Nature 4.0: A networked sensor system for integrated biodiversity monitoring.

Ecosystem functions and services are severely threatened by unprecedented global loss in biodiversity. To counteract these trends, it is essential to develop systems to monitor changes in biodiversity for planning, evaluating, and implementing conservation and mitigation actions. However, the implementation of monitoring systems suffers from a trade-off between grain (i.e., the level of detail), extent (i.e., the number of study sites), and temporal repetition. Here, we present an applied and realized networked sensor system for integrated biodiversity monitoring in the Nature 4.0 project as a solution to these challenges, which considers plants and animals not only as targets of investigation, but also as parts of the modular sensor network by carrying sensors. Our networked sensor system consists of three main closely interlinked components with a modular structure: sensors, data transmission, and data storage, which are integrated into pipelines for automated biodiversity monitoring. We present our own real-world examples of applications, share our experiences in operating them, and provide our collected open data. Our flexible, low-cost, and open-source solutions can be applied for monitoring individual and multiple terrestrial plants and animals as well as their interactions. Ultimately, our system can also be applied to area-wide ecosystem mapping tasks, thereby providing an exemplary cost-efficient and powerful solution for biodiversity monitoring. Building upon our experiences in the Nature 4.0 project, we identified ten key challenges that need to be addressed to better understand and counteract the ongoing loss of biodiversity using networked sensor systems. To tackle these challenges, interdisciplinary collaboration, additional research, and practical solutions are necessary to enhance the capability and applicability of networked sensor systems for researchers and practitioners, ultimately further helping to ensure the sustainable management of ecosystems and the provision of ecosystem services.

Voice analytics in the wild: Validity and predictive accuracy of common audio-recording devices.

The use of voice recordings in both research and industry practice has increased dramatically in recent years-from diagnosing a COVID-19 infection based on patients' self-recorded voice samples to predicting customer emotions during a service center call. Crowdsourced audio data collection in participants' natural environment using their own recording device has opened up new avenues for researchers and practitioners to conduct research at scale across a broad range of disciplines. The current research examines whether fundamental properties of the human voice are reliably and validly captured through common consumer-grade audio-recording devices in current medical, behavioral science, business, and computer science research. Specifically, this work provides evidence from a tightly controlled laboratory experiment analyzing 1800 voice samples and subsequent simulations that recording devices with high proximity to a speaker (such as a headset or a lavalier microphone) lead to inflated measures of amplitude compared to a benchmark studio-quality microphone while recording devices with lower proximity to a speaker (such as a laptop or a smartphone in front of the speaker) systematically reduce measures of amplitude and can lead to biased measures of the speaker's true fundamental frequency. We further demonstrate through simulation studies that these differences can lead to biased and ultimately invalid conclusions in, for example, an emotion detection task. Finally, we outline a set of recording guidelines to ensure reliable and valid voice recordings and offer initial evidence for a machine-learning approach to bias correction in the case of distorted speech signals.

Speech production and perception data collection in R: A tutorial for web-based methods using speechcollectr.

This tutorial is designed for speech scientists familiar with the R programming language who wish to construct experiment interfaces in R. We begin by discussing some of the benefits of building experiment interfaces in R-including R's existing tools for speech data analysis, platform independence, suitability for web-based testing, and the fact that R is open source. We explain basic concepts of reactive programming in R, and we apply these principles by detailing the development of two sample experiments. The first of these experiments comprises a speech production task in which participants are asked to read words with different emotions. The second sample experiment involves a speech perception task, in which participants listen to recorded speech and identify the emotion the talker expressed with forced-choice questions and confidence ratings. Throughout this tutorial, we introduce the new R package speechcollectr, which provides functions uniquely suited to web-based speech data collection. The package streamlines the code required for speech experiments by providing functions for common tasks like documenting participant consent, collecting participant demographic information, recording audio, checking the adequacy of a participant's microphone or headphones, and presenting audio stimuli. Finally, we describe some of the difficulties of remote speech data collection, along with the solutions we have incorporated into speechcollectr to meet these challenges.

Interviewer Error Within the Face-to-Face Food Frequency Questionnaire in Large Multisite Epidemiologic Studies.

