Community Standards Research Articles

Enhancing the Social Network: Multimodal Treatment for Comorbid Borderline Personality Disorder and Alcohol Use Disorder.

More than half of all people with borderline personality disorder will develop alcohol use disorder in their lifetime. These disorders mutually reinforce each other, with a higher risk for treatment failure and poor outcomes, including suicide, yet no widely available treatments have been found to be effective for both diagnoses concurrently, leaving patients and clinicians alike stranded between two clinical domains that rarely overlap despite shared features. In the absence of alternatives, good psychiatric management (GPM) capitalizes on standard-of-care interventions using generic clinical tools that do not require specialization. In an effort to broaden and stabilize the social networks of connections for patients with interpersonal hypersensitivity, GPM relies on a multimodal approach that combines the indicated pharmacological and psychosocial interventions for the treatment of alcohol use disorder with a common-factors approach for borderline personality disorder. This multimodal approach emphasizes psychoeducation, social rehabilitation, management of suicidality, and active management of these frequently comorbid conditions. In this article, the authors describe GPM's strategy of stabilizing and broadening the patient's social network to target the core interpersonal and stress hypersensitivity. To do this, clinicians can use interventions for significant others combined with empirically supported and widely available mutual-help groups, such as Alcoholics Anonymous, that structure and regulate relational instabilities with community norms, standards, roles, and procedures. GPM also promotes family interventions for both conditions to reduce conflict and increase support within existing relationships, thereby strengthening patients' capacity to work on their sobriety and borderline personality disorder by mitigating aloneness and its effects.

Seatizen Atlas: a collaborative dataset of underwater and aerial marine imagery

Citizen Science initiatives have a worldwide impact on environmental research by providing data at a global scale and high resolution. Mapping marine biodiversity remains a key challenge to which citizen initiatives can contribute. Here we describe a dataset made of both underwater and aerial imagery collected in shallow tropical coastal areas by using various low cost platforms operated either by citizens or researchers. This dataset is regularly updated and contains >1.6 M images from the Southwest Indian Ocean. Most of images are geolocated, and some are annotated with 51 distinct classes (e.g. fauna, and habitats) to train AI models. The quality of these photos taken by action cameras along the trajectories of different platforms, is highly heterogeneous (due to varying speed, depth, turbidity, and perspectives) and well reflects the challenges of underwater image recognition. Data discovery and access rely on DOI assignment while data interoperability and reuse is ensured by complying with widely used community standards. The open-source data workflow is provided to ease contributions from anyone collecting pictures.

Was That Use of Force Reasonable? The Public Doesn’t Know

There is a gap between what community members believe is reasonable use of force and what the law defines as reasonable. This divide has led to significant consequences for police departments across the country and society as a whole. In the current study, community members’ assessments of force reasonableness aligned with the legal standard on use of force only about half of the time, partly because legally irrelevant factors influenced their assessments. To reduce the divide between community and legal standards on use of force, police leaders can provide community education, revise departmental policy, and/or deliver de-escalation training.

Investigating the Barriers to Adoption of Renewable Energy Systems in Residential Buildings in Kuwait

This study investigates the barriers to adopting renewable energy (RE) systems in residential buildings in Kuwait, with a focus on technical, financial, environmental, and social challenges. Through a structured survey administered to 217 respondents from varied demographics, the study highlights key obstacles such as high initial costs, limited awareness of service providers, environmental challenges, and social influences. Technical barriers, including maintenance complexities and insufficient skilled personnel, further hinder adoption. Despite acknowledging the benefits of RE systems, respondents express concerns over system reliability and compatibility with Kuwait's extreme climatic conditions, such as high temperatures and frequent dust storms. Financial constraints, particularly the lack of government incentives, exacerbate these challenges. Social factors, including community standards and peer influence, play a nuanced role, with public awareness campaigns identified as critical for fostering adoption. The findings emphasize the need for targeted awareness initiatives, financial incentives, and advanced technologies tailored to Kuwait’s climate. This study provides actionable insights for policymakers, developers, and stakeholders to address these barriers effectively and promote RE adoption. By overcoming these challenges, Kuwait can enhance its energy sustainability, contribute to global climate goals, and diversify its energy portfolio in alignment with sustainable development objectives.

