Reuse Of Data Research Articles

Large-scale single-cell RNA sequencing atlases of human immune cells across lifespan: Possibilities and challenges.

Single-cell RNA sequencing technologies have successfully been leveraged for immunological insights into human prenatal, pediatric, and adult tissues. These single-cell studies have led to breakthroughs in our understanding of stem, myeloid, and lymphoid cell function. Computational analysis of fetal hematopoietic tissues has uncovered trajectories for T- and B-cell differentiation across multiple organ sites, and how these trajectories might be dysregulated in fetal and pediatric health and disease. As we enter the age of large-scale, multiomic, and integrative single-cell meta-analysis, we assess the advances and challenges of large-scale data generation, analysis, and reanalysis, and data dissemination for a broad range of scientific and clinical communities. We discuss Findable, Accessible, Interoperable, and Reusable data sharing and unified cell ontology languages as strategic areas for progress of the field in the near future. We also reflect on the trend toward deployment of multiomic and spatial genomic platforms within single-cell RNA sequencing projects, and the crucial role these data types will assume in the immediate future toward creation of comprehensive and rich single-cell atlases. We demonstrate using our recent studies of human prenatal and adult hematopoietic tissues the importance of interdisciplinary and collaborative working in science to reveal biological insights in parallel with technological and computational innovations.

Using Existing Clinical Information Models for Dutch Quality Registries to Reuse Data and Follow COUMT Paradigm.

Reuse of health care data for various purposes, such as the care process, for quality measurement, research, and finance, will become increasingly important in the future; therefore, "Collect Once Use Many Times" (COUMT). Clinical information models (CIMs) can be used for content standardization. Data collection for national quality registries (NQRs) often requires manual data entry or batch processing. Preferably, NQRs collect required data by extracting data recorded during the health care process and stored in the electronic health record. The first objective of this study was to analyze the level of coverage of data elements in NQRs with developed Dutch CIMs (DCIMs). The second objective was to analyze the most predominant DCIMs, both in terms of the coverage of data elements as well as in their prevalence across existing NQRs. For the first objective, a mapping method was used which consisted of six steps, ranging from a description of the clinical pathway to a detailed mapping of data elements. For the second objective, the total number of data elements that matched with a specific DCIM was counted and divided by the total number of evaluated data elements. An average of 83.0% (standard deviation: 11.8%) of data elements in studied NQRs could be mapped to existing DCIMs . In total, 5 out of 100 DCIMs were needed to map 48.6% of the data elements. This study substantiates the potential of using existing DCIMs for data collection in Dutch NQRs and gives direction to further implementation of DCIMs. The developed method is applicable to other domains. For NQRs, implementation should start with the five DCIMs that are most prevalently used in the NQRs. Furthermore, a national agreement on the leading principle of COUMT for the use and implementation for DCIMs and (inter)national code lists is needed.

An inexact subgraph matching algorithm for subpart retrieval in NC process reuse

With the wide application of CAD/CAM technology, huge amounts of process data are generated each year, which are valuable to be referred to and reused. For the reuse of these historical process data, the retrieval of the subpart model is the key, as similar structures usually correspond to similar NC processes. However, the existing subpart retrieval methods are only practical to the parts with simple structures or fixed geometry. This research proposes a subpart model retrieval method based on inexact subgraph matching, which can be used for parts of arbitrary complexity. By representing the part model as an attribute graph, the task is first transformed into an inexact subgraph matching task where some degree of matching difference is accepted to retrieve similar subparts. Next, this research proposes VF3-light-N, an inexact subgraph matching algorithm that operates on attribute graphs. The idea of VF3-light-N is to quantify the mismatch of both nodes and edges based on matching error first. Then use the empty map to remove the nodes that cannot be matched, so that the remaining nodes and edges can be matched exactly. Thus, VF3-light-N can search out all the matched subgraphs within a certain noise threshold. A case study based on aircraft structural parts is carried out, and the result shows that the proposed method can efficiently retrieve subparts from complex part models. Furthermore, it is believed that VF3-light-N can be also used in other inexact subgraph matching applications, such as feature recognition.

