Here are two transcripts from a set of interviews executed by Sabina Leonelli in the fall 2015 as part ERC project "The Epistemology Data-Intensive Science", and context case study phenotyping practices at National Plant Phenomics Centre Aberystwyth collaborators. The document researchers' experience data curation practices. Researchers have consented to these made available Open Data. Other interviewees did not give consent or ended up providing sensitive information their interviews, so those cannot be open held securely research team Exeter. You also find sheet provided interviewees, which gives you for this project. Further can found www.datastudies.eu. been redacted exclude names people who given participate study, but otherwise left unedited therefore contain several colloquial expressions. A paper specifically makes use will published 2019 European Journal Philosophy Science, under title “What Distinguishes Data Models?”. Freely accessible preprint here: http://philsci-archive.pitt.edu/id/eprint/15485 . Several related publications Access formats on website:

This is a set of interview transcripts executed by Niccolò Tempini between May 2016 and July 2017, as part the ERC project "The Epistemology Data-Intensive Science", in context case study COSMIC. Please read "Notes on transcript editing" document for further information. Papers that specifically make use these interviews have been published February 2021: Tempini, N., 2020. Data curation-research: practices data standardization exploration precision medicine database. New Genetics Society. https://doi.org/10.1080/14636778.2020.1853513 Leonelli, S., 2021. Actionable Precision Oncology: Framing Trustworthy Evidence Exploratory Research Clinical Diagnostics. Social Science &amp; Medicine. https://doi.org/10.1016/j.socscimed.2021.113760 The COSMIC researchers' experience infrastructure development curation re-use practices. Researchers consented to made available Open Data. Other interviewees did not give consent, so those are held securely research team Exeter.<br> You also find information sheet provided interviewees, which gives you this project. Further related publications can be found at www.datastudies.eu.

Here you find the transcripts of interviews collected by Sabina Leonelli as part ERC project "The Epistemology Data-Intensive Science". You also information sheet provided to interviewees, which gives context for this project. Further and related publications can be found at www.datastudies.eu. One paper that specifically makes use these was published in journal Philosophy Science 2018, under title "Data Time: Time-Scales Data Use Life Sciences." The document yeast researchers' attitudes data curation databases their field. Researchers have consented made available Open Data. Other interviewees did not give consent, so those are held securely research team Exeter. <br>

This is a collection of transcripts from interviews conducted by Gregor Halfmann as part the ERC project "The Epistemology Data-intensive Science". The served empirical basis for Halfmann's PhD thesis "Seafarers, Silk, and Science: Oceanographic Data in Making", submitted at University Exeter July 2018. relate to research practices, particular production processing samples scientific data, marine ecologists biological oceanographers working Marine Biological Association UK Continuous Plankton Recorder Survey. Researchers have consented these made available Open Data.

This is a collection of transcripts from interviews conducted by Gregor Halfmann as part the ERC project "The Epistemology Data-intensive Science". The served empirical basis for Halfmann's PhD thesis "Seafarers, Silk, and Science: Oceanographic Data in Making", submitted at University Exeter July 2018. relate to research practices, particular production processing samples scientific data, marine ecologists biological oceanographers working Marine Biological Association UK Continuous Plankton Recorder Survey. Researchers have consented these made available Open Data.

This is a set of interview transcripts executed by Niccolò Tempini between May 2016 and July 2017, as part the ERC project "The Epistemology Data-Intensive Science", in context case study COSMIC. Please read "Notes on transcript editing" document for further information. Papers that specifically make use these interviews have been published February 2021: Tempini, N., 2020. Data curation-research: practices data standardization exploration precision medicine database. New Genetics Society. https://doi.org/10.1080/14636778.2020.1853513 Leonelli, S., 2021. Actionable Precision Oncology: Framing Trustworthy Evidence Exploratory Research Clinical Diagnostics. Social Science &amp; Medicine. https://doi.org/10.1016/j.socscimed.2021.113760 The COSMIC researchers' experience infrastructure development curation re-use practices. Researchers consented to made available Open Data. Other interviewees did not give consent, so those are held securely research team Exeter.<br> You also find information sheet provided interviewees, which gives you this project. Further related publications can be found at www.datastudies.eu.

