Conceptualizing Slow Curation

Introduction

As individuals working in the data curation field and participants in a collective effort ( the Data Curation Network, DCN ), we have been reflecting on the pressure for speed in our work (let’s be honest, pressure for speed in everything we do). Increased attention to data sharing and curation, prompted by the White House Office of Science & Technology Policy Memorandum on Ensuring Free, Immediate, and Equitable Access to Federally Funded Research (colloquially called the Nelson Memo), the NIH Data Management and Sharing Policy , and decades of open science initiatives, have led to a continued increase in the demand on our time as data curators. While it’s wonderful that academia is starting to embrace data curation, it can be mentally, emotionally, and physically tiring. Curators feel the urgency to jump from dataset to dataset, which requires context switching and technical adjustments. We feel pressure to move expeditiously, even as we wait for responses from researchers, who similarly have demands on their work. Like many of our colleagues around the United States, we have received increased requests for workshops, data management plan reviews, and data curation, on top of researching, writing, presenting, and bearing the burden of constant staff turnover. Recognizing this ingrained societal need to move fast in ourselves, we stopped and asked: what, if anything, could data curators take from the Slow Movement? How do we reject a culture of busyness, innovation, and speed to embrace the necessary time and space for our work? Don’t get us wrong, we really enjoy the work we do, we know it’s important, and we understand the privilege we have to be able to do this work. We do not resent the work; we are struggling with the pace. This column is our initial attempt to grapple with these issues.

There are many intersecting frameworks, critical theories, and ideas that we draw on for this column, not all of which are explicitly named. However, it’s worth noting that this column is the direct result of years of interest in related topics which has led us here. We also would like to note that the three co-authors alone do not have the knowledge or expertise to do a topic of this magnitude and import justice—especially not in 3,000 words. Instead, our goal is to encourage readers to consider and reflect on their own practices and to start a dialogue within themselves and their communities. In this commentary, we ask many questions; some of which don’t have answers. We invite conversation and engagement with these questions in mechanisms that feel comfortable.

Background

The Slow Movement started in the late 1980’s in Italy, when Carlo Petrini founded the Slow Food Movement. This movement emphasizes eating slowly—embracing home cooked meals over fast food, taking the time to prepare and then savor the meals, as well as celebrating changing seasons and produce. ¹ Since then, the Slow Movement has spread to different aspects of daily life: fashion, art, and some technology sectors, among others, have embraced this critical, reflective approach to living, working, and just being. The Slow Movement has inspired people working at cultural heritage institutions, who have set forth the notions of Slow librarianship (Farkas 2021), Slow archiving (Christen and Anderson 2019), and Slow engagement with cultural heritage (e.g., Slow looking ). The Slow Movement has led us to question the innovation obsession common in libraries, and academia more broadly (e.g., Glassman 2017). The Slow Movement has provided the space for us, librarians, archivists, curators, and digital stewards, to consider how and when we can adjust the pace of our work to reflect critically on our work and move intentionally to serve ourselves and our communities.

This is not to suggest that the Slow Movement is not without faults or has equally benefited all individuals. As identified by Brooks-Kieffer (2019), the ability to set one’s own pace and to afford slowing down without risk is expensive, elitist, and exclusive. It’s not within everyone’s power to embrace Slow. Who has the resources to buy locally grown, seasonal foods, and spend hours preparing a meal at home? In the same way that factors like socioeconomic status, race, gender identity and expression, and other intersecting identities impact one’s ability to embrace the Slow Movement, similarly, institutional power and organizational hierarchy impact our ability to set our own pace at work.

Recognizing that this Movement has unequally benefitted different communities, the impact of the Slow Movement is most powerful with collective action. Those with the privilege of Slow have the duty to work towards systemic changes. This work, then, naturally intersects with other systems of oppressions: racism, classism, sexism, ableism, and other forms of discrimination. All these systems directly impact the pace of our work and our ability to choose our own speed.

While we must acknowledge and reflect on the shortcomings of the Slow Movement, we must also ask ourselves: what is the risk of not embracing a Slow mentality? What is the risk not only to our institutions and our collaborators, but also to ourselves and our loved ones? While there are data repositories automating some curation processes, the majority of curation work is done by humans—how do we balance automation and thoughtful, Slow efforts? How can the complex needs of our colleagues benefit from the Slow Movement, especially through collective action?

Data Curation: Definitions and Context

Data curation is the process of reviewing datasets to prepare them for sharing, use, and reuse. This can include normalizing file formats, adding descriptive metadata, assigning the item a persistent identifier for permanent access, and documenting these actions, among other things. While there are many possible curation activities (e.g., Johnston et al. 2016), essentially, curation ensures that datasets are fit for reuse. It’s worth noting that data curation is one component of the larger research data management lifecycle—while curation can, and should, happen throughout a project, most commonly employed is ad-hoc curation at the end of a project when data is being prepared for sharing and preservation.

