On a dedicated channel, #dvs-topics-in-data-viz, in the Data Visualization Society Slack, our members discuss questions and issues pertinent to the field of data visualization. Discussion topics rotate every two weeks, and while subjects vary, each one challenges our members to think deeply and holistically about questions that affect the field of data visualization. At the end of each discussion, the moderator recaps some of the insights and observations in a post on Nightingale. You can find all of the other discussions here.
在数据可视化协会Slack的专用频道＃dvs-topics-in-data-viz中，我们的成员讨论了与数据可视化领域相关的问题。 讨论主题每两周轮换一次，尽管主题有所不同，但每个主题都向我们的成员提出挑战，要求他们对影响数据可视化领域的问题进行深入而全面的思考。 在每次讨论结束时，主持人都会在Nightingale上的帖子中总结一些见解和观察。 您可以在这里找到所有其他讨论。
Even if change is the one constant, we’re not comfortable living with uncertainty. A sense of foreboding can cause more pain than experiencing actual pain. In a Harvard Business Review article on crisis management, Geeta Menon and Ellie Kyung cite various research showing this phenomenon, whether it’s the threat of an electric shock or the perceived loss of job security.
即使变化是一个常数，但我们仍不能适应不确定性。 与实际痛苦相比，预感会带来更多的痛苦。 在《哈佛商业评论》中有关危机管理的文章中，Geeta Menon和Ellie Kyung引用了各种研究，证明了这种现象，无论是电击的威胁还是工作保障的丧失。
Perhaps that's why there's a tendency to treat data as a proxy for reality, even when we shouldn't, at least not without further questions. It speaks about our human need for control. Data Visualization Society (DVS) members reflect on 2020, with a nod to our relationship with data and responsibilities as data practitioners.
也许这就是为什么有一种趋势将数据视为现实的代理，即使我们不这样做，至少没有其他问题也是如此。 它谈到了我们人类对控制的需求。 数据可视化协会(DVS)成员回顾了2020年，向我们与数据的关系和作为数据从业者的责任致敬。
The authors of the anthology, Raw Data is an Oxymoron, remind us that data are never truly raw like some natural resources:
这本选集的作者“原始数据是一种矛盾”提醒我们，数据永远不会像某些自然资源那样真正地原始：
“Some elements are privileged by inclusion, while others are denied relevance through exclusion.” — Daniel Rosenberg, Travis D. Williams
“某些元素通过包含而享有特权，而其他元素则通过排除而被拒绝具有相关性。” —丹尼尔·罗森伯格(Tranis D. Williams)
The very act of measuring and collecting data involves interpretation and value judgements. In Data Feminism, Catherine D’Ignazio and Lauren Klein challenge us to think deeper about the power dynamics behind data science. Whose interest is protected? Who gets heard? Who doesn’t?
衡量和收集数据的真正行为涉及解释和价值判断。 在数据女性主义中，凯瑟琳·迪格纳齐奥(Catherine D'Ignazio)和劳伦·克莱因(Lauren Klein)挑战我们对数据科学背后的动力动力学进行更深入的思考。 谁的利益受到保护？ 谁被听到？ 谁没有
Tableau consultant Bridget Cogley urged us to ask ourselves, what voices are being amplified in our field?
Tableau顾问布里奇特·科格利(Bridget Cogley)敦促我们自问，在我们的领域中正在放大哪些声音？
“We, as a profession, show our values in the stories we choose to tell, amplify, and not tell.” — Bridget Cogley
“我们作为一种职业，在我们选择讲，扩大而不是讲的故事中展示我们的价值观。” —布里奇特·科格利
In this light, it's worth reflecting on the relative quiet within the DVS in response to Black Lives Matter and anti-racism protests, as compared to the massive rush to visualize COVID-19 data. A few questions come to mind: Is it a matter of data — knowing what reliable datasets are out there and where to access them? If it’s because the topics make people feel uncomfortable, why is it easier to abstain than to use dataviz to put a spotlight on injustices? Does this point more to the importance of diversifying our membership or the need for greater awareness that data-related practices like data science and visualization are a form of power?