This is a set of interview transcripts executed by Niccolò Tempini between September 2015 and October 2016, as part the ERC project "The Epistemology Data-Intensive Science", in context case study SAIL. Please read "Notes on transcript editing" document for further information. Two papers that specifically make use these interviews have been published 2018: Tempini, N., Leonelli, S., 2018. Concealment discovery: The role information security biomedical data re-use. Soc Stud Sci 48, 663–690. https://doi.org/10.1177/0306312718804875 2016. Science Through “Golden Security Triangle”: Information Data Journeys Data-intensive Biomedicine, in: Proceedings 37th International Conference Systems (ICIS 2016). https://aisel.aisnet.org/icis2016/ISHealthcare/Presentations/20/ SAIL researchers' experience infrastructure development curation re-use practices. Researchers consented to made available Open Data. Other interviewees did not give consent, so those are held securely research team Exeter.<br> You also find sheet provided interviewees, which gives you this project. Further related publications can be found at www.datastudies.eu.

This is a set of interview transcripts executed by Niccolò Tempini between September 2015 and October 2016, as part the ERC project "The Epistemology Data-Intensive Science", in context case study SAIL. Please read "Notes on transcript editing" document for further information. Two papers that specifically make use these interviews have been published 2018: Tempini, N., Leonelli, S., 2018. Concealment discovery: The role information security biomedical data re-use. Soc Stud Sci 48, 663–690. https://doi.org/10.1177/0306312718804875 2016. Science Through “Golden Security Triangle”: Information Data Journeys Data-intensive Biomedicine, in: Proceedings 37th International Conference Systems (ICIS 2016). https://aisel.aisnet.org/icis2016/ISHealthcare/Presentations/20/ SAIL researchers' experience infrastructure development curation re-use practices. Researchers consented to made available Open Data. Other interviewees did not give consent, so those are held securely research team Exeter.<br> You also find sheet provided interviewees, which gives you this project. Further related publications can be found at www.datastudies.eu.

This is a set of interview transcripts executed by Niccolò Tempini between March and October 2016, as part the ERC project "The Epistemology Data-Intensive Science", in context case study MEDMI. Please read "Notes on transcript editing" document for further information. Three papers or chapters that specifically make use these interviews have been published 2020: Tempini, N., Leonelli, S., 2018. Concealment discovery: The role information security biomedical data re-use. Soc Stud Sci 48, 663–690. https://doi.org/10.1177/0306312718804875 Where health environment meet: invariant parameters big analysis. Synthese 1–20. https://doi.org/10.1007/s11229-018-1844-2 N. 2020. Reuse Digital Computer Data: Transformation, Recombination Generation <em>Data Mixes</em> Big Data Science. In: Leonelli (eds) Journeys Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-37177-7_13 MEDMI researchers' experience infrastructure development curation re-use practices. Researchers consented to made available Open Data. Other interviewees did not give consent, so those are held securely research team Exeter.<br> You also find sheet provided interviewees, which gives you this project. Further related publications can be found at www.datastudies.eu.

Smartphone technology provides us with a more convenient and less intrusive method of detecting changes in behavior symptoms that typically precede schizophrenia relapse. To take advantage the aforementioned, this study examines feasibility predicting relapse by identifying statistically significant anomalies patient data gathered through mindLAMP, an open-source smartphone app. Participants, recruited Boston, MA United States, Bangalore Bhopal India, were invited to use mindLAMP for up year. The passive (geolocation, accelerometer, screen state), active (surveys), quality metrics collected app then retroactively fed into prediction model utilizes anomaly detection. Overall, 2.12 times frequent month preceding 2.78 following compared intervals without relapses. detection incorporating proved better predictor than naive utilizing only survey data. These results demonstrate models can warn clinician potential

