Five online training modules on citizen science

At the beginning of the year, as part of my work at UCL and together with members of the Extreme Citizen Science group, I launched a new postgraduate level course “Introduction to Citizen Science and Scientific Crowdsourcing“. We have opened it for distance learners on the UCL eXtend platform. As a postgraduate course, it required a high time commitment, since such a course expects the students to invest about 150 hours over a period of 14 weeks, which translate to at least 5 hours a week. It was expected of students to read and prepare for class, follow the material, and do the practical element each week. Therefore, it was not a surprise that some of those that follow the course remotely found it challenging!

The Opening Science For All project (OPENER) provided an opportunity to create a lighter version of the course, which requires less time. Gitte Kragh from Earthwatch led on the selection of the elements from the full course that can be reused to create 5 modules that cover the following topics: an introduction to citizen science generally, focus on environmental citizen science, information technology in citizen science, understanding participant motivation, and evaluation. Each of the modules is designed to take about an hour and to be relevant on its own. Few slides were adjusted and re-recorded, to ensure that they make sense.

Selecting a platform for the course was challenging – after trying several options, which proved complex, we found the Wix is providing a template for a basic course structure. Unfortunately, it doesn’t provide a forum for interaction between learners, but the general framework of providing the modules and sharing the material of the course is fairly good. As always, putting all the information on the website took time and after testing the course internally, by the OPENER team, we have released the course and you can access it here.

As Gitte pointed: “Each module should only take about an hour, including watching a couple of short videos, reading through the suggested publications (with focus on practical publications, reports and articles rather than core academic papers), and trying out the suggested activity. (If you find any bugs, please let us know!)

This course was created as part of the NERC-funded Opening Up Science for All! (OPENER) project @openupsci. We focused on making this short course more accessible to practitioners and less academic in nature.”

We hope that it’s useful!

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Citizen Science & Scientific Crowdsourcing – week 5 – Data quality

This week, in the “Introduction to Citizen Science & Scientific Crowdsourcing“, our focus was on data management, to complete the first part of the course (the second part starts in a week’s time since we have a mid-term “Reading Week” at UCL).

The part that I’ve enjoyed most in developing was the segment that addresses the data quality concerns that are frequently raised about citizen science and geographic crowdsourcing. Here are the slides from this segment, and below them a rationale for the content and detailed notes

I’ve written a lot on this blog about data quality and in many talks that I gave about citizen science and crowdsourced geographic information, the question about data quality is the first one to come up. It is a valid question, and it had led to useful research – for example on OpenStreetMap and I recall the early conversations, 10 years ago, during a journey to the Association for Geographic Information (AGI) conference about the quality and the longevity potential of OSM.

However, when you are being asked the same question again, and again, and again, at some point, you start considering “why am I being asked this question?”. Especially when you know that it’s been over 10 years since it was demonstrated that the quality is beyond “good enough”, and that there are over 50 papers on citizen science quality. So why is the problem so persistent?

Therefore, the purpose of the segment was to explain the concerns about citizen science data quality and their origin, then to explain a core misunderstanding (that the same quality assessment methods that are used in “scarcity” conditions work in “abundance” conditions), and then cover the main approaches to ensure quality (based on my article for the international encyclopedia of geography). The aim is to equip the students with a suitable explanation on why you need to approach citizen science projects differently, and then to inform them of the available methods. Quite a lot for 10 minutes!

So here are the notes from the slides:

[Slide 1] When it comes to citizen science, it is very common to hear suggestions that the data is not good enough and that volunteers cannot collect data at a good quality, because unlike trained researchers, they don’t understand who they are – a perception that we know little about the people that are involved and therefore we don’t know about their ability. There are also perceptions that like Wikipedia, it is all a very loosely coordinate and therefore there are no strict data quality procedures. However, we know that even in the Wikipedia case that when the scientific journal Nature shown over a decade ago (2005) that Wikipedia is resulting with similar quality to Encyclopaedia Britannica, and we will see that OpenStreetMap is producing data of a similar quality to professional services.
In citizen science where sensing and data collection from instruments is included, there are also concerns over the quality of the instruments and their calibration – the ability to compare the results with high-end instruments.
The opening of the Hunter et al. paper (which offers some solutions), summarises the concerned that are raised over data

[Slide 2] Based on conversations with scientists and concerned that are appearing in the literature, there is also a cultural aspect at play which is expressed in many ways – with data quality being used as an outlet to express them. This can be similar to the concerns that were raised in the cult of the amateur (which we’ve seen in week 2 regarding the critique of crowdsourcing) to protect the position of professional scientists and to avoid the need to change practices. There are also special concerns when citizen science is connected to activism, as this seems to “politicise” science or make the data suspicious – we will see next lecture that the story is more complex. Finally, and more kindly, we can also notice that because scientists are used to top-down mechanisms, they find alternative ways of doing data collection and ensuring quality unfamiliar and untested.