Learning From Guided Play: Improving Exploration for Adversarial Imitation Learning With Simple Auxiliary Tasks

Adversarial imitation learning (AIL) has become a popular alternative to supervised imitation learning that reduces the distribution shift suffered by the latter. However, AIL requires effective exploration during an online reinforcement learning phase. In this work, we show that the standard, naïve approach to exploration can manifest as a suboptimal local maximum if a policy learned with AIL sufficiently matches the expert distribution without fully learning the desired task. This can be particularly catastrophic for manipulation tasks, where the difference between an expert and a non-expert state-action pair is often subtle. We present Learning from Guided Play (LfGP), a framework in which we leverage expert demonstrations of multiple exploratory, auxiliary tasks in addition to a main task. The addition of these auxiliary tasks forces the agent to explore states and actions that standard AIL may learn to ignore. Additionally, this particular formulation allows for the reusability of expert data between main tasks. Our experimental results in a challenging multitask robotic manipulation domain indicate that LfGP significantly outperforms both AIL and behaviour cloning, while also being more expert sample efficient than these baselines. To explain this performance gap, we provide further analysis of a toy problem that highlights the coupling between a local maximum and poor exploration, and also visualize the differences between the learned models from AIL and LfGP.

A Reconfigurable Spatial Architecture for Energy-Efficient Inception Neural Networks

Convolutional neural networks (CNNs) have been widely utilized in modern artificial intelligent (AI) systems. In particular, GoogLeNet, one of the most popular CNNs, consisting of a number of inception layers and max-pooling layers, has been intensively studied for mobile and embedded scenarios. However, the energy efficiency of GoogLeNet in hardware is still limited as the huge data movement between the processor and the memory. Therefore, designing a dataflow and the corresponding hardware architecture to achieve parallel processing with minimal data movement is rather critical to achieve high energy efficiency and throughput. In this paper, we propose a novel column stationary (CS) dataflow that maximally exploits the local data reuse of both the filter weights and feature maps. Moreover, a reconfigurable spatial architecture was proposed to map multiple convolution kernels (with different types and dimensions) in parallel to the processing engines (PEs) array. In this case, multiple convolution kernels can share the same input feature maps (activations) in computing process. In our hardware design, we utilize three typical convolution kernels (i.e., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5 \times 5$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3 \times 3$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1 \times 1$ </tex-math></inline-formula> , corresponding to the inception layers of GoogLeNet) as an example to test the efficiency of our proposed dataflow and hardware architecture. The accelerator was implemented for one inception layer of the GoogLeNet in a 55-nm foundry’s CMOS process. The test results show that our CS dataflow can reduce ~85% energy consumption for memory access and save area of 13% and power of 12% for computing. In summary, our CS dataflow is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.2\times $ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.5\times $ </tex-math></inline-formula> more energy-efficient compared to state-of-the-art dataflows.

Continuous Validation Across Macromolecular Structure Determination Process.

The overall quality of the experimentally determined structures contained in the PDB is exceptionally high, mainly due to the continuous improvement of model building and structural validation programs. Improving reproducibility on a large scale requires expanding the concept of validation in structural biology and all other disciplines to include a broader framework that encompasses the entire project. A successful approach to science requires diligent attention to detail and a focus on the future. An earnest commitment to data availability and reuse is essential for scientific progress, be that by human minds or artificial intelligence.

A harmonized analysis of five Canadian pregnancy cohort studies: exploring the characteristics and pregnancy outcomes associated with prenatal alcohol exposure

BackgroundAs a teratogen, alcohol exposure during pregnancy can impact fetal development and result in adverse birth outcomes. Despite the clinical and social importance of prenatal alcohol use, limited routinely collected information or epidemiological data exists in Canada. The aim of this study was to pool data from multiple Canadian cohort studies to identify sociodemographic characteristics before and during pregnancy that were associated with alcohol consumption during pregnancy and to assess the impact of different patterns of alcohol use on birth outcomes.MethodsWe harmonized information collected (e.g., pregnant women’s alcohol intake, infants' gestational age and birth weight) from five Canadian pregnancy cohort studies to consolidate a large sample (n = 11,448). Risk factors for any alcohol use during pregnancy, including any alcohol use prior to pregnancy recognition, and binge drinking, were estimated using binomial regressions including fixed effects of pregnancy cohort membership and multiple maternal risk factors. Impacts of alcohol use during pregnancy on birth outcomes (preterm birth and low birth weight for gestational) were also estimated using binomial regression models.ResultsIn analyses adjusting for multiple risk factors, women’s alcohol use during pregnancy, both any use and any binge drinking, was associated with drinking prior to pregnancy, smoking during pregnancy, and white ethnicity. Higher income level was associated with any drinking during pregnancy. Neither drinking during pregnancy nor binge drinking during pregnancy was significantly associated with preterm delivery or low birth weight for gestational age in our sample.ConclusionsPooling data across pregnancy cohort studies allowed us to create a large sample of Canadian women and investigate the risk factors for alcohol consumption during pregnancy. We suggest that future pregnancy and birth cohorts should always include questions related to the frequency and amount of alcohol consumed before and during pregnancy that are prospectively harmonized to support data reusability and collaborative research.