<b>Background:</b> Utilization of the available observational healthcare datasets is key to complement and strengthen postmarketing safety studies. Use common data models (CDM) predominant approach in order enable large scale systematic analyses on disparate vocabularies. Current CDM transformation practices depend proprietarily developed Extract-Transform-Load (ETL) procedures, which require knowledge both semantics technical characteristics source target CDM. <b>Purpose:</b> In this study, our aim develop a modular but coordinated separate semantic steps processes, do not have strict separation traditional ETL approaches. Such an would discretize operations extract from electronic health record systems, alignment source, level populate repositories. <b>Approach:</b> activities that are required transform heterogeneous sources CDM, we introduce composed three steps: (1) Resource Description Framework (RDF) format, (2) application conversion rules get as instances ontological model (3) population repositories, comply with specifications by processing RDF step 2. The proposed has been implemented real settings where Observational Medical Outcomes Partnership (OMOP) chosen comprehensive comparative analysis between native transformed conducted. <b>Results:</b> Health records ~1 million patients successfully OMOP based database database. Descriptive statistics obtained databases present analogous consistent results. <b>Discussion Conclusion:</b> Our method goes beyond approaches being more declarative rigorous. Declarative because use mapping makes each transparent understandable humans while retaining logic-based computability. Rigorous mappings be computer readable amenable validation through inference methods.

This paper presents the Summed Parallel Infinite Impulse Response (SPIIR) pipeline used for public alerts during third advanced LIGO and Virgo observation run (O3 run). The SPIIR uses infinite impulse response (IIR) filters to perform extremely low-latency matched filtering this process is further accelerated with graphics processing units (GPUs). It first online select candidates from multiple detectors using a coherent statistic based on maximum network likelihood ratio principle. Here we simplify derivation of singular-value-decomposition (SVD) technique show that single-detector signal-to-noise ratios can be directly construct statistic. Coherent searches are in general more computationally challenging than coincidence due extra search over sky direction parameters. directions follows an embarrassing parallelization paradigm has been GPUs. detection performance reported segment data LIGO-Virgo's second run. We demonstrate median latency less 9 seconds, present achievable road map reduce 5 seconds. During O3 run, registered triggers associated 38 56 nonretracted alerts. extreme nature makes it competitive choice joint time-domain observations, offers tantalizing possibility making prior merger phase binary coalescence systems involving at least one neutron star.

This study investigates the association between treatment with hydroxychloroquine and mortality in patients admitted COVID-19. Routinely recorded, clinical data, up to 24th of April 2020, from 2075 COVID-19, 17 hospitals Spain 1st March 20th 2020 were used. The following variables extracted for this study: age, gender, temperature, saturation oxygen on admission, hydroxychloroquine, azithromycin, heparin, steroids, tocilizumab, a combination lopinavir ritonavir, oseltamivir, together data mortality. Multivariable logistic regression models used investigate associations. At time collecting 301 had died, 1449 been discharged home hospitals, 240 still admitted, 85 transferred not included study. Median follow-up was 8 (IQR 5-12) days. Hydroxychloroquine 1857 patients. associated lower when model adjusted age OR (95% CI): 0.44 (0.29-0.67). remained significant < 90% temperature > 37 °C added de 0.45 (0.30-0.68) p 0.001, also all other drugs, as covariates. observed can be acknowledged by clinicians community. Randomized-controlled trials assess causal effects different therapeutic regimes are required.

Diabetic retinopathy is an eye disease caused by high blood sugar and pressure which damages the vessels in eye. root cause of more than 1% blindness worldwide. Early detection this crucial as it prevents from progressing to a severe level. However, current machine learning-based approaches for detecting severity level diabetic are either, i) rely on manually extracting features makes approach unpractical, or ii) trained small dataset thus cannot be generalized. In study, we propose transfer with accuracy. Our model deep learning based global average pooling (GAP) technique various pre-trained convolutional neural net- work (CNN) models. The experimental results our approach, best achieved 82.4% quadratic weighted kappa (QWK), corroborate ability detect efficiently.

Big data is the term for a collection of datasets so huge and complex that it becomes difficult to be processed using on-hand theoretical models technique tools. Brain big one most typical, important collected powerful equipments functional magnetic resonance imaging, multichannel electroencephalography, magnetoencephalography, Positron emission tomography, near infrared spectroscopic as well other various devices. Granular computing with multiple granular layers, referred multi-granular (MGrC) short hereafter, an emerging paradigm information processing, which simulates intelligent thinking model human brain. It concerns processing entities called granules, arise in process abstraction derivation even knowledge from data. This paper analyzes three basic mechanisms MGrC, namely granularity optimization, conversion, multi-granularity joint computation, discusses potential introducing MGrC into brain

mindLAMP shows promising results in improving anxiety and depression outcomes participants. Though our mirror the current literature assessing mental health apps' efficacy, they remain preliminary will be used to inform a larger, well-powered study further elucidate efficacy of mindLAMP.