有鉴于此，与急于将COVID-19数据可视化的需求相比，值得一提的是，DVS应对黑生命问题和反种族主义抗议活动时相对安静。 我想到了几个问题：这是数据问题吗？知道那里有什么可靠的数据集以及从何处访问它们？ 如果是因为主题使人们感到不舒服，为什么弃权比使用dataviz突出不公现象要容易得多？ 这是否更多地表明了使我们的会员资格多样化的重要性，还是需要更多地意识到与数据相关的实践(如数据科学和可视化是一种力量)的认识？
There is an enormous role for data visualization practitioners to play. Nightingale Managing Editor Isaac Levy-Rubinett observed that, for better or for worse, 2020 seems to cement data visualization as uniquely suited for the social media age. In some instances, a visualization can go viral in an instant and convey at least as much information as a long-form article.
数据可视化从业人员可以发挥巨大作用。 夜莺杂志执行主编艾萨克·利维·鲁比内特(Isaac Levy-Rubinett)指出，无论好坏，2020年似乎将数据可视化巩固为独特的社交媒体时代。 在某些情况下，可视化可以立即传播并传达至少与长篇文章一样多的信息。
Business Intelligence (BI) consultant Luke Stonehouse and data visualization designer Soti Coker identified the Financial Times' evolving "Coronavirus Tracked" as one of the best visualizations of the year so far. Nightingale Editor-in-Chief Jason Forrest’s vote went to the ubiquitous "flatten the curve" chart. What these visualizations have in common was their ability to clearly introduce important and complex concepts in a compelling and accessible way.
商业智能(BI)顾问卢克·斯通豪斯(Luke Stonehouse)和数据可视化设计师索蒂·科克(Soti Coker)将《金融时报》不断发展的“冠状病毒追踪”确定为迄今为止年度最佳可视化之一。 夜莺杂志主编杰森·福雷斯特(Jason Forrest)的投票投向了无处不在的“拉平曲线”图表。 这些可视化的共同点在于，它们能够以引人入胜且易于访问的方式清晰地介绍重要和复杂的概念。
However, data visualization can be a double-edged sword. With bad charts, dataviz can become a catalyst for misinformation and lies. Soti flagged that it was as if the creators knew the data literacy levels of the public were quite low at the moment so they could take advantage of that.
但是，数据可视化可能是一把双刃剑。 由于图表不好，dataviz可能成为错误信息和谎言的催化剂。 Soti标记为，好像创建者知道当前公众的数据知识水平还很低，因此他们可以利用这一点。
Not all bad dataviz is intentional, of course, but that doesn’t mean the harm will be any lesser. In the case of visualizing COVID-19 data, DVS Operations Director Amanda Makulec pointed out there are real-life implications to inadvertently creating a misleading chart for public release. This is in addition to the risk of getting caught in the polarization tangle, at least in the United States, where even the act of wearing a mask can be a charged, political statement.
当然，并非所有不良dataviz都是有意为之，但这并不意味着危害会更小。 在可视化COVID-19数据的情况下，DVS运营总监Amanda Makulec指出，无意中创建误导性图表以供公众发布具有现实意义。 至少在美国，这还有被两极分化纠缠的风险，在美国，即使戴着口罩的行为也可能是有罪的政治言论。
While data literacy is important, it’s an uphill task to get people to dig into the nuances of data interpretation. Some don’t have the skills or training to do so or feel intimidated by data; others simply don’t care.
尽管数据素养很重要，但要让人深入研究数据解释的细微差别是一项艰巨的任务。 有些人没有技能或训练，或者没有被数据吓倒； 其他人根本不在乎。
Independent data visualization designer Jane Zhang shared her own struggle in tackling the issue of data literacy with family and friends. In her experience, those who didn’t care about literacy continually misunderstood the data or exaggerated what they saw reported in the news. It was not easy to bring up the need for careful evaluation in casual conversation.