Review of methodology and re-analysis of lipidomic data focusing on specialised pro-resolution lipid mediators (SPMs) in a human model of resolving inflammation.

Using a model of UV-killed E. coli driven dermal inflammation in healthy human volunteers, we originally reported that following inflammatory resolution there was infiltration of macrophages, which, through prostanoids including prostaglandin (PG) E2, imprints long-term tissue immunity. In addition to the prostanoids, data on levels of Specialised Pro-Resolution Lipid Mediators (SPMs) throughout inflammatory onset, resolution and post-resolution phases of this model were presented, but as illustrations rather than as primary data. Therefore, in response to a request for increased transparency, a subset of the original data from our human UV-killed E. coli model was re-analysed by two experts from an independent laboratory alongside a review of the methodology used. The prostanoids were detected robustly following re-analysis but the areas of the chromatographic peaks of the SPM lipid mediators were too small to yield amounts that could be reliably detected and/or quantified using community standards. Importantly, with prostanoids including PGE2 being robustly detected, this re-analysis does not alter the original report that post-resolution PGs imprint tissue immunity. Here we show the outcome of this re-analysis and review the biology surrounding SPMs as a result.

Reprocessing of eXpendable BathyThermograph (XBT) profiles from the Ligurian and Tyrrhenian seas over the time period 1999–2019 with a full metadata upgrade

Abstract. The advent of open science and the United Nations Decade of Ocean Science for Sustainable Development are revolutionizing the ocean-data-sharing landscape for an efficient and transparent ocean information and knowledge generation. This blue revolution raised awareness on the importance of metadata and community standards to activate interoperability of the digital assets (data and services) and guarantee that data-driven science preserves provenance, lineage and quality information for its replicability. Historical data are frequently not compliant with these criteria, lacking metadata information that was not retained, crucial at the time of data generation and further ingestion into marine data infrastructures. The present data review is an example attempt to fill this gap through a thorough data reprocessing starting from the original raw data and operational log sheets. The data gathered using XBT (eXpendable BathyThermograph) probes during several monitoring activities in the Tyrrhenian and Ligurian seas between 1999 and 2019 have first been formatted and standardized according to the latest community best practices and all available metadata have been inserted, including calibration information never applied, uncertainty specification and bias correction from Cheng et al. (2014). Secondly, a new automatic quality control (QC) procedure has been developed and a new interpolation scheme applied. The reprocessed (REP) dataset has been compared to the data version, presently available from the SeaDataNet (SDN) data access portal, processed according to the pioneering work of Manzella et al. (2003) conducted in the framework of the European Union Mediterranean Forecasting System Pilot Project (Pinardi et al., 2003). The comparison between REP and SDN datasets has the objective to highlight the main differences derived from the new data processing process. The maximum discrepancy among the REP and SDN data versions always resides within the surface layer (REP profiles are warmer than SDN ones) until 150 m depth generally when the thermocline settles (from June to November). The overall bias and root mean square difference are equal to 0.002 and 0.041 °C, respectively. Such differences are mainly due to the new interpolation technique (Barker and McDougall, 2020) and the application of the calibration correction in the REP dataset. The REP dataset (Reseghetti et al., 2024; https://doi.org/10.13127/rep_xbt_1999_2019.2) is available and accessible through the INGV (Istituto Nazionale di Geofisica e Vulcanologia, Bologna) ERDDAP (Environmental Research Division's Data Access Program) server, which allows for machine-to-machine data access in compliance with the FAIR (findable, accessible, interoperable and reusable) principles (Wilkinson et al., 2016).

How to Apply the Notice and Action Requirements under Art. 16(6) sentence 1 DSA — Which action actually?

