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:

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 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 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.

<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.

Abstract 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

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.

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

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.

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.

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.

Data-driven knowledge acquisition is one of the key research fields in data mining. Dealingwith 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.

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.

Abstract With the emergence of Internet Things (IoT) as a new source “big” data and value creation, businesses encounter novel opportunities well challenges in IoT design. Although recent research argues that digital technology can enable kinds development processes are distinctive from their counterparts 20th century, minimal attention has been focused on design process. In order to contextualize New Product Development (NPD) for IoT, this paper comprehensively interrogates existing, emerging approaches products, services, software, integrated several factors affect designing IoT. This discussion includes generic process, commonalities differences different approaches, processes. The demonstrates only few existing reflect vital characteristics networked artifacts or integration science within model, which is one key attributes From these investigations, we propose “ Mobius Strip Model ProcessI ,” conceptual process design, others. continuous loops integrate phases consist two strategies. Understanding particular NPD help novice experienced researchers both feeding drawing insight broader discourse.

The use of routine laboratory biomarkers plays a key role in decision making the clinical practice COVID-19, allowing development screening tools for personalized treatments. This study performed short-term longitudinal cluster from patients with COVID-19 based on biochemical measurements first 72 h after hospitalization. Clinical and variables 1039 confirmed framed “COVID Data Save Lives” were grouped 24-h blocks to perform k-means clustering algorithm trajectories. final solution three clusters showed strong association different severity outcomes (OR death: Cluster A reference, B 12.83 CI: 6.11–30.54, C 14.29 6.66–34.43; OR ventilation: Cluster-B 2.22 1.64–3.01, Cluster-C 1.71 1.08–2.76), improving AUC models terms age, sex, oxygen concentration, Charlson Comorbidities Index (0.810 vs. 0.871 p &lt; 0.001 0.749 0.807 0.001, respectively). Patient diagnoses prognoses remarkably diverged between obtained, evidencing that data-driven technologies devised screening, analysis, prediction, tracking play application individualized management pandemics.