[Slide 3] Against this background, it is not surprising to see that checking data quality in citizen science is a popular research topic. Caren Cooper have identified over 50 papers that compare citizen science data with those that were collected by professional – as she points: “To satisfy those who want some nitty gritty about how citizen science projects actually address data quality, here is my medium-length answer, a brief review of the technical aspects of designing and implementing citizen science to ensure the data are fit for intended uses. When it comes to crowd-driven citizen science, it makes sense to assess how those data are handled and used appropriately. Rather than question whether citizen science data quality is low or high, ask whether it is fit or unfit for a given purpose. For example, in studies of species distributions, data on presence-only will fit fewer purposes (like invasive species monitoring) than data on presence and absence, which are more powerful. Designing protocols so that citizen scientists report what they do not see can be challenging which is why some projects place special emphasize on the importance of “zero data.”
It is a misnomer that the quality of each individual data point can be assessed without context. Yet one of the most common way to examine citizen science data quality has been to compare volunteer data to those collected by trained technicians and scientists. Even a few years ago I’d noticed over 50 papers making these types of comparisons and the overwhelming evidence suggested that volunteer data are fine. And in those few instances when volunteer observations did not match those of professionals, that was evidence of poor project design. While these studies can be reassuring, they are not always necessary nor would they ever be sufficient.” (http://blogs.plos.org/citizensci/2016/12/21/quality-and-quantity-with-citizen-science/)

[Slide 4] One way to examine the issue with data quality is to think of the clash between two concepts and systems of thinking on how to address quality issue – we can consider the condition of standard scientific research conditions as ones of scarcity: limited funding, limited number of people with the necessary skills, a limited laboratory space, expensive instruments that need to be used in a very specific way – sometimes unique instruments.
The conditions of citizen science, on the other hand, are of abundance – we have a large number of participants, with multiple skills, but the cost per participant is low, they bring their own instruments, use their own time, and are also distributed in places that we usually don’t get to (backyards, across the country – we talked about it in week 2). Conditions of abundance are different and require different thinking for quality assurance.

[Slide 5] Here some of the differences. Under conditions of scarcity, it is worth investing in long training to ensure that the data collection is as good as possible the first time it is attempted since time is scarce. Also, we would try to maximise the output from each activity that our researcher carried out, and we will put procedures and standards to ensure “once & good” or even “once & best” optimisation. We can also force all the people in the study to use the same equipment and software, as this streamlines the process.
On the other hand, in abundance conditions we need to assume that people are coming with a whole range of skills and that training can be variable – some people will get trained on the activity over a long time, while to start the process we would want people to have light training and join it. We also thinking of activities differently – e.g. conceiving the data collection as micro-tasks. We might also have multiple procedures and even different ways to record information to cater for a different audience. We will also need to expect a whole range of instrumentation, with sometimes limited information about the characteristics of the instruments.
Once we understand the new condition, we can come up with appropriate data collection procedures that ensure data quality that is suitable for this context.

[Slide 6] There are multiple ways of ensuring data quality in citizen science data. Let’s briefly look at each one of these. The first 3 methods were suggested by Mike Goodchild and Lina Li in a paper from 2012.

[Slide 7] The first method for quality assurance is crowdsourcing – the use of multiple people who are carrying out the same work, in fact, doing peer review or replication of the analysis which is desirable across the sciences. As Watson and Floridi argued, using the examine of Zooniverse, the approaches that are being used in crowdsourcing give these methods a stronger claim on accuracy and scientific correct identification because they are comparing multiple observers who work independently.

[Slide 8] The social form of quality assurance is using more and less experienced participants as a way to check the information and ensure that the data is correct. This is fairly common in many areas of biodiversity observations and integrated into iSpot, but also exist in other areas, such as mapping, where some information get moderated (we’ve seen that in Google Local Guides, when a place is deleted).