Interviewer error has long been recognized in face-to-face surveys, but little is known about interviewer error within face-to-face food frequency questionnaires, particularly in large multisite epidemiologic studies. Using dietary data from the China Multi-Ethnic Cohort (2018-2019), in which all field interviews were audio recorded, we identified a potentially error-prone sample by outlier detection and further examined the interviewer errors by reviewing these error-prone interviews. Among 174,012 questions for 5,025 error-prone interviews, 13,855 (7.96%) questions were identified with interviewer error, which mainly came from falsification (37.53%), coding error (31.71%), and reading deviation (30.76%). We found that 98.29% of interviewers and 73.71% of respondents had at least 1 error, and half of the errors could be attributed to 21.94% of interviewers or to 13.77% of respondents. Higher error risk was observed in complicated questions, such as questions assessing food quantification or referring to seasonally supplied food groups. After correcting the errors, the means and standard deviations of estimated food intakes all decreased. These findings suggested that interviewer error should not be ignored within face-to-face food frequency questionnaires and that more efforts are needed to monitor error-prone interviewers and respondents and reduce survey burdens in questionnaire design.

Automatic Behavior Assessment from Uncontrolled Everyday Audio Recordings by Deep Learning.

The manual categorization of behavior from sensory observation data to facilitate further analyses is a very expensive process. To overcome the inherent subjectivity of this process, typically, multiple domain experts are involved, resulting in increased efforts for the labeling. In this work, we investigate whether social behavior and environments can automatically be coded based on uncontrolled everyday audio recordings by applying deep learning. Recordings of daily living were obtained from healthy young and older adults at randomly selected times during the day by using a wearable device, resulting in a dataset of uncontrolled everyday audio recordings. For classification, a transfer learning approach based on a publicly available pretrained neural network and subsequent fine-tuning was implemented. The results suggest that certain aspects of social behavior and environments can be automatically classified. The ambient noise of uncontrolled audio recordings, however, poses a hard challenge for automatic behavior assessment, in particular, when coupled with data sparsity.

How doctors communicated with parents in a neonatal intensive care: Communication and ethical issues.

AIM: Doctors have a moral and legal obligation to keep patients and their families informed, and this is an integral part of care. We explored the communication strategies used by doctors when they spoke to parents in a French neonatal intensive care unit (NICU). METHODS: This was a single-centre qualitative pilot study carried out from October 2015 to January 2016. We asked five doctors (three female) to audiotape their discussions with the parents of newborn infants during their NICU stay. The doctors' mean age was 43 years, and they had a mean of 14 years of NICU experience. Each discussion was subjected to thematic content analysis. RESULTS: We analysed 40 discussions carried out between doctors on 26 newborn infants. Five communication strategy themes emerged: building understanding, how the communication was constructed, the role of the doctor, and of the parents, in the overall care of the newborn infant and how the information given to the parents developed over time. CONCLUSION: Analysing the content of the information discussed with parents provided us with the opportunity to understand the communication and ethical issues surrounding the delivery of information in a NICU. This could be used to improve future discussions between doctors and parents.

Conducting in-depth interviews with and without voice recorders: a comparative analysis.

The use of audio recordings has become a taken-for-granted approach to generating transcripts of in-depth interviewing and group discussions. In this paper we begin by describing circumstances where the use of a recorder is not, or may not be, possible, before sharing our comparative analysis of audio-recorded transcriptions and interview scripts made from notes taken during the interview (by experienced, well-trained interviewers). Our comparison shows that the data quality between audio-recorded transcripts and interview scripts written directly after the interview were comparable in the detail captured. The structures of the transcript and script were usually different because in the interview scripts, topics and ideas were grouped, rather than being in the more scattered order of the conversation in the transcripts. We suggest that in some circumstances not recording is the best approach, not 'second best'.

Animal Sound Identifier (ASI): software for automated identification of vocal animals.

Automated audio recording offers a powerful tool for acoustic monitoring schemes of bird, bat, frog and other vocal organisms, but the lack of automated species identification methods has made it difficult to fully utilise such data. We developed Animal Sound Identifier (ASI), a MATLAB software that performs probabilistic classification of species occurrences from field recordings. Unlike most previous approaches, ASI locates training data directly from the field recordings and thus avoids the need of pre-defined reference libraries. We apply ASI to a case study on Amazonian birds, in which we classify the vocalisations of 14 species in 194 504 one-minute audio segments using in total two weeks of expert time to construct, parameterise, and validate the classification models. We compare the classification performance of ASI (with training templates extracted automatically from field data) to that of monitoR (with training templates extracted manually from the Xeno-Canto database), the results showing ASI to have substantially higher recall and precision rates.