Abstract Art. 16 DSA and its framework for notice and action mechanisms are one of the DSA’s central due diligence obligations for hosting services, including online-platforms. Such services must put in place user-friendly reporting mechanisms, so reporters can easily notify them about allegedly illegal content. Following proper notification, Art. 16(6) S. 1 DSA obliges providers to make a decision in a “timely, diligent, non-arbitrary and objective manner”. What does that actually mean? This paper addresses these issues involved: First, what is the interplay of Art. 16(6) S. 1 DSA with other, more traditional takedown obligations? Can IP rights holders, based on Art. 16 DSA, initiate a Digital Services Coordinator (DSC) to act against, e.g., copyright violations? Does Art. 16(6) S. 1 DSA harmonize national tort law stay-down obligations? What triggers Art. 16(6)’s imperative: Every “notice” or only specific ones received through dedicated reporting mechanisms? What is the legal standard for “diligent” decisions: Must platforms enforce their Community Standards, or only relevant laws? Speaking of laws: Must platforms consider any Member State’s laws? And might the enforcement of Art. 16(6) S. 1 DSA through Art. 54 DSA advance civil law remedies against hosting providers?

SAMURAI: shallow analysis of copy number alterations using a reproducible and integrated bioinformatics pipeline.

Shallow whole-genome sequencing (sWGS) offers a cost-effective approach to detect copy number alterations (CNAs). However, there remains a gap for a standardized workflow specifically designed for sWGS analysis. To address this need, in this work we present SAMURAI, a bioinformatics pipeline specifically designed for analyzing CNAs from sWGS data in a standardized and reproducible manner. SAMURAI is built using established community standards, ensuring portability, scalability, and reproducibility. The pipeline features a modular design with independent blocks for data preprocessing, copy number analysis, and customized reporting. Users can select workflows tailored for either solid or liquid biopsy analysis (e.g. circulating tumor DNA), with specific tools integrated for each sample type. The final report generated by SAMURAI provides detailed results to facilitate data interpretation and potential downstream analyses. To demonstrate its robustness, SAMURAI was validated using simulated and real-world data sets. The pipeline achieved high concordance with ground truth data and maintained consistent performance across various scenarios. By promoting standardization and offering a versatile workflow, SAMURAI empowers researchers in diverse environments to reliably analyze CNAs from sWGS data. This, in turn, holds promise for advancements in precision medicine.

Three Problems of Business Addressed by Stakeholder Theory: Insights from the Institutional Economics of John R. Commons

AbstractStakeholder theory is traditionally motivated by the need to reconceptualize three core problems of business: value creation and trade, the ethics of capitalism, and managerial mindsets. We argue that the need for this reconceptualization arises, in part, from the limitations of mainstream economic theory in grasping the evolving business environment. The original institutional economics of John R. Commons offers an alternative body of economic thought that addresses these limitations by replacing the atomistic individualism of mainstream economics with an ontology grounded in collective action. Drawing on Commons’ insights, we demonstrate how the problem of value creation and trade takes on a new meaning of promoting security of expectations through conflict resolution and the maintenance of order. We further reconstruct the problem of the ethics of capitalism by drawing on Commons’ concept of reasonableness, rooted in evolving community standards of morality. Finally, we trace the problem of managerial mindsets to Commons’ category of industrial goodwill, which suggests that managers can deliberately pursue policies that maximize stakeholders’ collaborative attitudes. We conclude that reconstructing these three problems of business through Commons’ categories reveals the deeper institutional economics content embedded within contemporary stakeholder theory.

Community standards and future opportunities for synthetic communities in plant-microbiota research.

Harnessing beneficial microorganisms is seen as a promising approach to enhance sustainable agriculture production. Synthetic communities (SynComs) are increasingly being used to study relevant microbial activities and interactions with the plant host. Yet, the lack of community standards limits the efficiency and progress in this important area of research. To address this gap, we recommend three actions: (1) defining reference SynComs; (2) establishing community standards, protocols and benchmark data for constructing and using SynComs; and (3) creating an infrastructure for sharing strains and data. We also outline opportunities to develop SynCom research through technical advances, linking to field studies, and filling taxonomic blind spots to move towards fully representative SynComs.

