Celebrating Nurses’ Ways of Knowing

In honor of National Nurses Week, this nurse—now library director—is wondering how NLM, the world’s largest repository of biomedical knowledge, serves nurses and nursing.

Certainly, the Library—with its rich collections and extensive services—addresses the scientific, scholarly practice of nursing well, but what about the other dimensions of nurses’ ways of knowing?

In 1975, for her doctoral dissertation, Barbara Carper explored the published writings of nurses and works about nursing and found through her analysis a structure or typology to the practice of nursing. She labeled this typology “patterns of knowing” and proposed that the following four patterns work together to inform how nurses know patients and how to care for them:

  • Empirics, the science of nursing
  • Esthetics, the art of nursing
  • Personal, the therapeutic use of self
  • Ethics, the moral reasoning base of nursing

The empirical foundations of nursing arise from systematic inquiry, whether experimental, naturalistic, or observational. Nurses “know” about human response through controlled studies, though science, and as the emphasis on empirical foundations grew through the 20th century, nursing embraced the ideals of evidence-based practice.

But Carper’s influential work affirmed that the real practice of nursing went beyond science and, in fact, is significantly shaped by the other three patterns of knowing.

Esthetics as a means of knowing in nursing is part perception, part empathy, and part action. As the “art” in the practice of nursing, it involves paying attention to a patient’s health concerns and behaviors, along with scattered, relevant details and intangibles, and integrating them into a holistic understanding of the person and what she needs. It provides the creative spark that leads a nurse to know both what to do and how to get it done—that is, how to approach a patient and address her therapeutic needs.

Personal knowing reflects the engagement between nurse and patient. It demands that a nurse know himself so that he can approach the patient as a person and form an authentic relationship. Then, through that relationship, the nurse can apply scientific knowledge to help.

Ethical knowing focuses on “matters of obligation or what ought to be done.” Lying at the foundation of action, ethics requires judgment about what to do and what not to do. It arises as a complex consequence of learning, deliberation, and engagement with the standards, codes, and values of the profession and society.

As Carper noted, these four kinds of knowing “provide the discipline with its particular perspectives and significance.” As such, all four are important to the practice of nursing—and by extension, to the work of the National Library of Medicine.

NLM counts among its collections many of nursing’s important foundational, theoretical, and empirical articles and books. We preserve monographs that explore the nurse-patient relationship and that provide guidance for integrating the various ways of knowing others into clinical interventions. Our History of Medicine Division holds photographs and drawings that depict the healing dialogue between patients and nurses. And we have materials that reflect on the ethical premises for care.

Where I think we fall short is in the realm of personal knowing.

Perhaps NLM privileges what is shared and publicly validated, such as scientific articles, over nurses’ personal stories of knowing self and knowing others. It’s also possible that the language for documenting the personal knowing patterns of nursing doesn’t quite convey its essence. Maybe the personal knowing of nursing is indeed ephemeral and dissipates even if it is captured.

That said, I want NLM to support all of nursing’s patterns of knowing—to include descriptions of how one observes, studies, and verifies these patterns; to present the results of these patterns so the human experience is fully depicted; and to document the clinical impact of fully knowing a person.

I don’t quite know what that support might look like. We might find that words are not enough, and the best way NLM can support nursing’s patterns of knowing awaits future discoveries.

That’s why I invite you to come along with me to make sure this important perspective on the knowledge of health remains present and vibrant among our holdings. We owe it to nursing, and we owe it to ourselves.

What makes a data commons work?

The data commons is emerging as a key component to support data science and data-driven research.

The term “data commons” can refer to both the technological platform for storing and manipulating shareable data sets and the set of principles, governance strategies, and utilities that make use of those data sets possible.

Over the last five years, the scientific community has grown to embrace the data commons idea much faster than we’ve been able to agree on how to set up, govern, and fund these data commons. As a result, we’re seeing confusion and duplication in certain quarters. For example, we already have several data commons across NIH, including the Cancer Genome Data Commons, and the National Heart Lung and Blood Institute’s data commons, along with several others from the NIH Data Commons Pilot Program.