Consultation audio-recording reduces long-term decision regret after prostate cancer treatment: A non-randomised comparative cohort study.

INTRODUCTION: The life expectancy of prostate patients is long and patients will spend many years carrying the burdens & benefits of the treatment decisions they have made, therefore, it is vital that decisions on treatments are shared between patient and physician. The objective was to determine if consultation audio-recording improves quality of life, reduces regret or improves patient satisfaction in comparison to standard counselling. PATIENTS AND METHODS: In 2012 we initiated consultation audio-recordings, where patients are given a CD of their consultation to keep and replay at home. We conducted a prospective non-randomised study of patient satisfaction, quality of life (QOL) and decision regret at 12 months follow-up using posted validated questionnaires for the audio-recording (AR) patients and a control cohort. Qualitative and thematic analyses were used. RESULTS: Forty of 59 patients in the AR group, and 27 of 45 patients in the control group returned the questionnaires. Patient demographics were similar in both groups with no statistically significant differences between the two groups. Decision regret was lower in the audio-recording group (11/100) vs control group (19/100) (p = 0.04). The risk ratio for not having any long-term decision regret was 5.539 (CI 1.643-18.674), with NNT to prevent regret being 4. Regression analysis showed that receiving audio-recording was strongest predictor for absence of regret even greater than potency and incontinence. CONCLUSION: The study has shown that audio-recording clinic consultation reduces long-term decision regret, increases patient information recall, understanding and confidence in their decision. There is great potential for further expansion of this low-cost intervention.

A Brief Patient-Recorded Audio File Called TIMS (This Is My Story) Improves Communication and Empathy for Healthcare Teams in the Hospital.

Our objective was to assess the impact of a 4-question patient audio interview (this is my story [TIMS]) on medical staff empathy and communication with hospitalized patients and loved ones. We recorded a 4-question audio interview with patients and posted it to the electronic health record. We used a cross-sectional, mixed methods design to pilot this patient version of the TIMS intervention. To evaluate the intervention we collected a brief evaluation survey and conducted semistructured interviews with medical staff. Fifty-three participants responded to our TIMS evaluation survey. Fifty of 51 respondents reported the TIMS file contained useful information. Twenty-four respondents reported listening to the file decreased their distress. Most responded that they either did not have distress or the TIMS file did not change their distress. Of concern, 3 people reported that listening to the file increased their distress. Importantly, most respondents reported feeling greater empathy for the patient after listening (53%) and most reported listening improved their communication with family members (63%, n = 9/13). Qualitative analysis revealed most participants had positive impressions about TIMS. We conclude that empathy and communication were both improved with use of the 4-question TIMS recording.

Urbanization reduces diversity, simplifies community and filter bird species based on their functional traits in a tropical city.

Understanding how organisms are coping with major changes imposed by urban intensification is a complex task. In fact, our understanding of the impacts of urbanization on biodiversity is scarce in the global south compared to the north. In this study, we evaluated how bird communities are affected by impact of urban intensification in a tropical city. Thus, we assessed whether increased urban intensification 1) jeopardizes bird diversity (taking into account taxonomic-TD, phylogenetic-PD, and functional-FD dimensions), 2) drives changes in bird community composition and enables the detection of indicator species of such impact, and 3) leads to changes in bird functional traits linked to reproduction, resource acquisition, and survival. We found that urban intensification has a direct impact on the bird community, reducing all three types of diversity. Communities in areas of greater urban intensity are represented by fewer species, and these species are PD and FD less distinct. In addition, we detected at least ten species of areas of lower urban intensity that proved to be more sensitive to urban intensification. With regard to bird traits, we found no significant responses from reproductive, habitat use and feeding variables. Body weight and tail length were the only variables with significant results, with higher urbanization intensity areas selecting for species with lower weights and longer tails. Given the global biodiversity loss we are observing, this information can guide urban managers and planners in designing urban landscapes to maintain biodiversity in cities.

Should Researchers Destroy Audio or Video Recordings?

It is a common practice in qualitative research to transcribe audio or video files from interviews or focus groups and then destroy the files at some future time, usually after validating the transcript or concluding the research. We argue that it is time to rethink this practice and that retention of original qualitative data-including audio and video recordings-should be the default stance in most cases.