独立数据可视化设计师Jane Zhang与家人和朋友分享了自己在解决数据素养问题上的努力。 根据她的经验，那些不在乎识字的人不断误解数据或夸大了他们在新闻中看到的报道。 在随便的谈话中提出仔细评估的需求并不容易。
Ultimately, data literacy is one piece of the puzzle. We have to confront the responsibilities we have as people who work with data. On the brighter side, the Data Visualization Society is more than a water cooler for dataviz enthusiasts to geek out. It is a great place for setting the tone on visualizing data responsibly and showing newcomer practitioners why we as a profession need to be more mindful of the second-order effects of our dataviz creations.
归根结底，数据素养是难题之一。 我们必须面对作为数据处理人员的责任。 从好的方面来说，数据可视化协会不仅仅是供dataviz爱好者参考的水冷却器。 在这里，您可以为负责任地可视化数据定下基调，并向新来的从业人员展示为什么我们作为一个职业需要更加注意dataviz创作的二阶影响，这是一个好地方。
“There have always been pockets of people doing viz for a long time, but the DVS really brings it all together globally, as a great community that isn’t tied to one tool or technology base. “— DVS Member, Nicole Edmonds
“很长一段时间以来，总是有很多人在做可视化，但是DVS确实是一个全球性的组织，它是一个不依赖任何工具或技术基础的强大社区。 “-DVS会员，妮可·埃德蒙兹(Nicole Edmonds)
Be mindful of what you’re choosing to viz. Consider how you can lend your dataviz skills to explore and expose critical, but understated, issues like mental wellness or civic demonstrations (check out Mass Mobilization Project data for instance). For those who still want to viz about high-risk topics like the COVID-19 pandemic and share it openly, Amanda Makulec’s tip is to use datasets from countries with robust testing, those that have effectively “flattened the curve,” and where there are strong national information systems to capture timely, complete, and accurate data. Collaboration with subject matter experts (or at least investing time to learn from them) is critical for developing accurate visualizations of data about complex topics. If you’re unsure, reach out to the DVS community and ask for advice.
请注意要选择的内容。 考虑如何利用您的数据可视化技能来探索和揭示精神健康或公民示威等关键但低估的问题(例如，查看“群众动员项目”数据)。 对于那些仍想观察高风险主题(例如COVID-19大流行)并公开分享的人们，Amanda Makulec的提示是使用经过有效测试的国家/地区的数据集，这些数据集有效地“拉平了曲线”，并且强大的国家信息系统，可以捕获及时，完整和准确的数据。 与主题专家的协作(或至少花费时间向他们学习)对于开发有关复杂主题的数据的精确可视化至关重要。 如果不确定，请与DVS社区联系并寻求建议。
Invite others to question your data and share alternative views. Echoing the experiences of many, Data Science student Ben Xiao shared that he gains a lot when getting feedback from fresh eyes on his visualizations. Catherine D’Ignazio pushes us to take it one step further and “make dissent possible” during the design process. The idea is to include more voices and take a collaborative approach to the interpretation of the data we use. In a similar vein, Bridget Cogley suggested having more discussions on data lineage or how the data came to be so that we can better pinpoint the biases that get introduced along the way and confront them.
邀请其他人质疑您的数据并分享其他观点。 数据科学专业的学生Ben Xiao与许多人的经验相呼应，他分享了从可视化的新鲜眼光中获得反馈时所学到的东西。 Catherine D'Ignazio推动我们进一步发展，并在设计过程中“使异议成为可能” 。 这个想法是要包含更多的声音，并采取协作的方式来解释我们使用的数据。 同样，布里奇特·科格利(Bridget Cogley)建议对数据沿袭或数据如何发展进行更多讨论，以便我们可以更好地指出在此过程中引入的偏见并应对它们。
Learn more about visualizing uncertainty. Most visualization techniques were created with the assumption that the data are free from uncertainty. Yet, this is rarely the case. There are times we need to make explicit the inherent uncertainty in the data so that our viz can be taken in good faith. The good news is that there are a growing number of resources to help with this challenge. For example: Nathan Yau of FlowingData has a cheat-sheet on the options available, while Dr Lace Padilla, Assistant Professor in Cognitive and Information Sciences, and her coauthors recently reviewed best practices based on how the mind processes different types of uncertainty.
