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DiversityOne: A Multi-Country Smartphone Sensor Dataset for Everyday Life Behavior Modeling

T0 review · 3 major / 5 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read DiversityOne releases a four-week, eight-country dataset pairing raw smartphone sensor streams from 782 college students with more than 350,000 self-reports, enabling cross-country studies of how behavior-inference models generalize.

desk verdict A genuinely valuable multi-country sensing corpus, but the 'publicly available' claim is contradicted by the paper's own access-control section, and the participant count needs reconciling. read the letter →

arxiv 2502.03347 v1 pith:I7JWJSIT submitted 2025-02-05 cs.CY cs.SI

Matteo Busso , Andrea Bontempelli , Leonardo Javier Malcotti , Lakmal Meegahapola , Peter Kun , Shyam Diwakar , Chaitanya Nutakki , Marcelo Dario Rodas Britez
show 18 more authors
Hao Xu Donglei Song Salvador Ruiz Correa Andrea-Rebeca Mendoza-Lara George Gaskell Sally Stares Miriam Bidoglia Amarsanaa Ganbold Altangerel Chagnaa Luca Cernuzzi Alethia Hume Ronald Chenu-Abente Roy Alia Asiku Ivan Kayongo Daniel Gatica-Perez Amalia de Götzen Ivano Bison Fausto Giunchiglia
This is my paper · ORCID
classification cs.CYcs.SI
keywords smartphonesensingmobilemulti-countrydatasetself-reportsbehaviormodelingcollegestudentsdomaingeneralizationsocialpractices
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper presents DiversityOne, a publicly released dataset of everyday life behavior from 782 college students in eight countries over four weeks, combining raw data from 26 smartphone sensor modalities with more than 350,000 in-situ self-reports. The authors aim to overcome a bottleneck in mobile sensing research: existing public datasets are small, sensor-limited, and concentrated in the Global North, so models trained on them may not transfer across countries and cultures. DiversityOne is designed so that researchers can ask cross-country questions about behavior inference, domain adaptation, and model personalization. The paper's own validation shows pronounced cross-site differences in sleep, eating, mood, and app-use patterns, and initial studies on the data indicate that country-specific and individually adapted models outperform generic multi-country ones. If the dataset works as claimed, it gives the field a reusable resource for testing whether mobile behavior models can generalize beyond the populations they were trained on.

What carries the argument

The load-bearing artifact is the dataset itself, produced by a three-part protocol. An invitation questionnaire reached over 18,000 students; a subset of 782 installed a custom logging app (iLog) that passively recorded 26 smartphone sensor modalities at high frequency and prompted in-situ self-reports through morning/evening diaries, half-hourly time diaries, and snack diaries. The protocol operationalizes culture as social practices (material, competence, meaning) and follows HETUS-style time-use diary standards, combining them with experience sampling. To preserve comparability across sites, the central protocol was translated, back-translated, and adapted locally, with an offline mode for regions where cloud notification services or stable connectivity were unavailable. The dataset's modular organization into connectivity, environment, motion, position, app-usage, and device-usage bundles, plus its two GPS anonymization versions (RoundDown and POI), is what lets researchers flexibly assemble the data for behavior inference while respecting privacy constraints.

What would settle it

A re-analysis showing that the adapted self-report scales produce systematically different response styles across countries—for example, if controlling for country-specific scale use removes the cross-country differences in model performance—would falsify the claim that the dataset supports fair cross-country behavior comparisons.

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Extended reading notes

Core claim

The central claim is that DiversityOne is one of the largest and most geographically diverse public datasets that combine questionnaire responses from more than 18,000 students with four weeks of passive smartphone sensing and intensive longitudinal self-reports from 782 participants. Every site followed a shared protocol built on standardized time diaries and experience-sampling questions, with local translations and adaptations; the paper argues that these adaptations preserve enough comparability for meaningful cross-country analysis while capturing genuine behavioral diversity. The dataset ships raw, granular sensor streams (accelerometer, gyroscope, GPS, Bluetooth, WiFi, app usage, notifications, screen and battery events, and more), organized into thematic bundles, alongside structured time diaries. Validation analyses document substantial cross-country variation in daily rhythms and in the distribution of moods and activities, and earlier studies using the data show that country-specific or hybrid personalized models generally beat generic multi-country models. The contribution is therefore not a new algorithm but a new empirical resource with a field-tested collection protocol, intended to make cross-country generalization research in mobile sensing feasible.