Is the patient speaking or the nurse? Automatic speaker type identification in patient-nurse audio recordings.

OBJECTIVES: Patient-clinician communication provides valuable explicit and implicit information that may indicate adverse medical conditions and outcomes. However, practical and analytical approaches for audio-recording and analyzing this data stream remain underexplored. This study aimed to 1) analyze patients' and nurses' speech in audio-recorded verbal communication, and 2) develop machine learning (ML) classifiers to effectively differentiate between patient and nurse language. MATERIALS AND METHODS: Pilot studies were conducted at VNS Health, the largest not-for-profit home healthcare agency in the United States, to optimize audio-recording patient-nurse interactions. We recorded and transcribed 46 interactions, resulting in 3494 "utterances" that were annotated to identify the speaker. We employed natural language processing techniques to generate linguistic features and built various ML classifiers to distinguish between patient and nurse language at both individual and encounter levels. RESULTS: A support vector machine classifier trained on selected linguistic features from term frequency-inverse document frequency, Linguistic Inquiry and Word Count, Word2Vec, and Medical Concepts in the Unified Medical Language System achieved the highest performance with an AUC-ROC = 99.01 ± 1.97 and an F1-score = 96.82 ± 4.1. The analysis revealed patients' tendency to use informal language and keywords related to "religion," "home," and "money," while nurses utilized more complex sentences focusing on health-related matters and medical issues and were more likely to ask questions. CONCLUSION: The methods and analytical approach we developed to differentiate patient and nurse language is an important precursor for downstream tasks that aim to analyze patient speech to identify patients at risk of disease and negative health outcomes.

Investigating the peer Mentor-Mentee relationship: characterizing peer mentorship conversations between people with spinal cord injury.

PURPOSE: This study aimed to: (1) develop a coding manual to characterize topics discussed and conversation techniques used during peer mentorship conversations between people with spinal cord injury (SCI); (2) assess the reliability of the manual; and (3) apply the manual to characterize conversations. MATERIALS/METHODS: The study was conducted in partnership with three Canadian provincial SCI organizations. Twenty-five phone conversations between SCI peer mentors and mentees were audio-recorded and transcribed verbatim. Ten transcripts were inductively analyzed to develop a coding manual identifying topics and techniques used during the conversations. Inductive technique codes were combined and deductively linked to motivational interviewing and behaviour change techniques. Two coders independently applied the coding manual to all transcripts. Code frequencies were calculated. RESULTS: The coding manual included 14 topics and 31 techniques. The most frequently coded topics were personal information, recreational programs, and chronic health services for mentors and mentees. The most frequently coded techniques were giving personal information, social smoothers, and closed question for mentors; and giving personal information, social smoothers, and sharing perspective for mentees. CONCLUSION: This research provides insights into topics and techniques used during real-world peer mentorship conversations. Findings may be valuable for understanding and improving SCI peer mentorship programs.Implications for RehabilitationSCI peer mentorship conversations address a wide range of rehabilitation topics ranging from acute care to living in the community.Identification of the topics discussed, and techniques used in SCI peer mentorship conversations can help to inform formalized efforts to train and educate acute and community-based rehabilitation professionals.Identifying commonly discussed topics in SCI peer mentorship conversation may help to ensure that peer mentors are equipped with the necessary knowledge and resources, or the development of those resources be prioritized.Developing a method to characterize the topics discussed and techniques used during SCI peer mentorship conversations may aid in designing methods to evaluate how rehabilitation professionals provide support to people with SCI.

ArcticBirdSounds: An open-access, multiyear, and detailed annotated dataset of bird songs and calls.

Tracking biodiversity shifts is central to understanding past, present, and future global changes. Recent advances in bioacoustics and the low cost of high-quality automatic recorders are revolutionizing studies in biogeography and community and behavioral ecology with a robust assessment of phenology, species occurrence, and individual activity. This large volume of acoustic recordings has recently generated a plethora of datasets that can now be handled automatically, mostly via big data methods such as deep learning. These approaches need high-quality annotations to classify and detect recorded sounds efficiently. However, very few strongly annotated datasets-that is, with detailed information on start and end time of each vocalization-are openly accessible to the public. Moreover, these datasets mostly cover temperate species and are usually limited to a single year of recordings. Here, we present ArcticBirdSounds, the first open-access, multisite, and multiyear strongly annotated dataset of arctic bird vocalizations. ArcticBirdSounds offers 20 h of annotated recordings over 2 years (2018, 2019), taken from 15 distinct plots within six locations across the Arctic, from Alaska to Greenland. Recordings cover the arctic vertebrates' breeding period and are evenly spaced during the day; they capture most species breeding there with 12,933 temporal annotations in 49 classes of sounds. While these data can be used for many pressing ecological questions, it is also a unique resource for methodological development to help meet the challenges of fast ecosystem transformations such as those happening in the Arctic. All data, including audio files, annotation files, and companion spreadsheets, are available in an Open Science Framework repository published under a CC BY 4.0 License.

