dataesr/scientific-paragraphs-categorization huggingface dataset:
Areas corresponding to 512 × 512 points were scanned at a rate of 1 Hz, using an integral gain of 1 and a proportional gain of 5; amplitude set point values were approximately constant across all measurements. The acquired height images were processed using the Nanoscope Analysis software (Version 1. 4, Bruker, UK) in order to assess lateral dimensions and thickness of the GO samples. A drop of the original GO dispersions was placed onto a Tensor 27 FT-IR spectrometer (Bruker, UK) equipped with a 3000 Series High Stability Temperature Controller with RS232 Control (Specac, UK) and a MKII Golden Gate Single Reflection ATR system (Specac, UK) for measurements in ATR mode. The drop was allowed to dry on the plate for 5 min at 60 °C, until a dry powder remained. Transmittance spectra of GO were recorded by acquiring 32 scans between 700 and 4000 cm -1 with a resolution of 4 cm -1.
Data processing was completed using OriginPro 8. 5. 1 software (Origin Lab, USA). For Raman spectroscopy, the samples were prepared for analysis by drop casting 20 μl of sample (100 μg ml -1 ) dispersion onto a glass slide. The samples were left to dry for at least 2 h at 37 °C. The spectra were collected using a DXR micro-Raman spectrometer (Thermo Scientific, UK) using a λ = 633 nm laser. The spectra were considered between 500 and 3400 cm -1 , enabling visualization of the D and G bands. The spectra were collected at a laser power of 0. 4 mW at a magnification lens of ×50with 25 s exposure time, and averaged over five different locations. Immune cell purification and cell culture maintenance. PBMCs were harvested from ethylenediamine tetraacetic acid (EDTA)-venous blood from informed healthy donors (25-50 years old) using a Ficoll-Paque (GE Healthcare, CA, USA) standard separation protocol.
After the incubation, cells were washed twice with Maxpar Cell Staining Buffer and incubated with Cell-ID Intercalator-Ir solution at the final concentration of 125 nM into Maxpar Fix and Perm Buffer for 5 min. Each sample was then washed twice with Maxpar Cell Staining Buffer and suspended with 2 ml of ultrapure water. Before the data acquisition, each sample was filtered into 5 ml round bottom polystyrene tubes with a 30 μm cell strainer cap to remove possible cell clusters or aggregates. Data were analyzed using mass cytometry platform CyTOF2 (Fluidigm Corporation, CA, USA).
Acknowledgements This work was partly supported by the  Fondazione Banco di Sardegna  (grant no. to L. G. D. ), the  Italian Association against Leukemia (AIL)  (early career grant to L. G. D. ). This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement no. 734381  ( CARBO-immap ), and the  DEPTH  project of the  European Research Council  (grant agreement  322749  to G. C. ). This work was partly supported by the  Centre National de la Recherche Scientique (CNRS)  by the  Agence Nationale de la Recherche (ANR)  through the  LabEx  project  Chemistry of Complex Systems  ( ANR-10-LABX-0026_CSC ) (to A. B. ), and by the  International Center for Frontier Research in Chemistry (icFRC). We gratefully acknowledge financial support from  EU   H2020-Adhoc-2014-20 Graphene Core1  (no.
696656 ), and from  ANR  ( ANR-15-GRFL-0001-05 ) and FLAGERA JTC Graphene 2015 (G- IMMUNOMICS  project). L. N. and F. A. R. would like to acknowledge the  Engineering and Physical Sciences Research Council (EPSRC) Now-Nano DTC and GrapheneNOWNANO CDT  programmes at the  University of Manchester  for their fully funded student scholarships. K. K. would also like to thank the  EPSRC Programme Grant   2D-Health (EP/P00119X/1 ) for partially supporting this research. We thank the staff in the  Faculty of Life Sciences EM Facility ,  Dr. Aleksandr Mironov  and  Ms. Samantha Forbes , for their expertise and the  Wellcome Trust  for equipment grant support to the Facility. The University of Manchester Bioimaging Facility microscopes used in this study were purchased with grants from the  BBSRC ,  Wellcome Trust , and the  University of Manchester Strategic Fund.
