dataesr/scientific-paragraphs-categorization huggingface dataset:
However, with the increasing availability of data today, the existence of interested audiences with a variety of scientific backgrounds outside the domain of data collection must be taken into consideration in order for scientific knowledge to be widely conveyed and understood. Designated communities may also change over time  (Baker et al. 2016). These guidelines are intended to be more general and agnostic of the quality attributes, how to tailor the dataset quality information to the designated community is left to the specific entities who serve that particular community. Therefore, the guidelines aim to equip data consumers with readily available information that is consistently curated and disseminated, in a way that can be easily found, accessed, understood by end-users and integrated across tools and systems, regardless of the quality attribute and the assessment approach.
COMMUNITY CONSENTED TERMINOLOGY FOR ENHANCED INTEROPERABILITY For a given quality dimension, consistency in various components and attributes across entities in each component, namely, semantic and structured consistency as defined by  Redman (1996) , is important to generating machine-actionable quality information. Common terminology is necessary for integrating data and information across workflows, tools and systems, as well as for curating and representing dataset quality information. Moreover, terms should be defined, and, ideally, referenced with a persistent identifier, for all stages of a dataset lifecycle.
SUMMARY In summary, there are fundamental challenges in collecting, curating, and representing dataset quality information. Those challenges include: • Dataset quality information is multi-dimensional with many quality attributes, • Information about dataset quality traverses different knowledge domains and curating it requires cross-disciplinary collaborations and knowledge integration, • Requirements may be different for different user applications as well as ways to assess dataset quality. There are also strong community needs and benefits of having community-developed guidelines that are practical to implement. At the same time, guidelines for curating and representing dataset quality information are limited and sparse. Recognizing the needs, challenges, and benefits of sharing information on quality at a dataset/ collection level, interdisciplinary domain experts around the world have come together and called for community guidelines towards global access and harmonization of information on quality of individual Earth science datasets.
The guidelines will be targeted at addressing these fundamental challenges, as well as others that are identified through the development activities. The quality-attribute agnostic guidelines will be developed under community effort via an iterative process, leveraging the experiences and expertise of an international team of interdisciplinary domain experts and best practices. Description of what the quality attributes are, how the attributes are assessed, and what assessment approaches are utilized, should be included in relevant metadata or a document, preferably in a consistent way for transparency and enhanced usability. The guidelines will call for a machine-actionable mechanism to represent assessed results for enhanced interoperability across systems and disciplines. By adopting the FAIR principles, the guidelines will help to ensure global access to the dataset quality information.
Effective sharing of structured dataset quality information will help to move towards its global harmonization, which in turn will support (re)use of the data by both human and machine end users and therefore further enhance the value of the data. As mentioned in section 5. 1, an international FAIR dataset quality information working group has been formed under the leadership of ESIP Information Quality Cluster (IQC), the Barcelona Supercomputing Center (BSC) Evaluation and Quality Control (EQC) team, and the Australian Research Data Commons (ARDC) coordinated Australia/New Zealand Data Quality Interest Group (AU/NZ DQIG). The membership of this working group is open to any domain expert who is willing to contribute to the development effort. Development of the guidelines has begun and the outcomes will be reported in a follow-up paper.
The guidelines will be primarily developed for the Earth Science community. They will, however, be general enough so that other disciplines can readily adapt them, which will further promote global access and harmonization of dataset quality information in supporting open science. The damage done by Superstorm Sandy in October 2012 was unprecedented in its size and scope. Approximately 10 million customers lost power across 24 states in the Northeast, Mid-Atlantic, and parts of the Midwest. In response, the electric power industry deployed an army of tens of thousands of restoration workers-representing 80 companies from almost every state and Canada. The goal was to restore power as quickly and safely as possible. 1 At its highest point, utility response through mutual assistance reached $20,000,000 per hour as mutual assistance reached across 40+ states, including Hawaii, from where military aircraft were used to transport electric utility vehicles to the US East Coast.
HUMAN RELATIONSHIPS, TRUST AND THE EVOLUTION OF DATA QUALITY PRINCIPLES TO ACCELERATE TRUST IN DATA TO ACCELERATE HIGH CONFIDENCE SITUATIONAL AWARENESS AND DECISION MAKING With the development of the FAIR data principles, ORLs and other evolving data quality standards, it is critical to provide ample meta data that can drive confidence in the data. ORLs are an evolving use-case-based approach for indicating trustworthiness of the data, thus enabling decision makers, who are generally not data experts, to use datasets and make rapid decisions. Developed by the ESIP and AHC, ORLs are becoming a widely-adopted approach to validating the fitness for use of digital information for the mobilization of disaster response efforts. The human relationship between information providers and decision makers has been essential to building trust.