Load-bearing premise

That self-reported answers about mood, activity, and context remain comparable across countries after translation and local adaptation, even though the questions, incentives, and recruitment differed by site.

Editorial extensions

If this is right

  • Researchers can benchmark behavior-inference models across eight countries, including Global South sites, testing whether models trained in one country transfer to another.
  • The dataset enables domain adaptation and domain generalization experiments on raw multimodal time series rather than only pre-computed features.
  • The rich self-report stream allows study of label shift and the reliability of self-reported ground truth across cultural contexts.
  • Initial results cited in the paper suggest country-specific or hybrid personalized models will systematically outperform generic multi-country models, guiding deployment choices in real-world applications.
  • The documented protocol and lessons learned give future multi-country studies a template for ethical, privacy-compliant, adaptive data collection.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: the dataset's claim to support fair cross-country comparison depends on the equivalence of the self-report instruments; a promising test would be to compare the psychometric properties of the mood and activity scales across sites before using them as ground truth.
  • The ranking of apps and daily rhythms already hints at strongly culture-specific behaviors; models that explicitly encode country or cultural context may benefit more than generic transfer methods.
  • The paper's acknowledgment that self-reported labels are only a silver standard suggests that objective sensor-based labels, such as step count or location patterns, could serve as more stable anchors for validation in future work.
  • Releasing raw sensor data rather than only pre-processed features opens the door to self-supervised pretraining on mobile sensing, an area the paper notes is largely unexplored.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper introduces DiversityOne, a multi-country smartphone sensing dataset collected from college students in eight countries over four weeks, combining passive sensor data from 26 modalities with intensive longitudinal self-reports and questionnaires from over 18,000 respondents. It describes the study design, adaptation procedures, privacy and anonymization pipeline, data catalog, and lessons learned from previous analyses using the dataset. The stated contributions are the public release of this dataset, a detailed description of the cross-country data collection methodology, and a synthesis of findings and recommendations for future smartphone sensing studies.

Significance. If the availability and scale claims hold, DiversityOne would be a valuable community resource for studying cross-country generalization and domain adaptation in mobile sensing, areas that have been limited by the lack of diverse, raw sensor datasets. The paper's strengths include the unusually wide geographic coverage spanning Global North and South, the documentation of local adaptations across sites, the inclusion of raw sensor data rather than only preprocessed features, and the explicit discussion of ethical and privacy procedures. Several prior publications already use subsets of the data, which provides some evidence of usability. However, the central claim that the dataset is publicly available is directly contradicted by the access-control description, and the participant counts are inconsistent between the abstract and the detailed tables; these issues must be resolved before the contribution can be assessed accurately.