To sustain growth, maintain competitive advantage and to innovate, companies must make a paradigm shift in which both short-and long-term value aspects are employed to guide their decision-making. Such need is clearly pressing in innovative industries, such as ICT, and is also the core of Value-based Software Engineering (VBSE). The goal of this paper is to detail a framework called VALUE-improving decision-making relating to softwareintensive products and services development-and to show its application in practice to a large ICT company in Finland. The VALUE framework includes a mixed-methods approach, as follows: to elicit key stakeholders' tacit knowledge regarding factors used during a decisionmaking process, either transcripts from interviews with key stakeholders are analysed and validated in focus group meetings or focus-group meeting(s) are directly applied.
Introduction and motivation Many ICT companies worldwide use only cost and effort estimates when making decisions relating to their software/software-intensive products. However, given today's cutthroat product and services industries, to rely solely upon such short-term decisions is misguided  (Biffl et al. 2005). To sustain growth, maintain competitive advantage and to innovate, companies must make a paradigm shift in which both short-and long-term value aspects are employed to guide their decision-making. Such need is clearly pressing in innovative industries, such as ICT  (Mendes et al. 2015) , and is also the core of value-based software engineering (VBSE)  (Boehm 2003). Boehm (2003)  very clearly has put forward that much of the current software engineering practice and research is carried out in a value-neutral setting, where & Every requirement, use case, object, test case and defect is treated as equally important & "Earned value" systems track project cost and schedule, not stakeholder or business value & A "separation of concerns" is practised, in which the responsibility of software engineers is confined to turning software requirements into verified code.
2. 1), either transcripts from interviews with those stakeholders are analysed and followed by focus groups meetings attended by them or directly elicited in one or more focus-group meeting(s) attended by them (Sect. 4). These value factors are later used as input to a Web-based tool employed to support decision-making (Value tool, described in Sect. 5). This tool was co-created with all of our four industrial partners via a design science approach that includes several case studies and focus-group meetings. Later, data on decisions, gathered using the Value tool, plus additional input from key stakeholders, are used, in combination with the expert-based knowledge engineering of Bayesian networks (EKEBN) process (Sect. 2. 2), coupled with the WSA method (Sect. 6), to build and validate a company-specific value estimation model.
g. Mendes and Mosley 2008; Mendes 2012b; Misirli and Bener 2014; Radlinski 2010). This technique was applied using an adaptation of the knowledge engineering of Bayesian networks process  (Woodberry et al. 2004 ) (EKEBN), introduced in Sect. 3 and detailed in Sect. 6. This is the first time within the context of VBSE that such framework solution is being suggested for value-based decision making, and also the first time that a genuine prediction technique has been put forward to estimate the overall value of decisions within the context of software/software-intensive product management and development. However, note that we see the use of a value estimation model as complementary to other data visualisation techniques in helping improve value-based decision-making, rather than as the only support.
Section 4 presents the first part of the framework-A1, detailing how we explicitated the knowledge relating to value factors from key stakeholders in company A. Section 5 describes the second part of the framework-A2, via a pilot study using our bespoke Web-based Value tool, embedding the value factors from A1, which supports decision-making. Section 6 takes the reader through the EKEBN process applied to building a value estimation model for company A, using real decisions data gathered from A2, complemented with an example; this covers the other three parts of the VALUE framework-A3 to A5. Section 7 provides our comments on implications of this work to research and practice, followed by a discussion on threats to the validity of our work in Sect. 8; finally, conclusions and comments on future work are presented in Sect.
2005). When thinking in terms of value, the spectrum and variety of stakeholders involved in the decision making is wider than when thinking in terms of costs, and a broader range of factors is also considered (e. g. business, marketing, technical, etc. ). In addition, different value propositions from different stakeholders have also been recognised as an aspect characterising value in the software domain  (Biffl et al. 2005). For example, different stakeholders might differ on which features bring the most value to a product and on reasons for arguing their views. With regard to earlier work in VBSE, previous studies have proposed value considerations and corresponding measurement solutions needed for making decisions about software product management and development (e. g. Biffl et al. 2005; Boehm 2003).
The VALUE framework The main goal of the VALUE framework is to support companies improve their value-based decision-making. It has five main parts (shown in Fig. 3  as A1 to A5), which jointly aim to (i) elicit the fundamental value factors from key stakeholders; (ii) use the fundamental value factors with a bespoke software tool-Value tool, to support and improve decision-making within the context of software/software-intensive products and (iii) also provide a value estimation model (integrated in the Value tool), built using data on previous decisions gathered using the Value tool, input from key stakeholders and a semi-automatic algorithm for probability generation, as additional support to improving decision-making. Figure  3  provides an overview of the VALUE framework's main parts-A1 to A5.