As you can imagine, many questions remain unresolved. Should there be one gigantic data commons, encompassing all the data in the world? (No.) Should there be country-specific data commons to help ensure they are established with access, use, and storage principles consistent with each country’s laws and regulations? Should funding authorities have the authority to dictate a common location for storing the data? If so, will these authorities then pay for storage in perpetuity? How long should we retain data? And who decides?

Obviously, we’re not going to solve these issues—and the myriad others associated with data storage and access—in a single blog post. But I do want to affirm the National Library of Medicine’s support for the emerging NIH Strategic Plan for Data Science (PDF). NLM is ready to contribute its experience collecting and managing scientific data and literature to the ongoing discussions of how a data commons could be shaped to best support biomedical discovery.

I envision a world where data-driven research is supported by a variety of data commons, enabled by knowable rules of engagement and governed by a set of key principles. Together, these newly-emerging data commons will identify and shape best practices, culled from current data stewards, active data scientists, and the larger public community.

What might these practices be? And what overall makes a data commons work?

Here are a few of my thoughts:

  1. A data commons should provide a safe and secure physical space for housing data.
  2. A data commons should include tools, models, and visualization routines that allow interrogation of the data.
  3. A data commons should make it easy to locate the data stored within it.
  4. No data commons is an island. Each data commons should be designed to support discovering and linking to other relevant data sets, whether those data sets are held internally or located in another data commons.
  5. Contributors to the data commons should be able apply standardized metadata to their data sets.
  6. A data commons should manage permissions and control access so that it maintains the access rules and conditions under which the data were originally collected.
  7. A data commons should handle identity and access management in a way that avoids the challenge of continuous and arduous authentication.
  8. Management issues, such as forecasting the cost of data storage and establishing the time horizon for sunsetting data sets, should be informed  by assessments of the data’s present and future value to society.
  9. All those involved—from data depositors to those who oversee and manage the data commons—should employ the principles of risk trade-offs, balancing the anticipated scientific worth of data sets against the possible loss of valuable data sets.
  10. Effectively managing legacy data sets provides a needed but incomplete perspective on designing the data commons of the future.

Of course, we’ll need many other principles and ideas to design and establish a robust data commons that can accelerate data-driven discovery in the ways we need and imagine. Please share your thoughts. After all, a commons serves as a meeting place for many perspectives and benefits by those in the community actively engaging with those ideas.

Photo credit (top): Modified from Figure 1 in Monnet C, Loux V, Gibrat J-F, Spinnler E, Barbe V, et al. (2010) The Arthrobacter arilaitensis Re117 Genome Sequence Reveals Its Genetic Adaptation to the Surface of Cheese. PLoS ONE 5(11): e15489. doi:10.1371/journal.pone.0015489 (CC BY 2.5)

Quality Data, Quality Findings

Stanford’s John Ioannidis recently joined 71 other methodologists in proposing that we lower the p-value threshold for claiming statistical significance in research from .05 to .005. This proposal is intended to reduce the rate of false positives and improve reproducibility in scientific research.

On the other hand, given the lax, inaccurate, or confusing ways the p-value has been applied, other researchers such as Jonas Ranstam have called for abandoning the p-value entirely in favor of confidence intervals. (A confidence interval is a range of values that provides a pretty good estimate of what the true value actually is—such as the degree to which a medication improves sleep or the likelihood that two samples came from the same genus).

Of course, given the nature of science and scientific research—namely, that we must rely upon sampling because we cannot study every person or every cell—neither the p-value nor confidence intervals can be the perfect arbiter of scientific “truth.” Instead, these figures only help us determine the extent to which scientific results can be generalized beyond the specific group (of people, of cells) tested or experimented upon.

So, p-value or confidence interval—take your pick.

To me, the important issue is how the findings of a research project can direct—with sufficient confidence—the next researcher’s work or the clinician trying to select a therapeutic course.

Ultimately, quality findings require quality data.