major comments (3)
  1. [Abstract, Contribution 1, Table 1, Section 3.7.2, Section 5.3] The paper's headline claim that DiversityOne is 'publicly released' and 'publicly available' is inconsistent with the access procedure described in Section 5.3 and the explicit statement in Section 3.7.2 that 'the dataset is not publicly available online and can only be accessed under specific conditions.' The request process requires affiliation with a research institution, submission and approval of a research proposal, signing of a Terms and License Agreement that prohibits redistribution and public sharing, and a ban on re-identification attempts. This is controlled access, not public release. The comparison in Table 1 is specifically about 'Public available datasets,' so the characterization materially affects the paper's central novelty claim. Please either revise the abstract and Contribution 1 to describe the dataset as available under controlled conditions, or provide a genuinely open release (including derived benchmark datasets, which Section 5.1 currently defers indefinitely).
  2. [Abstract, Contribution 1, Table 4] The abstract and Contribution 1 state that the dataset contains 'data from 782 college students' and 'passive smartphone sensor data and self-reports from 782 participants,' but Table 4 reports 782 students who signed into the iLog app and only 666 who actively contributed data. The numbers 666 and 782 are used in different places, and the text should be consistent about which count is being reported. Please correct the abstract and Contribution 1 to '782 signed participants' or '666 participants with iLog data,' and add a sentence in Section 4.1 explaining the difference between signing in and providing at least one data record.
  3. [Section 3.1.1, Section 3.2.3, Section 6.2.5] The cross-country comparability of the self-report labels is a load-bearing assumption for the dataset's stated purpose of enabling cross-country behavior modeling and generalization. Section 3.1.1 and Section 3.2.3 describe substantial local adaptations: questionnaire items were modified or removed (e.g., sexuality and religiosity items), response options were changed (e.g., nationality lists, foods, apps), and self-report prompt frequencies differed (IPICYT used half-hour notifications throughout). Section 6.2.5 further acknowledges that self-reported labels are only a 'silver standard.' The paper should provide a per-site summary of which items and response options were adapted, and ideally report basic measurement invariance or per-country response-distribution statistics, so that users can judge whether country differences reflect behavior or instrument differences. Without this, the cross-country generalization analyses that the dataset advertises rest on an unverified comparability assumption.
minor comments (5)
  1. [Section 3.7.2] There is a typo in the sentence 'Relevant sens r file columns were anonymized'; it should read 'sensor file columns.'
  2. [Section 6.2.5] The phrase 'after transer learning' contains a typo and should read 'after transfer learning.'
  3. [Section 6.3] The sentence 'This second will be released soon, hopefully within 2024' is awkward and outdated for a 2025 publication; it should be removed or updated with the actual release status.
  4. [Section 6.3] The text mentions a comparison with data collected 'in 1918' in a study by [40]; this appears to be a typo for 2018 and should be corrected.
  5. [Table 3] The incentive table uses dashes for AMRITA and IPICYT; a footnote should clarify whether no incentives were offered or whether the information is unavailable.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation chain: DiversityOne is a data-release and collection-protocol paper, not a fitted model, so its central claims do not reduce to their inputs; the only flagged issue is a non-circular internal contradiction between the claimed 'public release' and the gated access described in Sections 3.7.2 and 5.3.

full rationale

This paper's central claims are descriptive: that a multi-country dataset was collected, that it contains 26 sensor modalities and 350K+ self-reports from college students, and that it is released to researchers. There are no fitted parameters, no predictive equations, and no derivation whose output is equivalent to its input by construction. The paper describes the collection protocol, recruitment, translation/adaptation, ethics/anonymization, and presents descriptive statistics; claims such as 'one of the largest and most diverse publicly available datasets' rest on the sample size and country count in Table 1, not on a self-referential definition. Prior studies by the same research group that used DiversityOne (e.g., [3, 80, 82]) are cited as evidence of the dataset's utility, but they are supporting illustrations rather than load-bearing premises for the dataset's existence or its descriptive properties; no uniqueness theorem or ansatz is imported from self-citations. One non-circular issue is flagged and weighed: the abstract and Contribution 1 say the dataset is 'publicly available,' but Section 3.7.2 states verbatim 'the dataset is not publicly available online and can only be accessed under specific conditions (see Section 5),' and Section 5.3 describes an approval, research-affiliation, and non-redistribution license. This is a genuine internal contradiction about the headline availability claim, but it is not a circular derivation and therefore does not raise the circularity score. Similarly, Table 4 reports 782 'iLog signed' participants versus 666 with 'iLog data,' which is an internal consistency concern about the stated 782-participant figure, not a circularity. Overall, the paper's derivation chain is self-contained and no circular step meeting the required evidence standard is present.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No free parameters are fitted because this is a data release. The analysis depends on domain assumptions about the validity of self-reports, cross-site comparability, anonymization sufficiency, and the chosen sociological framework.