-Semi-automatic generation of a probabilistic value estimation model This part entails the use of data in the decision database, input from key stakeholders and a semi-automatic algorithm for probability generation, to build and validate a company-specific Bayesian network (BN) model to be used to estimate the overall value of a decision. Note that in order to build a value estimation BN model, a company will need to have access to experts in BNs to guide through steps A3 to A5. The overall process we use when building BN models is called expert-based knowledge engineering of Bayesian networks process (EKEBN) (see Fig. 4 )  (Mendes 2012; Mendes 2014)  and has been adapted from  Woodberry et al. (2004)  in order to provide a set of steps that are achievable and meaningful to the practitioners participating in the process.
Structure building The   2018 ) 26:607-656 factors will be measured, i. e. the states that each factor will take (e. g. positive impact, neutral impact, negative impact). With regard to causal relationships, they will be identified in two different ways, depending on the company's preferred choice: 1. Focus group meeting with key stakeholders, using DEs' domain expertise to decide upon causal relationships. This is the same approach used by  Mendes (2012 Mendes ( , 2014)). 2. Available data from past decision-making meetings (stored in the Decisions Databasesee Fig. 3 ), which is used to learn the relationships between value factors, via the same approach by  Mendes and Mosley (2008). The Decisions Database stores the decisions made by the key stakeholders, when using the Value tool for decision-making.
Uncertainty quantification The uncertainty quantification step represents the quantitative component of a BN, where conditional probabilities corresponding to the quantification of the relationships between factors  (Jensen 1996)  are obtained. Within the context of the research detailed herein, there are two sub-processes that are used in order to obtain the probabilities: 1. Obtaining directly from the data stored in the Decisions Database (see Fig. 3 ). This applies to all the priors for the value factors that were chosen to be parent nodes by the DEs during the structure building step. 2. Use of an additional algorithm-weight sum algorithm (detailed in Sect. 6), in combination with input from DEs to provide a solution to the semi-automatic generation of probabilities for the child nodes part of the value estimation BN model.
As data is gathered in the Decisions Database, it is also important to specify with a company checkpoints in which more recent data may be used to recalibrate the BN model. Such decisions will also be part of our ongoing discussions with the industry partners and one of the topics of our future work. This paper presents empirical results for parts A1 and A2 (see Sects. 4 and 5 respectively) and uses a detailed example partially based on the data on decisions gathered from A2 to describe parts A3 to A5 (see Sect. 6). When carrying out each of the five parts abovementioned, one must have in mind whether it contributes to achieving the Framework's three aims. With regard to aim (i), Sect. 4 (in Fig. 3(A. 1 )) details the different research methods we have employed to elicit value factors and also validate the elicitation results.
2. Second, to gather data throughout several decision-making meetings, using as basis the value factors jointly agreed upon by the key stakeholders and relating to the selection and ranking of decision items (e. g. features, requirements, ideas). Such data will be later on used to partially build a value estimation BN model for the company (company-specific model). The value estimation functionality will also be added to the Value tool, to be available for use by companies as an additional functionality. Details on this step are given in Sect. 6. The Value tool (Fig. 5 ) was co-created with all four industry partners, using a design science approach  (Hevner et al. 2004 ) through an iterative process involving interviews, elicitation of requirements and the progressive tool implementation and assessment.
Each of the meetings is composed by stakeholders, decision items, value factors and measures. During a meeting, stakeholders follow three sequential steps: individual assessment, group assessment and final decision. Each of these three types of assessment will be explained next using data from a pilot study that took place with one of the industry partners (company A) on the 1st of December 2015. This pilot study was attended by two of the three key stakeholders making decisions relating to product B (see Sect. 4 for further details on the stakeholders and also the value factors that were decided upon). These two key stakeholders were the product marketing director (PMk) and the product manager for the entire product family to which product B belongs (PF).
13 ). First choice The BN model's structure (value factors to be parent nodes, value factors to be child nodes, other possible structure 'simplification' nodes; all relationships between nodes) is defined in meetings with the key stakeholders. OR Second choice Use steps 1 to 4 below as a detailed mechanism to build the model's structure. Note that we are using herein as possible states to measure the impact of decisions the same ones that were decided upon by company A, i. e. positive, neutral and negative. Step 1. Arrange the data, as shown in Table  2 , for the range of decision-making meetings that are to be considered for model-building. All the data comes from the Decisions Database (see  Fig. 3 in Sect. 3). Such data will be saved into an excel file, for use by BN structure learning algorithms  (Mendes and Mosley 2008).