Well-done curation requires a confluence of factors to align: time, technology, working environment, mindset, disciplinary and format expertise, etc. We aren’t curating for the sake of curating. We are curating for the sake of manifesting the CARE and FAIR principles (Carroll et al. 2020; Wilkenson et al. 2016), and more broadly, to bolster public trust in and reuse of academic research data.

As members of the Data Curation Network, we collaborate with one another to share knowledge and expertise, as well as staff time, to collaboratively curate datasets. At the heart of our work, though, is an emphasis on humanity. We acknowledge that each curator brings with them a set of personal and professional experiences, identities, biases, strengths, and weaknesses. We also recognize and celebrate the fact that each curator is human: we make mistakes, we have limited capacity, and our personal and professional well-being is more important than a perfectly curated dataset. Like technology and machines, people require regular maintenance to not only survive, but also thrive. To that end, we set forth the following recommendations for Slow Curation. We have adapted our CURATE(D) model (Data Curation Network 2022) and identified areas where Slow can be embraced.

Slow Curation

We envision Slow Curation as the ability to take our time when reviewing a dataset: to read the documentation, mull over the metadata, sift through files, identify concerns and solutions to rectify these concerns, and allow space for our thoughts to simmer. This time provides us the chance to reflect on the dataset from the researcher’s perspective as well as future reuses, to see new issues we need to address, and maybe even identify solutions we hadn’t previously considered. Slow Curation is a process, not an end goal.

This space is a grace to us and to our researchers. Slow Curation allows us to be gentler in our recommendations and communications, especially in asynchronous communication in which tone can be interpreted differently. Slow Curation gives us permission to be kinder to ourselves and our depositors and is more likely to foster lasting relationships.

Rejecting a fast curation pace means that we have time and the authority to move strategically. Instead of hammering out an email of recommendations, we can reflect on our recent work. We can create template requests for information that are specific to our researchers and frequent issues they may encounter. Instead of writing the same thing, or repeating similar sentiments, we can create and adapt these templates for ourselves, that reflect our tones, our personalities, focusing on reintegrating the human at the center of curation into our recommendations for researchers.

Slow Curation means being comfortable with “No”. This, perhaps, will be one of the most challenging Slow practices for data curators, who are eager to support and embrace research partners. When saying “No” might be difficult, we need to establish boundaries that protect ourselves and one another—we can say “Yes, but with caveats.” We can remind ourselves that someone’s lack of planning is not our emergency. We must remember the very real power dynamics in which we are often working—roles, race, gender, faculty status, and other facets of identity that intersect in unique ways to build and reify hierarchies and systematic issues (Mirza and Seale 2019; Ross-White 2021). As we noted earlier, saying “No” and embracing Slow is not feasible for everyone due to power dynamics, yet our work is a marathon which we are trying to run with limited resources.

Slow Curation is also self-care. It’s about applying the empathy we so often provide to our colleagues and researchers but can forget to afford ourselves. It’s about making space for the parts of our work that bring us joy. The more we consider what we are giving up when we say “Yes”, the easier it is to say “No” (e.g., Peltzman 2016). Simply put, we can’t do it all. Slow Curation, including saying “No,” is self-care.

We acknowledge that this will be uncomfortable for many people. We can feel guilty about supposedly not working as hard as our colleagues. How many meetings start or end with small talk about how busy we are? Slowing our work is swimming against the stream of capitalism and white supremacy. This can even feel uncomfortable for our programs when we focus on quality over quantity, urgency, individualism, and perfectionism to name a few characteristics of white supremacy that permeate our work (Okun 2022). We will need to look closer at the current importance of statistics as the way repositories demonstrate success and value, and instead leverage qualitative mechanisms to describe impact.

Slowing down our work, including data curation, is a process, a learning opportunity, a chance to take care of ourselves and our communities. This work will take time and will meet resistance.

Slow and the CURATE(D) steps

While much of this piece has focused on theory, we now offer a few practical examples of how we believe Slowness can be incorporated into data curation, within the context of the CURATE(D) steps (Data Curation Network 2022). We’ve identified three steps of this process where Slow Curation could apply—but this is not an exhaustive list.

The first step, C or Check, is to check files and read documentation. The goal of this step is to review all the data files, metadata, and accompanying documentation. The curator will also open many (if not all) of the data files on their local machine. Do the files open as expected? Is specialty software needed? Are all the expected files present and up to date? This step can be a considerable task, especially if the dataset is large or complex. Checking the files, reading the documentation, and reviewing the metadata all requires a keen attention to detail—noticing errors or inconsistencies, noting how things can be improved, just getting a handle on the dataset contents—all of this requires time to be thorough. This step provides a crucial opportunity to also review datasets with a critical lens for human participant concerns, ethical research on animals, reparative descriptive practices, and other ethical considerations that require significant mental capacity. In other words, Slow Curation means giving ourselves the time, space, and permission to really check a dataset, to know its contents, before moving on.

The second step, U or Understand, is to understand the data (or try to). By envisioning themselves as a researcher who wishes to reuse this dataset, the curator tries to comprehend the contents of the data files, the context of them and connections between the data files, metadata, and documentation. Again, this can be a lengthy endeavor. This step will most likely involve the curator needing to learn more about the area of research: conducting background research, locating/installing/playing around with new software, and discussing with colleagues, among other things. As with the Check step, allowing the time and grace to slow down and be thoughtful will be more successful than trying to rush through a dataset.