The Role of Urban Community Leaders in Local Product Development Using Intellectual Capital: A Case Study of Bangkok, Thailand

This research investigates the role of urban community leaders in leveraging local intellectual capital for the design and development of community products. It examines how these leaders contributed to creating products that fostered self-sufficiency within their communities. Data were collected from 43 communities across 5 sub-districts in Dusit District, Bangkok, involving a total of 453 participants. The study employed a combination of explanatory and interdisciplinary research methods. The findings revealed that community leaders significantly contributed to accumulating intellectual capital for product development. Especially, the highest engagement observed in collective brainstorming sessions. The most actively engaged leaders were female, aged 40 years. In contrast, self-reliant leaders, who typically held bachelor’s degrees, exhibited cautious spending habits and a commitment to community rules and self-reliance without government support. Community leaders collaboratively designed the Dusit Brand logo and proposed a seven-stage process for urban community product development. This process includes incorporating local wisdom, reflecting community identity, fostering public participation, developing local knowledge, exhibiting products, building community reputation and recognition, and achieving community standards. The research highlights that sustainable community products are created through a process of understanding, accessing, and developing the local economy and society, with a focus on enhancing social quality for sustainability.

Predictive Modelling for Sensitive Social Media Contents Using Entropy-FlowSort and Artificial Neural Networks Initialized by Large Language Models

This work offers an integrated methodological framework that integrates the capabilities of large language models (LLMs), rules-based reasoning, multi-criteria sorting, and artificial neural networks (ANN) in developing a predictive model for classifying the intensity of sensitive social media contents. The current literature lacks a holistic consideration of multiple attributes in evaluating social media contents, and the proposed framework intends to bridge such a gap. Three actions constitute the development of the framework. First, LLMs (i.e., GPT4) evaluate the social media contents under a predefined set of attributes, leveraging the power of LLMs in content analytics. Second, rules-based reasoning and multi-criteria sorting (i.e., entropy-FlowSort) determine the categories of social media contents. Lastly, the two previous actions produced a complete dataset that can be used to train a predictive model using ANN to classify sensitive social media contents. With 1100 randomly extracted social media contents and the predefined categories of violations against community standards set by Facebook, the proposed integrated methodology produces an ANN-based classification model with 86.36% prediction accuracy. Comparative analysis using Decision Trees, k-nearest neighbors, Linear Discriminant Analysis, Random Forest, and Naive Bayes classification yields the highest performance of ANN. The predictive model can be used as a decision-support tool to design moderation actions on social media contents.

One Hundred Years of Seeking Respectful Maternity Care: History and Evolution.

Growing awareness of poor maternal health outcomes and maternal health disparities in the United States has heightened urgency around the need to promote Respectful Maternity Care (RMC) as a fundamental tenet of obstetric/midwifery care and standardize efforts to improve safety, eliminate obstetric violence and racism, and optimize health outcomes for all birthing people. The historical context of prior and contemporary perspectives around childbirth influences our understanding of RMC and are shaped by varying scholarly, clinical, and community standards (e.g., religion, human rights, government, public health, midwifery, ethics, activism, and the law), which have changed significantly since the mid-19th century. In this commentary, we share results of a contextual question scoped as part of a larger systematic review of RMC to help inform consensus around a shared definition and development of a metric to standardize delivery and evaluation of RMC. Synthesis of this literature identified landmark historical influences on RMC over the past 100 years, highlighting the multidisciplinary scholarship and historical context influencing the progress toward RMC. Further understanding of this history may also inform policies and guidance for ongoing efforts to center respect and accountability in all aspects of maternity care, with particular attention to populations who are disproportionally impacted by disrespectful care.