A closer look at yoga nidra- early randomized sleep lab investigations.

OBJECTIVES: We aimed to examine trial feasibility plus physiological and psychological effects of a guided meditation practice, Yoga Nidra, in adults with self-reported insomnia. METHODS: Twenty-two adults with self-reported insomnia were recruited to attend two visits at our research center. At Visit 1 (V1), participants were asked to lie quietly for ninety minutes. The primary outcome was change in electroencephalography (EEG). Heart rate variability (HRV), respiratory rate and self-reported mood and anxiety were also measured. At Visit 2 (V2), the same protocol was followed, except half of participants were randomized to practice Yoga Nidra for the first 30-min. RESULTS: There were no between-group changes (V1-V2) in alpha EEG power at O1 (Intervention: 13 ± 70%; Control: -20 ± 40%), HRV or sleep onset latency in response to Yoga Nidra. Respiratory rate, however, showed statistically significant difference between groups (Yoga Nidra -1.4 breaths per minute (bpm) change during and - 2.1 bpm afterwards vs. Control +0.2 bpm during and + 0.4 bpm after; p = .03 for both during and after). The intervention displayed good acceptability (well-tolerated) and credibility (perceived benefit ratings) with implementation success (target sample size reached; 5% dropout rate). CONCLUSIONS: This preliminary clinical trial provides early evidence that Yoga Nidra is a well-tolerated, feasible intervention for adults reporting insomnia. Decreased respiratory rate in response to Yoga Nidra needs to be confirmed in more definitive studies. TRIAL REGISTRATION INFORMATION: This trial was registered on ClinicalTrials.gov as "A Closer Look at Yoga Nidra: Sleep Lab Analyses" (NCT#03685227).

Including migrant oncology patients in research: A multisite pilot randomised controlled trial testing consultation audio-recordings and question prompt lists.

Background: Oncology patients who are migrants or refugees face worse outcomes due to language and communication barriers impacting care. Interventions such as consultation audio-recordings and question prompt lists may prove beneficial in mediating communication challenges. However, designing robust research inclusive of patients who do not speak English is challenging. This study therefore aimed to: a) pilot test and assess the appropriateness of the proposed research design and methods for engaging migrant populations, and b) determine whether a multi-site RCT efficacy assessment of the communication intervention utilising these methods is feasible. Methods: This study is a mixed-methods parallel-group, randomised controlled feasibility pilot trial. Feasibility outcomes comprised assessment of: i) screening and recruitment processes, ii) design and procedures, and iii) research time and costing. The communication intervention comprised audio-recordings of a key medical consultation with an interpreter, and question prompt lists and cancer information translated into Arabic, Greek, Traditional, and Simplified Chinese. Results: Assessment of feasibility parameters revealed that despite barriers, methods utilised in this study supported the inclusion of migrant oncology patients in research. A future multi-site RCT efficacy assessment of the INFORM communication intervention using these methods is feasible if recommendations to strengthen screening and recruitment are adopted. Importantly, hiring of bilingual research assistants, and engagement with community and consumer advocates is essential. Early involvement of clinical and interpreting staff as key stakeholders is likewise recommended. Conclusion: Results from this feasibility RCT help us better understand and overcome the challenges and misconceptions about including migrant patients in clinical research.

Audio Recording Patient-Nurse Verbal Communications in Home Health Care Settings: Pilot Feasibility and Usability Study.