Effective and feasible interventions to improve structured EHR data registration and exchange: A concept mapping approach and exploration of practical examples in the Netherlands

BackgroundData in Electronic Health Records (EHRs) is often poorly structured and standardized, which hampers data reuse. Research described some examples of interventions to increase and improve structured and standardized data, such as guidelines and policies, training and user friendly EHR interfaces. However, little is known about the translation of this knowledge into practical solutions. Our study aimed to specify the most effective and feasible interventions that enable better structured and standardized EHR data registration and described practical examples of successfully implemented interventions. MethodsA concept mapping approach was used to determine feasible interventions that were considered to be effective or have been successfully implemented in Dutch hospitals. A focus group was held with Chief Medical Information Officers and Chief Nursing Information Officers. After interventions were determined, multidimensional scaling and cluster analysis were performed to categorize sorted interventions using Groupwisdom™, an online tool for concept mapping. Results are presented as Go-Zone plots and cluster maps. Following, semi-structured interviews were conducted to describe practical examples of successful interventions. ResultsInterventions were classified into seven clusters ranked from highest to lowest perceived effectiveness: (1) education on usefulness and need; (2) strategic and (3) tactical organizational policies; (4) national policy; (5) monitoring and adjusting data (6) structure of and support from the EHR and (7) support in the registration process (EHR independent). Interviewees emphasized the following interventions proven successful in their practice: an enthusiastic ambassador per specialty who is responsible for educating peers by increasing awareness of the direct benefit of structured and standardized data registration; dashboards for continuous feedback on data quality; and EHR functionalities that support (automating) the registration process. ConclusionsOur study provided a list of effective and feasible interventions including practical examples of interventions that have been successful. Organizations should continue to share their best practices to learn from and attempted interventions to prevent implementation of ineffective interventions.

Allocation of attention to metadata and retrieval functions: Implications for perceived value and open data discovery and reuse

Metadata and retrieval functions play a vital role in aiding researchers in the discovery and reuse of open data. However, the diversity of metadata elements and retrieval functions poses a challenge to data searchers’ limited attentional resources. This study aims to examine the allocation of attention to metadata elements and retrieval functions and its implications for perceived value and intentions to discover and reuse open data by drawing upon the attentional drift-diffusion model, flow theory, and perceived value literature. An experiment with 48 participants was conducted to explore the proposed relationships. Multiple linear regression analysis was performed to analyze the data. The results suggest that researchers’ attention to high-value functions amplifies the perceived value and motivates data discovery intention. Attention to high-value metadata elements motivates data discovery and reuse intention. In contrast, attention to low-value metadata elements hampers the perceived value and inhibits data discovery and reuse intention. These findings put forward a new lens for exploring the attention mechanisms underlying perceived value, data discovery and reuse intention and highlight the important role of the value of metadata and retrieval functions in attention mechanisms. Additionally, this paper identifies the positive effect of perceived ease of use on users’ intentions to find, evaluate, and access open data. Perceived usefulness positively affects users’ intentions to evaluate open data. However, in contrast to perceived intentions to reuse open data assessed by self-reported measures, perceived value is not a salient motivator of open data reuse intention measured by behavioral indicators. These findings reveal the distinct effects of perceived value on perceived intention and intentional action in data reuse. With these insights, this study develops practical strategies to optimize the design of metadata and retrieval functions in data retrieval systems.