The Other Squid Game: How Splatoon 3 Promotes a Culture of Coziness, Inclusivity, and Becoming-Cephalopod

The 2021 Netflix series Squid Game's themes of economic critique are often framed as contradicting the exploitative nature of its ancillary texts like Squid Game: The Challenge (Netflix 2023), a reality series that materially reproduces many of the same hardships that the narrative series ostensibly condemns. This paper argues that this is a product of the series’ emphasis on interactive screen media as a marketing and production tool without accounting for how this modal difference would alter the themes of the series’ narrative. The Nintendo multiplayer shooter Splatoon 3 (2022) serves as a useful example of how these challenges can be overcome within the context of a popular multimedia franchise. This is done through the game's emphasis on an ecological coziness whereby principles of cozy gaming and a cephalopod-like tentacular approach to screen media create a text whose themes and inclusive community standards do not exhibit the contradictions seen in Squid Game.

Mapping interpretations of the law in online content moderation in Germany

Content moderation is a vital condition that online platforms must facilitate, according to the law, to create suitable online environments for their users. By the law, we mean national or European laws that require the removal of content by online platforms, such as EU Regulation 2021/784, which addresses the dissemination of terrorist content online. Content moderation required by these national or European laws, summarised here as ‘the law’, is different from the moderation of pieces of content that is not directly required by law but instead is conducted voluntarily by the platforms. New regulatory requests create an additional layer of complexity of legal grounds for the moderation of content and are relevant to platforms’ daily decisions. The decisions made are either grounded in reasons stemming from different sources of law, such as international or national provisions, or can be based on contractual grounds, such as the platform's Terms of Service and Community Standards. However, how to empirically measure these essential aspects of content moderation remains unclear. Therefore, we ask the following research question: How do online platforms interpret the law when they moderate online content?To understand this complex interplay and empirically test the quality of a platform's content moderation claims, this article develops a methodology that facilitates empirical evidence of the individual decisions taken per piece of content while highlighting the subjective element of content classification by human moderators. We then apply this methodology to a single empirical case, an anonymous medium-sized German platform that provided us access to their content moderation decisions. With more knowledge of how platforms interpret the law, we can better understand the complex nature of content moderation, its regulation and compliance practices, and to what degree legal moderation might differ from moderation due to contractual reasons in dimensions such as the need for context, information, and time.Our results show considerable divergence between the platform's interpretation of the law and ours. We believe that a significant number of platform legal interpretations are incorrect due to divergent interpretations of the law and that platforms are removing legal content that they falsely believe to be illegal (‘overblocking’) while simultaneously not moderating illegal content (‘underblocking’). In conclusion, we provide recommendations for content moderation system design that takes (legal) human content moderation into account and creates new methodological ways to test its quality and effect on speech in online platforms.

A Foundation Language-Image Model of the Retina (FLAIR): encoding expert knowledge in text supervision

Foundation vision-language models are currently transforming computer vision, and are on the rise in medical imaging fueled by their very promising generalization capabilities. However, the initial attempts to transfer this new paradigm to medical imaging have shown less impressive performances than those observed in other domains, due to the significant domain shift and the complex, expert domain knowledge inherent to medical-imaging tasks. Motivated by the need for domain-expert foundation models, we present FLAIR, a pre-trained vision-language model for universal retinal fundus image understanding. To this end, we compiled 38 open-access, mostly categorical fundus imaging datasets from various sources, with up to 101 different target conditions and 288,307 images. We integrate the expert’s domain knowledge in the form of descriptive textual prompts, during both pre-training and zero-shot inference, enhancing the less-informative categorical supervision of the data. Such a textual expert’s knowledge, which we compiled from the relevant clinical literature and community standards, describes the fine-grained features of the pathologies as well as the hierarchies and dependencies between them. We report comprehensive evaluations, which illustrate the benefit of integrating expert knowledge and the strong generalization capabilities of FLAIR under difficult scenarios with domain shifts or unseen categories. When adapted with a lightweight linear probe, FLAIR outperforms fully-trained, dataset-focused models, more so in the few-shot regimes. Interestingly, FLAIR outperforms by a wide margin larger-scale generalist image-language models and retina domain-specific self-supervised networks, which emphasizes the potential of embedding experts’ domain knowledge and the limitations of generalist models in medical imaging. The pre-trained model is available at: https://github.com/jusiro/FLAIR.

