could lead to.

“I think that nonprofits are typically relatively resource-strapped and data science happens to be an area that really helps you identify where to spend your effort,” he said. “You can identify opportunities that are going to have a greater payoff than other opportunities. I do think through the use of these projects, we can help nonprofits get abetter at applying their resources in areas where they can maximize impacts and identify areas that would be wasteful of resources.”

And Ellwood hoped that the chapter’s work — alongside that of the five other ones — might result in technology that nonprofits in many other places could use.

“It may result in a resource kit for how, you know, education nonprofits can identify high-potential students or, you know, pick your area,” he said. “Our thinking is not that we’re going to just have point-to-point contact with individual nonprofits in the Bay Area but rather we’re going to be looking for solutions that apply more broadly and really help a class of nonprofits apply these techniques efficiently.”

本文由北理大数据教育 翻译自infoworld，拒绝任何不标明译者和来源的转载。

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