Software-hardware co-design for accelerating large-scale graph convolutional network inference on FPGA

Inspired by convolutional neural networks, graph convolutional networks (GCNs) have been proposed for processing non-Euclidean graph data and successfully been applied in recommendation systems, smart traffic, etc. However, subject to the sparsity and irregularity of GCN models, the complex execution pattern of large-scale GCN poses huge challenges to the efficient inference on general purpose CPUs and GPUs, such as workload imbalance and irregular memory access. Therefore, we propose a software-hardware co-design framework for low-latency GCN inference on field programmable gate array. Specifically, at the algorithm level, we propose an attention-mechanism-based graph sparsification approach to reduce the redundant relation in the graph structure and alleviate irregularity without losing accuracy. Then, at the hardware design level, based on the sparsified graph, we propose a two-stage hardware architecture that supports the two phases with a distinct execution mode in the GCN. In order to achieve low-latency computation, edge-level and feature-level parallelism are exploited in the aggregation phase. In addition, a graph partition strategy is exploited to efficiently improve data reuse. The experimental results demonstrate that our proposed framework can achieve 739× speedup compared to CPU, 13.7× speedup compared to GPU on average and 6.8× speedup compared to state-of-the-art accelerators.

Developing a standardized but extendable framework to increase the findability of infectious disease datasets

Biomedical datasets are increasing in size, stored in many repositories, and face challenges in FAIRness (findability, accessibility, interoperability, reusability). As a Consortium of infectious disease researchers from 15 Centers, we aim to adopt open science practices to promote transparency, encourage reproducibility, and accelerate research advances through data reuse. To improve FAIRness of our datasets and computational tools, we evaluated metadata standards across established biomedical data repositories. The vast majority do not adhere to a single standard, such as Schema.org, which is widely-adopted by generalist repositories. Consequently, datasets in these repositories are not findable in aggregation projects like Google Dataset Search. We alleviated this gap by creating a reusable metadata schema based on Schema.org and catalogued nearly 400 datasets and computational tools we collected. The approach is easily reusable to create schemas interoperable with community standards, but customized to a particular context. Our approach enabled data discovery, increased the reusability of datasets from a large research consortium, and accelerated research. Lastly, we discuss ongoing challenges with FAIRness beyond discoverability.

The Extending Ocean Drilling Pursuits (eODP) Project: Synthesizing Scientific Ocean Drilling Data

AbstractFor over 50 years, cores recovered from ocean basins have generated fossil, lithologic, and chemical archives that have revolutionized fields within the earth sciences. Although scientific ocean drilling (SOD) data are openly available following each expedition, the formats for these data are heterogeneous. Furthermore, lithological, chronological, and paleobiological data are typically separated into different repositories, limiting researchers' abilities to discover and analyze integrated SOD data sets. Emphasis within Earth Sciences on Findable, Accessible, Interoperable, and Reusable (FAIR) Data Principles and the establishment of community‐led databases provide a pathway to unite SOD data and further harness the scientific potential of the investments made in offshore drilling. Here, we describe a workflow for compiling, cleaning, and standardizing key SOD records, and importing them into the Paleobiology Database and Macrostrat, systems with versatile, open data distribution mechanisms. These efforts are being carried out by the extending Ocean Drilling Pursuits (eODP) project. eODP has processed all of the lithological, chronological, and paleobiological data from one SOD repository, along with numerous other data sets that were never deposited in a database; these were manually transcribed from original reports. This compiled data set contains over 79,899 lithological units from 1,125 drilling holes from 422 sites. Over 26,000 fossil‐bearing samples, with 5,378 taxonomic entries from 13 biological groups, are placed within this lithologic spatiotemporal framework. All information is available via GitHub and Macrostrat's application programming interface, which renders data retrievable by a variety of parameters, including age, site, and lithology.

ModelCIF: An Extension of PDBx/mmCIF Data Representation for Computed Structure Models

ModelCIF (github.com/ihmwg/ModelCIF) is a data information framework developed for and by computational structural biologists to enable delivery of Findable, Accessible, Interoperable, and Reusable (FAIR) data to users worldwide. ModelCIF describes the specific set of attributes and metadata associated with macromolecular structures modeled by solely computational methods and provides an extensible data representation for deposition, archiving, and public dissemination of predicted three-dimensional (3D) models of macromolecules. It is an extension of the Protein Data Bank Exchange / macromolecular Crystallographic Information Framework (PDBx/mmCIF), which is the global data standard for representing experimentally-determined 3D structures of macromolecules and associated metadata. The PDBx/mmCIF framework and its extensions (e.g., ModelCIF) are managed by the Worldwide Protein Data Bank partnership (wwPDB, wwpdb.org) in collaboration with relevant community stakeholders such as the wwPDB ModelCIF Working Group (wwpdb.org/task/modelcif). This semantically rich and extensible data framework for representing computed structure models (CSMs) accelerates the pace of scientific discovery. Herein, we describe the architecture, contents, and governance of ModelCIF, and tools and processes for maintaining and extending the data standard. Community tools and software libraries that support ModelCIF are also described.