That’s why one of the goals of our new strategic plan targets improving the data gathered and analyzed in research studies.

NLM is investing in clinical terminologies to improve how the data collected during research and clinical care are labeled. Properly labeled data not only provide trustable indicators of the phenomena under study; they also allow researchers to more readily combine data from different studies, thus supporting data reuse and expanding the possibility of new findings from that data.

NLM is also investing in strategies to improve data capture and curation, both of which will improve the integrity and precision of data collected during research. Thoughtful, intentional curation and disciplined annotation will also make it easier to locate data sets, increasing the efficiency with which new studies can be designed and implemented.

So pick your side in the debate on the best way to signal significance, but remember that NLM resources are invested at multiple points along the research process, helping to ensure data quality, to simplify its discovery, and to apply analytical tools to uncover the insights the data hold.

The results of NLM’s investments promise to improve our ability to conduct research and interpret its findings, and in turn, those research improvements will be good for science, good for clinical care, and good for health.

Celebrating Libraries and Those Who Make Them Run

It’s National Library Week in the US, the 60th year of celebrating all things library. This year’s theme is “Libraries Lead,” and Misty Copeland, principal dancer at the American Ballet Theatre, is the Honorary Chair.

Sponsored by the American Library Association, this national observance celebrates the role of libraries in our communities, with each day set aside to acknowledge different contributions.

Today, the spotlight is on library workers.

We have 1,700 stellar library workers from a variety of backgrounds, all of whom lend their expertise to advancing the mission of the National Library of Medicine and the National Institutes of Health and to supporting the health and well-being of the public at large. I take this opportunity to thank them for their service, their hard work, and their dedication, and to acknowledge the contributions they make every day to the success of this institution. NLM would not be the special place it is without them.

What makes NLM so special?

Let’s take a look.

L | A library is both a place and a space. NLM’s place, a beautiful mid-century, low-rise building on the NIH campus, was built nearly 60 years ago, and then expanded in 1980 by adding a towering research center. Increasingly, however, we are a space, an abstract location, accessed through the internet, where we meet our patrons and they consult our resources.

I | A library provides information. Our literature services, well-known through MEDLINE and PubMed, form the core of the information our library provides, but we also collect, store, and make data and images available to the world through dbGaP (the Database of Genotypes and Phenotypes), Genbank, NLM Digital Collections, Open-i, and other archives.

B | Of course, our library holds books. Some of those books come from the 10th century, others just arrived yesterday. The NLM collection development plan guides our staff in selecting books, along with other materials, that reflect the state of health and biomedical knowledge. And we don’t just put books on the shelf. We carefully catalog them, make them findable, and through digitization or other lending processes, make them available.

R | Research! As one of the 27 institutes and centers of the National institutes of Health, we are a research operation. We have two intramural research programs, one in computational biology and one exploring clinical data through natural language processing, machine learning, and deep learning approaches. We also support research training and fund basic and applied research in biomedical informatics at universities and research centers. In addition, given our rich literature and data resources, we help researchers around the world. Indeed, I would posit that no biomedical discovery has occurred in the past 50 years that hasn’t been touched by our research.

A | Answers. Between our online resources and our cracker-jack staff, NLM provides answers, whether to the questions of a young parent worried about a child’s rash, to the high school student exploring genetics, to the physician seeking treatment options for a thorny case. And where we can’t provide the answer, we can still point people in the right direction by recommending websites, materials, or organizations to consult.

R | We reach out to a range of customers and stakeholders in every way we can. We train scientists who must register their studies in ClinicalTrials.gov. We support projects in underserved communities to ensure everyone can access current, quality health information. And we work with our partners in the National Network of Libraries of Medicine to get our resources into every corner of the country, serving industry, local governments, and communities as a whole.

Y |Though this library is almost 200 years old, we must be ready for the youth of today who will become the scientists, clinicians, and patrons of tomorrow. These young people take to Instagram and Snapchat rather than email and expect visual and interactive experiences rather than reading. We are inspired by them to build toward a future where reading our resources is only one of the ways to help foster the public’s health.