As Craig Fugate, former FEMA Administrator used to say, "You do not want to share business cards during a disaster," trusted relationships also connote service quality. Peng et al. Data Science Journal DOI: 10. 5334/dsj-2021-019  You are more likely to trust the quality of the information provided by a familiar and trusted individual or organization than from someone you have never met. Trusted data providers can further elevate their trust levels by documenting the maturity of data quality processes and adhering to evolving FAIR data principles. This, hopefully, will be reflected in the metadata which can inform users of the lineage of the data.
ACKNOWLEDGEMENTS The virtual pre-ESIP workshop held on July 13, 2020 was sponsored by ESIP and co-organized by the  ESIP IQC  and the  BSC EQC  team, in collaboration with the  ARDC AU/NZ DQIG. An additional community engagement event was carried out by the  AU/NZ DQIG  prior to the pre-ESIP workshop. ESIP is primarily supported by the  National Aeronautics and Space Administration (NASA) , the  National Oceanic and Atmospheric Administration (NOAA)  and the  United States Geological Survey (USGS). The technological and infrastructural support during the preparation and conduct of the workshop was invaluable. In particular, we thank  Megan Carter ,  ESIP Community  Director, for supporting us throughout the workshop and providing helpful advice during the planning stage of the virtual workshop, and ESIP Community Fellow,  Alexis Garretson , for supporting the ESIP SM20 report-out session.
Methods: Data were collated by systematically identifying existing, eligible studies in any language (≥10 IPD, data on time since stroke, and language performance) and included sourcing from relevant aphasia research networks. We invited electronic contributions and also extracted IPD from the public domain. Data were assessed for completeness, validity of value-ranges within variables, and described according to pre-defined categories of demographic data, therapy descriptions, and language domain measurements. We cleaned, clarified, imputed and standardised relevant data in collaboration with the original study investigators. We presented participant, language, stroke, and therapy data characteristics of the final database using summary statistics. Results: From 5256 screened records, 698 datasets were potentially eligible for inclusion; 174 datasets (5928 IPD) from 28 countries were included, 47/174 RCT datasets (1778 IPD) and 91/174 (2834 IPD) included a speech and language therapy (SLT) intervention.
Participants' median age was 63 years (interquartile range  [53, 72] ), 3407 (61. 4%) were male and median recruitment time was 321 days  (IQR 30, 1156)  after stroke. IPD were available for aphasia severity or ability overall (n = 2699; 80 datasets), naming (n = 2886; 75 datasets), auditory comprehension (n = 2750; 71 datasets), functional communication (n = 1591; 29 datasets), reading (n = 770; 12 datasets) and writing (n = 724; 13 datasets). Information on SLT interventions were described by theoretical approach, therapy target, mode of delivery, setting and provider. Therapy regimen was described according to intensity (1882 IPD; 60 datasets), frequency (2057 IPD; 66 datasets), duration (1960 IPD; 64 datasets) and dosage  (1978 IPD; 62 datasets). Discussion: Our international IPD archive demonstrates the application of big data principles in the context of aphasia research; our rigorous methodology for data acquisition and cleaning can serve as a template for the establishment of similar databases in other research areas.
Background Big data approaches to data synthesis and analysis typically involve collation of large datasets to reveal patterns, trends, and associations which would be overlooked in single trials due to a lack of statistical power. These approaches generate data "high in volume and diversity, gathered from single participants to large groups, at one or more time points"  (Auffray et al. , 2016) , and can be applied to a range of health care settings. Big data methods have several advantages, the most important being sample size  (Fan et al. , 2014). Synthesising large volumes of data, facilitates greater representation of the population under investigation, whilst also reducing research waste  (Rumsfeld et al. , 2016). Collating data at the level of the individual, instead of at the level of aggregated, group or summary data, permits analyses that can account for individual confounders and missing data, and mitigates ecological bias, which can arise when using aggregated or group level data.