[Slide 9] The geographical rules are especially relevant to information about mapping and locations. Because we know things about the nature of geography – the most obvious is land and sea in this example – we can use this knowledge to check that the information that is provided makes sense, such as this sample of two bumble bees that are recorded in OPAL in the middle of the sea. While it might be the case that someone seen them while sailing or on some other vessel, we can integrate a rule into our data management system and ask for more details when we get observations in such a location. There are many other such rules – about streams, lakes, slopes and more.

[Slide 10] The ‘domain’ approach is an extension of the geographic one, and in addition to geographical knowledge uses a specific knowledge that is relevant to the domain in which information is collected. For example, in many citizen science projects that involved collecting biological observations, there will be some body of information about species distribution both spatially and temporally. Therefore, a new observation can be tested against this knowledge, again algorithmically, and help in ensuring that new observations are accurate. If we see a monarch butterfly within the marked area, we can assume that it will not harm the dataset even if it was a mistaken identity, while an outlier (temporally, geographically, or in other characteristics) should stand out.

[Slide 11] The ‘instrumental observation’ approach removes some of the subjective aspects of data collection by a human that might make an error, and rely instead on the availability of equipment that the person is using. Because of the increase in availability of accurate-enough equipment, such as the various sensors that are integrated in smartphones, many people keep in their pockets mobile computers with the ability to collect location, direction, imagery and sound. For example, images files that are captured in smartphones include in the file the GPS coordinates and time-stamp, which for a vast majority of people are beyond their ability to manipulate. Thus, the automatic instrumental recording of information provides evidence for the quality and accuracy of the information. This is where the metadata of the information becomes very valuable as it provides the necessary evidence.

[Slide 12] Finally, the ‘process oriented’ approach bring citizen science closer to traditional industrial processes. Under this approach, the participants go through some training before collecting information, and the process of data collection or analysis is highly structured to ensure that the resulting information is of suitable quality. This can include the provision of standardised equipment, online training or instruction sheets and a structured data recording process. For example, volunteers who participate in the US Community Collaborative Rain, Hail & Snow network (CoCoRaHS) receive standardised rain gauge, instructions on how to install it and online resources to learn about data collection and reporting.

[Slide 13]  What is important to be aware of is that methods are not being used alone but in combination. The analysis by Wiggins et al. in 2011 includes a framework that includes 17 different mechanisms for ensuring data quality. It is therefore not surprising that with appropriate design, citizen science projects can provide high-quality data.

 

 

Citizen Science & Scientific Crowdsourcing – week 3 – Participation inequality

One of the aspects that fascinates me about citizen science and crowdsourcing is the nature of participation and in particular participation inequality. As I’ve noted last week, when you look at large scale systems, you expected to see it in them (so Google Local Guides is exhibiting 95:5:0.005 ratio).

I knew that this phenomenon has been observed many times in Massive Online Open Courses (MOOCs) so I expected it to happen in the course. I’m particularly interested in the question of the dynamic aspect of participation inequality: for example, at the point of the beginning of the “introduction to citizen science and scientific crowdsourcing” course, every single person is at exactly the same level of participation – 0. However, within three weeks, we are starting to see the pattern emerges. Here are some of the numbers:

At this point in time, there are 497 people that went through the trouble of accessing UCLeXtend and creating a profile. They are a small group of the people that seen the blog post (about 1,100) or the tweet about it (about 600 likes, retweets or clicking on the link). There are further 400 people that filled in the online form that I set before the course was open and stated their interest in it.

The course is structured as a set of lectures, each of them broken into segments of 10 minutes each, and although the annotated slides are available and it is likely that many people prefer them over listening to a PowerPoint video (it’s better in class!), the rate of viewing of the videos gives an indication of engagement.

Here are our viewing statistics for now:

ICSSC260118Videos

We can start seeing how the sub-tasks (viewing a series of videos) is already creating the inequality – lots of people watch part of the first video, and either give up (maybe switching to the notes) or leaving it to another time. By part 4 of the first lecture, we are already at very few views (the “Lecture 3 Part 2” video is the one that I’ve integrated in the previous blog post).

What is interesting to see is how fast participation inequality emerges within the online course, and notice that there is now a core of about 5-10 people (about 1% to 2%) that are following the course at the same rate as the 9 students who are in the face to face class. I expect people to also follow the course over a longer period of time, so I wouldn’t read too much into the pattern and wait until the end of the course and a bit after it to do a full analysis.

When I was considering setting up the course as a hybrid online/offline, I was expecting this, since the amount of time that is required to follow up the course is nearly 4-5 hours a week – something reasonable for an MSc student during a course, but tough for a distance learner (I have a huge appreciation to these 10 people that are following!).