assumptions (4)
  • domain assumption Self-reported time diaries and questionnaires are valid ground truth for behavior despite cultural differences in reporting.
    Section 3.2.1 and Section 6.2.5 acknowledge labels are silver standard; the dataset's utility depends on this.
  • domain assumption Local adaptations and translations preserve cross-site comparability.
    Sections 3.1.1 and 3.6 describe changes to items, response options, and recruitment; comparability is assumed for cross-country analysis.
  • domain assumption Anonymization and gated access sufficiently protect participants against re-identification.
    Section 3.7.2 says re-identification may still be possible; Section 5.3 restricts access to managed requests.
  • domain assumption Social practice theory provides a meaningful operationalization of culture for this study.
    Section 2.4.1 introduces the framework and it guides questionnaire design and interpretation.

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Cite this review

Pith. "Pith review of DiversityOne: A Multi-Country Smartphone Sensor Dataset for Everyday Life Behavior Modeling." pith.science (2026). https://pith.science/paper/I7JWJSIT

@misc{pith2026250203347,
  author       = {Pith},
  title        = {Pith review of: DiversityOne: A Multi-Country Smartphone Sensor Dataset for Everyday Life Behavior Modeling},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/I7JWJSIT}},
  note         = {Machine review of arXiv:2502.03347}
}
read the original abstract

Understanding everyday life behavior of young adults through personal devices, e.g., smartphones and smartwatches, is key for various applications, from enhancing the user experience in mobile apps to enabling appropriate interventions in digital health apps. Towards this goal, previous studies have relied on datasets combining passive sensor data with human-provided annotations or self-reports. However, many existing datasets are limited in scope, often focusing on specific countries primarily in the Global North, involving a small number of participants, or using a limited range of pre-processed sensors. These limitations restrict the ability to capture cross-country variations of human behavior, including the possibility of studying model generalization, and robustness. To address this gap, we introduce DiversityOne, a dataset which spans eight countries (China, Denmark, India, Italy, Mexico, Mongolia, Paraguay, and the United Kingdom) and includes data from 782 college students over four weeks. DiversityOne contains data from 26 smartphone sensor modalities and 350K+ self-reports. As of today, it is one of the largest and most diverse publicly available datasets, while featuring extensive demographic and psychosocial survey data. DiversityOne opens the possibility of studying important research problems in ubiquitous computing, particularly in domain adaptation and generalization across countries, all research areas so far largely underexplored because of the lack of adequate datasets.

Figures

Figures reproduced from arXiv: 2502.03347 by the authors.

Figure 1
Figure 1. Study set-up and data collection process. Invitation, Selection, and Closing procedures were done using the LimeSurvey [PITH_FULL_IMAGE:figures/full_fig_p010_1.png] view at source ↗
Figure 2
Figure 2. The iLog app adopted for intensive longitudinal survey and sensor data collection. [PITH_FULL_IMAGE:figures/full_fig_p013_2.png] view at source ↗
Figure 3
Figure 3. Distribution of participants based on the number of [PITH_FULL_IMAGE:figures/full_fig_p021_3.png] view at source ↗
Figures from the paper (4 more)
Figure 5
Figure 5. Figure 5: Average percentage of participants that provided sensor data for each site. [PITH_FULL_IMAGE:figures/full_fig_p022_5.png]
Figure 6
Figure 6. Figure 6: Proportion of time diary reports during different hours of the day. [PITH_FULL_IMAGE:figures/full_fig_p023_6.png]
Figure 7
Figure 7. Figure 7: Ranking comparison of the three most consumed foods and snacks. [PITH_FULL_IMAGE:figures/full_fig_p023_7.png]
Figure 8
Figure 8. Figure 8: Mood ratings distribution. Figure 7a). Interestingly, water consistently appears among the top-reported drinks during snack breaks across all sites, as illustrated in Figure 7b. Mood reporting adds an intriguing layer to this study, allowing researchers to investigate …

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Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.