High accuracy meets high throughput for near full-length 16S ribosomal RNA amplicon sequencing on the Nanopore platform.

Small subunit (SSU) ribosomal RNA (rRNA) gene amplicon sequencing is a foundational method in microbial ecology. Currently, short-read platforms are commonly employed for high-throughput applications of SSU rRNA amplicon sequencing, but at the cost of poor taxonomic classification due to limited fragment lengths. The Oxford Nanopore Technologies (ONT) platform can sequence full-length SSU rRNA genes, but its lower raw-read accuracy has so-far limited accurate taxonomic classification and de novo feature generation. Here, we present a sequencing workflow, termed ssUMI, that combines unique molecular identifier (UMI)-based error correction with newer (R10.4+) ONT chemistry and sample barcoding to enable high throughput near full-length SSU rRNA (e.g. 16S rRNA) amplicon sequencing. The ssUMI workflow generated near full-length 16S rRNA consensus sequences with 99.99% mean accuracy using a minimum subread coverage of 3×, surpassing the accuracy of Illumina short reads. The consensus sequences generated with ssUMI were used to produce error-free de novo sequence features with no false positives with two microbial community standards. In contrast, Nanopore raw reads produced erroneous de novo sequence features, indicating that UMI-based error correction is currently necessary for high-accuracy microbial profiling with R10.4+ ONT sequencing chemistries. We showcase the cost-competitive scalability of the ssUMI workflow by sequencing 87 time-series wastewater samples and 27 human gut samples, obtaining quantitative ecological insights that were missed by short-read amplicon sequencing. ssUMI, therefore, enables accurate and low-cost full-length 16S rRNA amplicon sequencing on Nanopore, improving accessibility to high-resolution microbiome science.

A standardized framework to test event-based experiments

The replication crisis in experimental psychology and neuroscience has received much attention recently. This has led to wide acceptance of measures to improve scientific practices, such as preregistration and registered reports. Less effort has been devoted to performing and reporting the results of systematic tests of the functioning of the experimental setup itself. Yet, inaccuracies in the performance of the experimental setup may affect the results of a study, lead to replication failures, and importantly, impede the ability to integrate results across studies. Prompted by challenges we experienced when deploying studies across six laboratories collecting electroencephalography (EEG)/magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), and intracranial EEG (iEEG), here we describe a framework for both testing and reporting the performance of the experimental setup. In addition, 100 researchers were surveyed to provide a snapshot of current common practices and community standards concerning testing in published experiments’ setups. Most researchers reported testing their experimental setups. Almost none, however, published the tests performed or their results. Tests were diverse, targeting different aspects of the setup. Through simulations, we clearly demonstrate how even slight inaccuracies can impact the final results. We end with a standardized, open-source, step-by-step protocol for testing (visual) event-related experiments, shared via protocols.io. The protocol aims to provide researchers with a benchmark for future replications and insights into the research quality to help improve the reproducibility of results, accelerate multicenter studies, increase robustness, and enable integration across studies.

The Core Metadata Schema for Learner Corpora (LC-meta)

Abstract Metadata is critical throughout the research process, from study design to corpus selection/compilation, result interpretability and cumulative research. To date, however, learner corpus research has not developed community standards or best practices for metadata collection and sharing. In this article, we present the results of a collaborative project aimed at addressing this issue by developing a standardised metadata schema for learner corpora. We first describe the procedure implemented to design the schema, including the ways in which we continuously involved learner corpus researchers in this initiative. We then introduce the Core Metadata Schema for Learner Corpora (LC-meta, Version 2), which consists in a set of obligatory and optional variables that encapsulate crucial information about L2 data (administrative details, corpus design, text-related variables, learner-related variables, annotations, annotators, or transcribers). Finally, we discuss future developments and emphasise the importance of continued maintenance and further refinement of this schema by the research community.

Towards Establishing Community Standards for Bioinformatic Analysis of the Human Placenta

Towards Establishing Community Standards for Bioinformatic Analysis of the Human Placenta