The D step, Documenting the curation activities, ideally will occur throughout the curation process. This is often done in the Curator Log and is a record of all the actions performed on a dataset by a curator. It’s the essential provenance that will help future re-users better understand not only what the researcher did in compiling the data, but how the dataset was then adjusted. It takes significant time and effort to set up the workflow for capturing documentation, in addition to the work needed to create the documentation describing the dataset. Slow Curation is about accepting the time it takes to complete this process well—knowing that the end result is worth the investment. Data curators often remind researchers to think of their future selves when creating documentation, and we should do the same.

A Call to Action

Many of us are feeling the burden of the COVID-19 pandemic, including mass staff turnover and having to fill the gaps (often permanently) to pick up the slack, and to do more with less. This is not sustainable. We need to quit the Busy culture and do less with less. We need to take care of ourselves first, then our colleagues, and then the researchers, and finally their data. To do this we need to set boundaries for ourselves and with others. A few examples of boundaries could include:

• Advocating for policies and procedures that protect ourselves and one another (e.g., paid leave time, dedicated writing/focus time, etc.).

• Ensuring we offer realistic turnaround times, based on current capacity and staffing levels—a turnaround time of three business days may have been feasible previously, but might need to be adjusted in the current reality.

• Accepting that sometimes researchers will never respond to our requests. This is not a reflection of our work or value, but could be due to a multitude of reasons, all of which are outside of our control.

• Blocking out time in your calendar to do the work , and don’t accept meetings that schedule over your work time. Set this boundary, hold it firm.

• Persuading administrators and supervisors (in conjunction with other curators) that Slow Curation is crucial to our work, so that they can defend their employees when these boundaries are inevitably questioned.

Join us as we work to escape the cult of Busy. Long live the Slow Movement!

References

Brooks-Kieffer, Jamene. 2019. “Structures in Tension: Navigating Fast and Slow in the Neoliberal University.” Midwest Data Librarian Symposium , Chicago, IL, September 30-October 1. http://hdl.handle.net/1808/29834 .

Carroll, Stephanie Russo, Ibrahim Garba, Oscar L. Figueroa-Rodríguez, Jarita Holbrook, Raymond Lovett, Simeon Materechera, Mark Parsons, Kay Raseroka, Desi Rodriguez-Lonebear, Robyn Rowe, et al. 2020. “The CARE Principles for Indigenous Data Governance.” Data Science Journal 19(1): 43. http://doi.org/10.5334/dsj-2020-043 .

Christen, Kimberly, and Jane Anderson. 2019. “Toward Slow Archives.” Archival Science 19(2): 87–116. https://doi.org/10.1007/s10502-019-09307-x .

Data Curation Network. “The DCN CURATE(D) Steps.” Accessed May 31, 2023. https://datacurationnetwork.org/outputs/workflows .

Farkas, Meredith. “What is slow librarianship?” 2021. Information Wants To Be Free (blog) October 18, 2021. https://meredith.wolfwater.com/wordpress/2021/10/18/what-is-slow-librarianship .

Glassman, Julia. 2017. “The Innovation Fetish And Slow Librarianship: What Librarians Can Learn From The Juicero.” In the Library with the Lead Pipe. Last modified October 18, 2017. https://www.inthelibrarywiththeleadpipe.org/2017/the-innovation-fetish-and-slow-librarianship-what-librarians-can-learn-from-the-juicero .

Johnston, Lisa R., Jake Carlson, Cynthia Hudson-Vitale, Heidi Imker, Wendy Kozlowski, Robert Olendorf, and Claire Stewart. 2016. “Definitions of Data Curation Activities used by the Data Curation Network.” Retrieved from the University of Minnesota Digital Conservancy. https://hdl.handle.net/11299/188638 .

Okun, Tema. “White Supremacy Culture Characteristics.” Accessed May 31, 2023. https://www.whitesupremacyculture.info/characteristics.html .

Peltzman, Shira. 2016. “Should You Take on a Project?: A Flowchart.” Last modified April 4, 2016. https://doi.org/10.5281/zenodo.4694788 .

Ross-White, Amanda. 2021. “Search is a verb: systematic review searching as invisible labor.” Journal of the Medical Library Association: JMLA 109(3): 505-506. http://dx.doi.org/10.5195/jmla.2021.1226 .

Seale, Maura, and Rafia Mirza. 2019. “Empty Presence: Library Labor, Prestige, and the MLS.” Library Trends 68(2): 252-268. https://doi.org/10.1353/lib.2019.0038 .

Wilkinson, Mark D., Michel Dumontier, IJsbrand Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, Jam-Willem Boiten, Luiz Bonino da Silva Santos, Philip E. Bourne, et al. 2016. “The FAIR Guiding Principles for scientific data management and stewardship.” Scientific Data 3(160018). https://doi.org/10.1038/sdata.2016.18 .