Data-Driven Quantitative Intrinsic Hazard Criteria for Nanoproduct Development in a Safe-by-Design Paradigm: A Case Study of Silver Nanoforms.

The current European (EU) policies, that is, the Green Deal, envisage safe and sustainable practices for chemicals, which include nanoforms (NFs), at the earliest stages of innovation. A theoretically safe and sustainable by design (SSbD) framework has been established from EU collaborative efforts toward the definition of quantitative criteria in each SSbD dimension, namely, the human and environmental safety dimension and the environmental, social, and economic sustainability dimensions. In this study, we target the safety dimension, and we demonstrate the journey toward quantitative intrinsic hazard criteria derived from findable, accessible, interoperable, and reusable data. Data were curated and merged for the development of new approach methodologies, that is, quantitative structure-activity relationship models based on regression and classification machine learning algorithms, with the intent to predict a hazard class. The models utilize system (i.e., hydrodynamic size and polydispersity index) and non-system (i.e., elemental composition and core size)-dependent nanoscale features in combination with biological in vitro attributes and experimental conditions for various silver NFs, functional antimicrobial textiles, and cosmetics applications. In a second step, interpretable rules (criteria) followed by a certainty factor were obtained by exploiting a Bayesian network structure crafted by expert reasoning. The probabilistic model shows a predictive capability of ≈78% (average accuracy across all hazard classes). In this work, we show how we shifted from the conceptualization of the SSbD framework toward the realistic implementation with pragmatic instances. This study reveals (i) quantitative intrinsic hazard criteria to be considered in the safety aspects during synthesis stage, (ii) the challenges within, and (iii) the future directions for the generation and distillation of such criteria that can feed SSbD paradigms. Specifically, the criteria can guide material engineers to synthesize NFs that are inherently safer from alternative nanoformulations, at the earliest stages of innovation, while the models enable a fast and cost-efficient in silico toxicological screening of previously synthesized and hypothetical scenarios of yet-to-be synthesized NFs.

Standardized naming of microbiome samples in Genomes OnLine Database.

The power of next-generation sequencing has resulted in an explosive growth in the number of projects aiming to understand the metagenomic diversity of complex microbial environments. The interdisciplinary nature of this microbiome research community, along with the absence of reporting standards for microbiome data and samples, poses a significant challenge for follow-up studies. Commonly used names of metagenomes and metatranscriptomes in public databases currently lack the essential information necessary to accurately describe and classify the underlying samples, which makes a comparative analysis difficult to conduct and often results in misclassified sequences in data repositories. The Genomes OnLine Database (GOLD) (https:// gold.jgi.doe.gov/) at the Department of Energy Joint Genome Institute has been at the forefront of addressing this challenge by developing a standardized nomenclature system for naming microbiome samples. GOLD, currently in its twenty-fifth anniversary, continues to enrich the research community with hundreds of thousands of metagenomes and metatranscriptomes with well-curated and easy-to-understand names. Through this manuscript, we describe the overall naming process that can be easily adopted by researchers worldwide. Additionally, we propose the use of this naming system as a best practice for the scientific community to facilitate better interoperability and reusability of microbiome data.

Are Institutional Research Data Policies in the US Supporting the FAIR Principles? A Content Analysis

Objective: The FAIR principles were created with the goal of enhancing the reusability of research data and to give guidance on how to make data Findable, Accessible, Interoperable and Reusable. In this article we explore the role of institutional research data policies in enabling and encouraging researchers at their institutions to generate FAIR data.Methods: We identified the research data policies in place for “very high research activity” institutions (as defined by Carnegie classification) in the United States. We created a list of 31 criteria, based on previous work by Davidson et al. (2019) and Briney et al. (2015), and evaluated the 40 policies using a content analysis methodology.&amp;nbsp;Results: The guiding principles and the definitions for research data in the policies support the idea that institutional policies are a potential tool for the implementation of the FAIR principles. However, our analysis indicates that they are not generally used for that purpose. Only one policy mentions FAIR. Data sharing is mentioned in half of the policies, but 11 of these only note this concept in the context of funder requirements. Access and retention sections are mostly written without considering publicly available data. Twenty-nine policies do not mention data documentation.&amp;nbsp;Conclusions: We discuss ways in which these institutional policies represent a missed opportunity to implement the FAIR principles and suggest ways policies could be modified to encourage researchers to follow them. We also discuss future research opportunities to examine how policy implementation may affect what institutional support researchers receive.