The most severe clinical manifestations of the Coronavirus disease 2019 (COVID-19), caused by acute respiratory syndrome coronavirus 2 (SARS-CoV-2), are due to an unbalanced immune response and a pro-thrombotic hemostatic disturbance, with arterial hypertension or diabetes as acknowledged risk factors. While waiting for specific treatment, management hospitalized patients is still matter debate, effectiveness treatments manage comorbidities has been questioned. In this study, we aim assess impact hypertension, inflammation thrombosis on survival COVID-19 patients. Spanish cohorts included in observational retrospective study from HM Hospitales (2035 patients) Hospital Universitario Central de Asturias (72 patients). Kaplan Meier curves, Cox regression propensity score matching analyses were employed, considering demographic variables, treatment arms (when opportune) covariates. angiotensin-converting enzyme (ACE2) inhibitors angiotensin receptor blockers not detrimental, was initially reported, neither use non-steroidal anti-inflammatory drugs (NSAIDs). On contrary, our analysis shows that itself corticosteroids beneficial. Importantly, low molecular weight heparin (LMWH) anticoagulant significantly improves These results delineate current options under supporting prophylaxis first-line without need compromising comorbidities, while suggesting cautiousness when administering corticosteroids.

Traditional cognitive assessments in schizophrenia are time-consuming and necessitate specialized training, making routine evaluation challenging. To overcome these limitations, this study investigates the feasibility advantages of utilizing smartphone-based to capture both functioning digital phenotyping data compare results gold standard measures. We conducted a secondary analysis from 76 individuals with schizophrenia, who were recruited across three sites (one Boston, two India) was conducted. The open-source mindLAMP smartphone app captured Trails A/B attention / memory for up 12 months. smartphone-cognitive tasks exhibited potential normal distribution scores showed small but significant correlations Brief Assessment Cognition Schizophrenia, especially span symbol coding (r2 = 0.21). A correlation 0.29) between smartphone-derived health-related behaviors such as sleep duration patterns observed. Smartphone-based show promise cross-cultural tools that can relevant on momentary states among schizophrenia. Cognitive related suggest functional applications data, multimodal approach research.

The use of big data to investigate the spread infectious diseases or impact built environment on human wellbeing goes beyond realm traditional approaches epidemiology, and includes a large variety objects produced by research communities with different methods goals. This paper addresses conditions under which researchers link, search interpret such diverse focusing "data mash-ups"-that is linking from biomedicine, climate environmental science, typically achieved holding one more basic parameters, as geolocation, invariant. We argue that this strategy works best when epidemiologists localisation procedures through an idiographic perspective recognises their context-dependence supports critical evaluation epistemic value geolocation whenever they are used for new purposes. Approaching invariants strategic constructs can foster linkage re-use, support carefully-targeted predictions in ways meaningfully inform public health. At same time, it explicitly signals limitations scope applicability original datasets incorporated into collections, thus situated nature exercises predictive power.

Huge amounts of genomic data produced by researchers around the world undermine data-centred discovery and therapeutic development. This paper considers how make decisions about actionability specific datasets conditions that allow such to be trusted. We discuss case COSMIC, a leading cancer genomics database which aggregates large amount sources. research what means in different situations use, contrasting exploratory diagnostics research. They highlight questions concerns upon use medical At same time, strategies justifications pursued evaluate re-use can also share important similarities. To explain differences similarities, we argue for an understanding trust depends on goals resources within situation inquiry, social epistemology standards.

Radiation oncology, a major treatment modality in the care of patients with malignant disease, is technology- and computer-intensive medical specialty. As such, it should lend itself ideally to data science methods, where computer science, statistics, clinical knowledge are combined advance state-of-the-art care. Nevertheless, methods radiation oncology research still their infancy successful applications leading improved patient remain scarce. Here, we discuss interoperability issues within across organizational boundaries that hamper introduction big techniques oncology. At semantic level, creating common underlying models codification data, including use elements standardized definitions, an ontology, remains work progress. Methodological large population-based health registries identified. We show cannot replace randomized trials comparative effectiveness by reviewing series instances outcomes analyses at odds. also modern wave machine learning artificial intelligence as represented deep convolutional neural networks. Finally, identify promising avenues optimistic sources can be linked yield important insights near future. argue will valuable complement to, but not replacement of, traditional hypothesis-driven translational chain form backbone evidence-based medicine.