BACKGROUND: Patients' spontaneous speech can act as a biomarker for identifying pathological entities, such as mental illness. Despite this potential, audio recording patients' spontaneous speech is not part of clinical workflows, and health care organizations often do not have dedicated policies regarding the audio recording of clinical encounters. No previous studies have investigated the best practical approach for integrating audio recording of patient-clinician encounters into clinical workflows, particularly in the home health care (HHC) setting. OBJECTIVE: This study aimed to evaluate the functionality and usability of several audio-recording devices for the audio recording of patient-nurse verbal communications in the HHC settings and elicit HHC stakeholder (patients and nurses) perspectives about the facilitators of and barriers to integrating audio recordings into clinical workflows. METHODS: This study was conducted at a large urban HHC agency located in New York, United States. We evaluated the usability and functionality of 7 audio-recording devices in a laboratory (controlled) setting. A total of 3 devices-Saramonic Blink500, Sony ICD-TX6, and Black Vox 365-were further evaluated in a clinical setting (patients' homes) by HHC nurses who completed the System Usability Scale questionnaire and participated in a short, structured interview to elicit feedback about each device. We also evaluated the accuracy of the automatic transcription of audio-recorded encounters for the 3 devices using the Amazon Web Service Transcribe. Word error rate was used to measure the accuracy of automated speech transcription. To understand the facilitators of and barriers to integrating audio recording of encounters into clinical workflows, we conducted semistructured interviews with 3 HHC nurses and 10 HHC patients. Thematic analysis was used to analyze the transcribed interviews. RESULTS: Saramonic Blink500 received the best overall evaluation score. The System Usability Scale score and word error rate for Saramonic Blink500 were 65% and 26%, respectively, and nurses found it easier to approach patients using this device than with the other 2 devices. Overall, patients found the process of audio recording to be satisfactory and convenient, with minimal impact on their communication with nurses. Although, in general, nurses also found the process easy to learn and satisfactory, they suggested that the audio recording of HHC encounters can affect their communication patterns. In addition, nurses were not aware of the potential to use audio-recorded encounters to improve health care services. Nurses also indicated that they would need to involve their managers to determine how audio recordings could be integrated into their clinical workflows and for any ongoing use of audio recordings during patient care management. CONCLUSIONS: This study established the feasibility of audio recording HHC patient-nurse encounters. Training HHC nurses about the importance of the audio-recording process and the support of clinical managers are essential factors for successful implementation.

Parents' Interactive Problem-Solving Behavior and Emotion Studied With Audio Compared With Video Source.

BACKGROUND AND PURPOSE: Parents' communication and problem-solving interaction with each other and with clinicians influences the caregiving of infants with a chronic health problem, making in-depth study of this interaction critical for design of interventions to support caregiving. This study, however, has been severely limited by lack of observational methods that can be applied in home, clinic and community settings. The Iowa Family Interaction Rating Scales provide comprehensive description of communicative and problem-solving behavior and emotion, but have only been applied to video-recorded interaction. Audio recording, in contrast to video recording, has the advantage of being unobtrusive, readily accessible, and generally acceptable, increasing the opportunity for focused examination and intervention of parents' interaction with each other or with clinicians. Our study objective was to examine the agreement of scores obtained on parents' interactive problem-solving behavior coded with the Iowa Family Interaction Rating Scales using an audio-recorded source for coding compared with coding from a video-recorded source. METHOD: In secondary analysis, audio-recordings were derived from video recordings of 15 parent-parent interactions. Audio recordings were created and coded blind of the original video recording and coding. RESULTS: Using Gwet's AC1 coefficient, agreement was at least moderate (0.61 - 0.80) for 69.1% of paired codes, signifying reliability of coding from audio recording for most codes. IMPLICATIONS FOR PRACTICE: Selected Iowa Family Interaction Rating Scales can be used with acceptable reliability for coding parents' interactive problem-solving behavior from audio source, advancing the study of parent interactive-problem solving behavior and potentially parents' problem solving with clinicians.

Using big data from long-form recordings to study development and optimize societal impact.

Big data are everywhere. In this chapter, we focus on one source: long-form, child-centered recordings collected using wearable technologies. Because these recordings are simultaneously unobtrusive and encompassing, they may be a breakthrough technology for clinicians and researchers from several diverse fields. We demonstrate this possibility by outlining three applications for the recordings-clinical treatment, large-scale interventions, and language documentation-where we see the greatest potential. We argue that incorporating these recordings into basic and applied research will result in more equitable treatment of patients, more reliable measurements of the effects of interventions on real-world behavior, and deeper scientific insights with less observational bias. We conclude by outlining a proposal for a semistructured online platform where vast numbers of long-form recordings could be hosted and more representative, less biased algorithms could be trained.