Low-Complexity Data-Reuse RLS Algorithm for Stereophonic Acoustic Echo Cancellation

Stereophonic audio devices employ two loudspeakers and two microphones in order to create a bidirectional sound effect. In this context, the stereophonic acoustic echo cancellation (SAEC) setup requires the estimation of four echo paths, each one corresponding to a loudspeaker-to-microphone pair. The widely linear (WL) model was proposed in recent literature in order to simplify the handling of the SAEC mathematical model. Moreover, low complexity recursive least- squares (RLS) adaptive algorithms were developed within the WL framework and successfully tested for SAEC scenarios. This paper proposes to apply a data-reuse (DR) approach for the combination between the RLS algorithm and the dichotomous coordinate descent (DCD) iterative method. The resulting WL-DR-RLS-DCD algorithm inherits the convergence properties of the RLS family and requires an amount of mathematical operations attractive for practical implementations. Simulation results show that the DR approach improves the tracking capabilities of the RLS-DCD algorithm, with an acceptable surplus in terms of computational workload.

EnzymeML: seamless data flow and modeling of enzymatic data.

The design of biocatalytic reaction systems is highly complex owing to the dependency of the estimated kinetic parameters on the enzyme, the reaction conditions, and the modeling method. Consequently, reproducibility of enzymatic experiments and reusability of enzymatic data are challenging. We developed the XML-based markup language EnzymeML to enable storage and exchange of enzymatic data such as reaction conditions, the time course of the substrate and the product, kinetic parameters and the kinetic model, thus making enzymatic data findable, accessible, interoperable and reusable (FAIR). The feasibility and usefulness of the EnzymeML toolbox is demonstrated in six scenarios, for which data and metadata of different enzymatic reactions are collected and analyzed. EnzymeML serves as a seamless communication channel between experimental platforms, electronic lab notebooks, tools for modeling of enzyme kinetics, publication platforms and enzymatic reaction databases. EnzymeML is open and transparent, and invites the community to contribute. All documents and codes are freely available at https://enzymeml.org .

Healthcare utilization is a collider: an introduction to collider bias in EHR data reuse.

Collider bias is a common threat to internal validity in clinical research but is rarely mentioned in informatics education or literature. Conditioning on a collider, which is a variable that is the shared causal descendant of an exposure and outcome, may result in spurious associations between the exposure and outcome. Our objective is to introduce readers to collider bias and its corollaries in the retrospective analysis of electronic health record (EHR) data. Collider bias is likely to arise in the reuse of EHR data, due to data-generating mechanisms and the nature of healthcare access and utilization in the United States. Therefore, this tutorial is aimed at informaticians and other EHR data consumers without a background in epidemiological methods or causal inference. We focus specifically on problems that may arise from conditioning on forms of healthcare utilization, a common collider that is an implicit selection criterion when one reuses EHR data. Directed acyclic graphs (DAGs) are introduced as a tool for identifying potential sources of bias during study design and planning. References for additional resources on causal inference and DAG construction are provided.

The CARE Principles and the Reuse, Sharing, and Curation of Indigenous Data in Canadian Archaeology

ABSTRACTReuse and sharing of archaeological data are tied to ethics in data practice, research design, and the rights of Indigenous peoples in decision-making about their heritage. In this article, the authors discuss how the CARE (Collective benefit, Authority to control, Responsibility, and Ethics) principles and Indigenous data governance create intellectual space for archaeological research. We show how archaeologists can use this framework to highlight hidden costs and labor associated with the “data ecosystem,” which are often borne by Indigenous nations and communities. The CARE framework gives voice to Indigenous peoples’ concerns around data sharing, curation, and reuse; ways we can redress these issues; and strategies that facilitate Indigenous nations and communities in deriving collective benefit from research. In archaeology, these efforts include greater work on heritage legislation and policy, repositioning Indigenous peoples as active stewards of their data, and building capacity in digital methods and ethical data practice. Each Indigenous nation and community has its own interests, values, and protocols, and we suggest paths to bring data practice into alignment with the CARE framework.