了解有关可视化不确定性的更多信息。 大多数可视化技术都是在假设数据没有不确定性的前提下创建的。 但是，这种情况很少发生。 有时我们需要明确数据中固有的不确定性，以便我们能真诚地把握自己的远见。 好消息是，越来越多的资源可以帮助您应对这一挑战。 例如：FlowingData的Nathan Yau拥有可用选项的备忘单，认知和信息科学助理教授Lace Padilla博士及其合著者最近根据思维如何处理不同类型的不确定性来回顾了最佳实践。
Stress-test your viz for possible misinterpretations. Put yourself in the shoes of your audience and consider how much dataviz skill or data literacy is required to understand your work. Consider how you can make the visualization more user-friendly by adding elements like annotation and color cues. It’s even better if you can get opinions from a diverse range of audience. The DVS slack has a dedicated channel #share-critique for receiving feedback, use it!
对您的视力进行压力测试，以免产生误解。 让自己陷入听众的视线中，并考虑了解您的工作需要多少数据分析技能或数据素养。 考虑如何通过添加注释和颜色提示等元素使可视化更加用户友好。 如果您可以从各种各样的受众中获得意见，那就更好了。 DVS备用站具有专用的＃share-critique频道，用于接收反馈，请使用它！
These steps are likely to push us out of our comfort zone. In 2020, we saw many new wonderful initiatives like DVS Office Hour and the Find a Dataviz Buddy system to help upskill the community. We can expand what it means to be proficient in data visualization: recognizing how culture and its associated biases are embedded in the data, knowing when to visualize and when not to, etc.
这些步骤可能会将我们赶出我们的舒适区。 在2020年，我们看到了许多新的精彩计划，例如DVS Office Hour和Find a Dataviz Buddy系统，以帮助提升社区技能。 我们可以扩展精通数据可视化的含义：识别文化及其相关偏见如何嵌入数据中，了解何时可视化以及何时不可视化等等。
It won’t be easy, but cultivating such core non-technical skills are especially critical when we create visualizations outside of our domain expertise or use data on sensitive topics. These same skills will mark the professionalization and maturation of the dataviz profession.
这并非易事，但是当我们在我们的领域专业知识之外创建可视化效果或使用敏感主题的数据时，培养此类核心非技术技能尤为关键。 这些相同的技能将标志着dataviz行业的专业化和成熟。
Many thanks to DVS members who contributed to the discussion that made this article possible: Amanda Makulec, Ben Xiao, Bridget Cogley, Isaac Levy-Rubinett, Jane Zhang, Jason Forrest, Luke Stonehouse, Max Graze, Nicole Edmonds, and Soti Coker.
非常感谢DVS成员，他们为使本文成为可能的讨论做出了贡献：Amanda Makulec，Ben Xiao，Bridget Cogley，Isaac Levy-Rubinett，Jane Zhang，Jason Forrest，Luke Stonehouse，Max Graze，Nicole Edmonds和Soti Coker。
Alexandra Khoo is a policy strategist turned cultural data scientist. As part of the tech team at Synthesis, she builds and layers bespoke datasets to give a fresh take on people’s behaviors, passions, and values. She also enjoys doodling imaginary creatures.
亚历山德拉·邱(Alexandra Khoo)是由策略策略师转变为文化数据科学家。 作为Synthesis技术团队的一部分，她构建并分层了定制的数据集，以使人们的行为，热情和价值观焕然一新。 她还喜欢涂鸦虚构的生物。
翻译自: https://medium.com/nightingale/the-data-are-not-objective-and-neither-are-you-285b15a1b584