Historically, primary aphasia research studies in the stroke population have enrolled and analysed smaller sample sizes due to the heterogeneity of the population  (Plowman et al. , 2012) , recruitment challenges  (Wallace, 2010) , the assessment of a variety of outcomes  (Flowers et al. , 2016) , with data gathered on a range of stroke, aphasia and language measurement tools, capturing information on selected language modalities and conducting assessments in a range of different languages. Additionally, there is heterogeneity in the availability of participant, intervention and outcome data across existing aphasia research  (Brady et al. , 2020) , limiting clinical implementation of research findings. The feasibility of developing a large aphasia individual participant data (IPD) database is challenging due to the differences in study designs (e.
g. , randomised controlled trials 'RCTs', case studies, cohorts), data collection time points, approaches for collecting, processing and interpreting data  (Brady et al. , 2016). Generating high-quality data, which is representative of people affected by post-stroke aphasia, representing the different language domains (e. g. , everyday communication, auditory comprehension, naming, reading comprehension and writing), time since stroke, overall aphasia severity and therapy data (e. g. , approaches, frequency, intensity, duration, and dosage) in large numbers would facilitate important language recovery analyses, both within and between sub-populations. Pooling pre-existing IPD from international studies in a large, multi-disciplinary, multi-lingual database offers a cost and time-effective  (Nishimura et al.
, 2016)  approach to addressing current aphasia research priorities  (Franklin et al. , 2018; Pollock et al. , 2012). The specialised nature of aphasia research provides opportunities to work collaboratively with internationally renowned research hubs to collate datasets across languages, research settings, and using different measurement tools. However, this can also pose a challenge to synthesising and standardizing IPD into a uniform format for analyses, and relies on working closely with collaborators, ensuring trust in how data are to be collated, stored, used, analysed and acknowledged in subsequent publications. Each of these aspects of international collaborative work is essential in order to build an IPD resource which can be adopted internationally. Yet the feasibility of the development and aggregation of such diverse research and clinical datasets into a big-data archive is currently unknown within the field of aphasia rehabilitation.
Ethical approval and study registration We received university ethical approval to collate data in the RELEASE archive (HLS/NCH/15/ 09). Principal investigators of included studies obtained local ethical approval for sharing their data to the RELEASE database as required. Our protocol was registered with the international prospective register of systematic reviews (PROSPERO CRD42018110947), and the database was approved by the UK's Integrated Research Application System (IRAS; 179,505).
Dataset identification & management We conducted a systematic search of existing literature to identify potential datasets in any language that met our eligibility criteria; IPD for a minimum of ten participants with stroke-related aphasia, time since stroke information and a measurement of language ability  (Brady et al. , 2020). Working copies of original datasets were given a unique identifier, standardised to SAS Inc. 9. 4 software for data management, and stored with all available associated dataset documents (e. g. , study protocols, papers and data dictionaries). Extracted data items were recorded in a data extraction table in accordance with the Template for Intervention Description and Replication (TIDieR) guidelines in reporting complex interventions  (Hoffmann et al. , 2014). We standardised the format, coding and units of measurement of variables, maximising the number of available IPD for analysis and documented this on a decision log.
At primary dataset level, we described study design, inclusion/exclusion criteria, recruitment date (or publication date as a proxy when recruitment date was unavailable), sample size, country and language, data collection time point(s), blinding of assessors, dropouts and for RCTs, randomisation method and allocation concealment. Participant level data included demographic, stroke and language impairment information. Data cleaning and categorisation decisions were taken in collaboration with the wider RELEASE group. Where possible, each dataset profile summary was sent to the collaborating investigator for review and approval prior to inclusion in the final RELEASE database.
Datasets Following the systematic search, 11,314 records were identified. After the removal of duplicates, we screened 5256 records, excluding 2935 ineligible records. We excluded a further 1210 following abstract review and an additional 433 records after a review of the full texts (Figure  1 ). We invited the authors of 698 potentially eligible reports to contribute the associated IPD. Of which, 592 did not result in data contribution as 193/592 were trial registration records, no responses were received from 318/592 research teams, IPD from 78 datasets were unavailable IPD, and 3/592 declined to participate. We received 125 expressions of interest relating to eligible datasets and identified a further 99 eligible datasets in the public domain (Figure  1 ). Our RELEASE database thus comprised of 174 datasets with 5928 IPD from 28 countries, in 23 languages; 13 were unpublished.
Study types More than a quarter of the primary research studies 47/174 (27. 0%) were RCTs (1778/5928, 30. 0% IPD); over half 104/174 (59. 8%) were a case series or cohort design (2886, 48. 7% IPD); non-randomized controlled trial studies whereby participants were recruited to groups, but random allocation was not applied (non RCTs) accounted for 18/174 (10. 3%) datasets (n = 411, 6. 9% IPD). The remaining 2. 9% (5/174) of datasets came from registries (853, 1. 4% IPD). Data inclusion in each planned analysis was on a dataset-bydataset basis and did not necessarily reflect the primary research study designs. For example, in some cases, participants were randomly allocated to two (non-SLT) interventions but all received the same SLT treatment. In such cases, the IPD were variously analysed as a single cohort or by group depending on the nature of the dataset and the planned analyses.