One day, some of those young people will become part of the 1,700 strong who make NLM hum, and one of them will be NLM’s director, talking or writing about the many things that continue to make this place special. Like today, I’m sure the staff will be at the top of the list.

Thank you for all you do, colleagues!

Reflections on the Work of the Research Data Alliance

The Research Data Alliance (RDA) is a community-driven, interdisciplinary, international organization dedicated to collaboratively building the social and technical infrastructure necessary for wide-scale data sharing and advancing open science initiatives. Just short of five years old, this group gathers twice a year at plenary meetings, the most recent just last week.

These are no big-lecture, hallway-conversation meetings. As I discovered in Berlin last week, they are working meetings, in the best sense of the phrase—where the work involves creating and validating the mechanisms and standards for data sharing. That work is done by volunteers from across disciplines—over 7,000 people engaged in small work groups, local activities, and conference-based sessions. These volunteers deliberate and construct standards for data sharing, and then establish strategies for testing and endorsing these standards and gaining community consensus and adoption—including partnering with notable standard-setting bodies such as ISO or IEEE.

Much of the work focuses on making data and data repositories FAIR— Findable, Accessible, Interoperable, and Reusable—which is something I’ve talked a lot about in this blog.

But RDA espouses a broader vision than the approach NLM has taken so far with data. Where we provide public access to full-text articles, some of which link to associated data, RDA advocates for putting all research-generated data in domain-specific, well-curated repositories.

To achieve that vision, RDA members are working to develop the following three key elements:

  • a schema to link data to articles,
  • a mechanism for citing data extracts, and
  • a way to recognize high-quality data repositories.

Right now, a single publisher may have 50 or 60 different ways of linking articles to data. That means that the estimated 25,000 publishers and 5,000 repositories that manage data have potentially millions of ways of accomplishing this task. Instituting a standardized schema to link data to articles would bring significant order and discoverability to this overwhelming diversity. That consistency would yield immediate benefits, tops among them making data findable and the links interoperable.

Efficient data citations will also be a boon to findability. RDA is working on developing dynamic data citations, which would provide persistent identifiers tying data extracts to their repositories and tracking different versions of the data. Machine-created and machine-readable, data citations would enhance rigor and reproducibility in research by ensuring the data generated in support of key findings remains accessible.

But linking to and tracking data won’t get us far if the data itself is untrustworthy.

To address that, RDA encourages well-curated repositories, but what exactly does that mean?

Certification provides one way of acknowledging the quality of a repository. RDA doesn’t sponsor a certification mechanism, but it recognizes several, including the CoreTrustSeal program.  (For more on data certification, see “A Primer on the Certifications of a Trusted Digital Repository,” by Dawei Lin from the NIH National Institute of Allergy and Infectious Diseases.)

But why does all this matter to NIH and to NLM specifically?

I came to the RDA meeting to explore complementary approaches to what NLM is already doing to curate and assign metadata to data. I was especially looking for guidance on how to handle new data types such as images and environmental exposures.

I got some of that, but I also learned that NLM has much to contribute to RDA’s work. Particularly given our expertise in clinical terminologies and literature languages, we add rich depth to the ways data and other resources can be characterized.

In addition, I learned that we at NLM and NIH face many of the same challenges as our global partners: efficiently managing legacy data while not constraining the future to the problems of the past; fostering the adoption of common approaches and standards when the benefit to the larger scientific community may be greater than the value to the individual investigator; coordinating a voluntary, community-led process that has mission-critical consequences; and creating a permanent home and support organization for the wide range of standards actually needed for data-driven discovery.

Finally, I learned that people participate in the work of RDA because it both draws on their expertise and advances their own scholarly efforts. In other words, it’s mutually beneficial. But after my time with the group last week, I suspect we all get more than we give. For NLM anyway—as we begin to implement our new strategic plan—RDA’s goal of creating a global data ecosystem of best practices, standards, and interoperable data infrastructures is encouraging and something to look forward to.