Data-driven knowledge acquisition is one of the key research fields in data mining. Dealing with large amounts has received a lot attention field recently, and number methodologies have been proposed to extract insights from an automated or semi-automated manner. However, these generally target specific aspect mining process, such as acquisition, preprocessing, classification. comprehensive method crucial support end-to-end engineering process. In this paper, we introduce system that covers all major phases cross-industry standard process for Acknowledging importance designed developed easy-to-use data-driven tool (DDKAT). The features DDKAT are: (1) novel unified scoring approach selection; (2) user-friendly processing interface improve quality raw data; (3) appropriate decision tree algorithm selection build classification model; (4) generation production rules various models Furthermore, two diabetes studies were performed assess value terms user experience. A total 19 experts involved first study 102 students artificial intelligence domain second study. results showed overall experience was positive its attractiveness, well pragmatic hedonic factors.

The restrictions that have been implemented due to the COVID-19 pandemic highlighted growing importance of digital financing. While traditional banking services limited by social distancing, reduced work hours, and lockdowns, financial can deal effectively with those restriction measures while facilitating governments channel relief stimulus funds micro, small medium-sized enterprises (MSMEs). This paper analyzes, using bibliometric review approach along VOSviewer, a data visualization software, 629 Scopus journal articles relevant key components financing for SMEs under pandemic. Based on review, it identifies most crucial policy areas presents implications how support MSMEs in dealing COVID's challenges. JEL classification codes: G21, G23, G28, G32.

Condition-based maintenance (CBM) has emerged as a proactive strategy for determining the best time activities. In this paper, case of milling process with imperfect at German automotive manufacturer is considered. Its major challenge that only data missing labels are available, which does not provide sufficient basis classical prognostic models. To overcome shortcoming, science study carried out combines several analytical methods, especially from field machine learning (ML). These include time-domain and time–frequency domain techniques feature extraction, agglomerative hierarchical clustering series unsupervised pattern detection, well recurrent neural network model training. With approach developed, it possible to replace decisions were made based on subjective criteria data-driven increase tool life machines. The solution can be employed beyond presented similar scenarios decision support development. Moreover, helps further close gap between ML research practical implementation CBM.

Currently, many intelligent building energy management systems (BEMSs) are emerging for saving in new and existing buildings realizing a sustainable society worldwide. However, installing an BEMS does not realize innovative advanced because it only involves simple equipment replacement (i.e., of old or LED (Light Emitting Diode) lamps) savings based on stand-alone system. Therefore, artificial intelligence (AI) is applied to implement optimization the latest ICT (Information Communications Technologies) technology. AI can analyze usage data, predict future requirements, establish appropriate policy. In this paper, we present dynamic heating, ventilation, air conditioning (HVAC) scheduling method that collects, analyzes, infers data intelligently save reinforcement learning (RL). regard, hotel used as testbed study. The proposed IoT from provide policy futuristic HVAC (heating system) system RL. Through process, purpose-oriented methodology achieve goals proposed.

The complexity of analysing data from IoT sensors requires the use Big Data technologies, posing challenges such as curation and quality assessment. Not facing both aspects potentially can lead to erroneous decision-making (<i>i.e.</i>, processing incorrectly treated data, introducing errors into processes, causing damage or increasing costs). This article presents ELI, an IoT-based pipeline for developing a process assessing usability collected by in offline online scenarios. We propose that integrates transformation integration tools customisable decision model based on Decision Model Notation (DMN) evaluate quality. Our study emphasises importance integrate information identifying discarding low-quality obstruct meaningful insights introduce making. evaluated our approach smart farm scenario using agricultural humidity temperature various types sensors. Moreover, proposed exhibited consistent results (stream data) In addition, performance evaluation has been developed, demonstrating its effectiveness. summary, this contributes development usable effective with capabilities Additionally, it introduces models measuring across multiple dimensions.