Blinding of outcome assessment was reported in 49/174 datasets (28. 2%) though in many cases blinding was not relevant to the primary dataset study design (e. g. , a clinical registry or observational study). An additional nine datasets (5. 2%) described blinding for a subset of data and 117 datasets either did not report blinding (91/117) or had indicated that blinding was not present (25/117). A third of the IPD were available in the public domain; some of these IPD datasets were only partially reported in those publications (for example, baseline data only) while in other cases full RCT datasets were available. Some primary research dataset electronic contributions were only partially available with published reports relating to that dataset indicatinghat additional data items were available at the time of publication.
Discussion Using an international multidisciplinary collaborative approach, robust methodology, and applying big data principles within the context of aphasia research, we systematically created an international IPD database that adhered to data sharing guidelines  (Knoppers, 2014) , and which ensured the feasibility of our analysis plan  (Brady et al. , 2020). Working with our collaborators and accessing data in the public domain, allowed us to include demographic, stroke, aphasia, language impairment and SLT intervention IPD from 28 countries and language outcomes gathered using 64 measurement tools. Over half the datasets included an SLT intervention, IPD that can be used to inform future analyses to optimise the delivery of SLT in people with aphasia. Our database is representative of available aphasia research datasets and reflects the heterogeneity in data collection practices across a range of different study designs and contexts.
Primary researchers were actively involved in the development and cleaning of data, thereby contributing to the validity, accuracy and robustness of the data contained within the archive. The research landscape across a range of health settings has highlighted the importance of minimising research waste  (Chan et al. , 2014; Doubal et al. , 2017) , resulting in encouragement and recognition of the value  (Taichman et al. , 2016) , in sharing research datasets to facilitate new analyses. Guidelines exist to support IPD sharing  (Waithira et al. , 2019) , with appropriate approvals, accreditation and acknowledgement  (Wilkinson et al. , 2016) , but the feasibility of such an approach in the context of aphasia rehabilitation research had not yet been examined; we set out to explore this. Previous work has highlighted that health research protocols, full study reports, and participant-level datasets are not widely available in the public domain  (Chan et al.
, 2014). However, recent initiatives that require the lodging of datasets in publicly available resources have changed the landscape of health research  (Taichman et al. , 2016). Additionally, in one study publications reporting study findings were available for only half of all studies  (Chan et al. , 2014) , and can be subject to selective reporting or other sources of bias. A strength in our method of identifying eligible studies was that it relied not only on searches of the existing literature, but also on communication with existing international research networks such as the Collaboration of Aphasia Trialists (www. aphasiatrials. org) to identify potentially eligible datasets for inclusion. Our model builds on historical examples of in-house research activities that have included data sharing amongst specific research teams.
Use of datasets based on availability and access within research groups can often bias dataset selection, impacting upon the representativeness of the data and limiting the generalisability of results. Our study highlighted some restrictions to collaborative approaches. Despite our wide eligibility criteria and our systematic identification of, and open invitation to principal investigators with potentially eligible datasets, we were only able to acquire a subset of potentially eligible datasets; of 698 datasets identified as potentially eligible, 267 were unavailable for data sharing. We highlighted several important methodological steps that were required to develop this international database. These included the establishment of relevant but inclusive eligibility criteria, the systematic identification of eligible studies using multiple information sources, open invitation to international researchers to contribute data, collaborative working and consensus building to agree on variable parameters and categories, cleaning and standardisation of the data, and finally transparent description of the database to facilitate informed exploratory analyses.
This model can be used by investigators in other health research areas to create similar data repositories within which to conduct exploratory analyses. Beyond the RELEASE project, a long-term legacy, big data repository for aphasia datasets is under development within the Collaboration of Aphasia Trialists, using the principles set out above. We invite further contributions of eligible datasets to this aphasia archive, to facilitate future analyses for the benefit of people with aphasia. Chief Scientist Office (CSO), Scottish Government Health and Social Care Directorates. The views expressed are those of the authors and not necessarily those of the funders.
Multi-objective Mixed-Integer Linear Programming-Based Approach The designed MOMILP-based approach is implemented through the PuLP library of Python objective-oriented programming language. The library allows modelling linear programming problems and is supported by different optimization solvers. In particular, the solver used for the simulations is GUROBI, as it shows the best performances among the tested ones. Furthermore, Gantt charts are used for the graphical representations of the scheduling, which are built through Plotly (a library for Python based on Dash framework). In the simulation experiment, the global scheduling of 704 jobs each of which composed of 3 coils with a weight of 10 tons has to be accomplished in a daily timeframe. Jobs are selected according to their due date and are equally distributed among the production stages.