Child nodes There are three main challenges relating to the uncertainty quantification of child nodes, as follows: (i) the data gathered in the decisions database (Fig. 3 ) would need to be large and quite diverse to offer a sufficient range of combinations so to learn probabilities for all the childs' different states. Fig. 14  Frequencies for the parent nodes for company A's example value estimation BN model using real data from the pilot study (see Table  3)  This was the problem faced by  Mendes and Mosley (2008)  when learning probabilities for child nodes directly from data. (ii) There may be additional factors that the DEs (key stakeholders) may decide to add to the model, which were not elicited previously, and for which there is no existing data in the decisions database (one such example is factor 'Customer Satisfaction' in Fig.
If the same occurs in relation to all the other scenarios used, then one can say that the model has face validity. However, note that a model that has face validity does not mean the same as a model that has been 'shown/demonstrated' to work. This is why we have an additional method also being used, outcome adequacy, and explained next. Outcome adequacy uses real data from past decision-making meetings, rather than hypothetical scenarios, to compare what the BN model is suggesting as the state (positive, neutral, negative) for node overall value with the highest probability to the state that is obtained for a given scenario (e. g. row in Table  2 ) based on data from past decisions. With regard to company A, and also any other companies using the VALUE framework, outcome adequacy will be measured using the following steps: 1.
Implications to research and practice Implications of this work to research This is the first time that a framework that incorporates and intertwines a conceptual framework of knowledge creation  (Nonaka and Toyama 2003) , Web-based tool support, and a process for the knowledge engineering of Bayesian networks  (Mendes 2012) , is put forward for use within the context of value-based software engineering, as means to help improve value-based decision-making, and also to estimate the value of decisions relating to software/software-intensive products. This   4 ). We also put forward that the framework detailed herein can be applied to other contexts within value-based software engineering, and even outside VBSE (e. g. organisational governance). This means that additional extensions to the Value tool, and also to the way estimation models are to be built, can be proposed and validated, so providing an opportunity to widen the range of applicability of the initial framework to areas other than product management.
P7 Implementing mobile electronic patient reported outcomes (EPRO) in a long-term trial with an aging and diverse population Ashley Hogan, Nicole Butler, Ashley N. Hogan, Alla Sapozhnikova, Ella Temprosa George Washington University Correspondence: Ashley Hogan Trials 2017, 18(Suppl 1):P7 The clinical research enterprise cannot escape the shift from paper case report forms (CRF) to direct data entry of case report forms. This daunting shift has the potential to reduce the workload of clinical sites and provide an environmentally friendly source of data collection while also improving the quality and integrity of the data by removing the chance of transcription errors, minimizing missing data, allowing for real time logic and range checks, and leading to faster database locks. For our long-term multi-center clinical trial, the use of mobile electronic patient reported outcomes (EPRO) for self-administered questionnaires was a clear first step towards this shift implemented within our custom built web-based data collection and management system called MIDAS (Multi-modal Integrated Data Acquisition System).
Mobile EPRO for self-administered questionnaires increases flexibility of visit flow, and allows for the collection of sensitive information such as questions about sexual health. Concern for EPRO implementation and anxiety flourished when making considerations for the aging and diverse population of the trial for whom the use of mobile devices may be less ubiquitous. The aging study population is majority female, and ethnically diverse. During the implementation of EPRO for self-administered questionnaires, we kept in mind the needs of elderly participants with cognitive decline, dexterity problems, and visual impairments as well as the needs of participants who speak English as a second language or those with disabilities in reading and writing. Data collection via mobile data entry for six self-administered questionnaires began in August 2016 and was accompanied by a survey to assess user acceptability.
Monitoring the Primary Outcome Data In order to monitor the completeness of the primary outcome data, a monthly reporting system was developed by the trial statistician to allow close analysis of data completeness. The reports revealed 17. 9% of missing primary outcome data from babies known to be alive at SD28. A large proportion of these did not have a reported SD28 CUSS. This was due to a variety of causes, including transfer out of neonates prior to SD28 from the recruiting site to smaller nonparticipating units.
Measures to optimise the data The monthly reports allow the TMG to take measures to maximise the completeness of the data obtained from the study. A transfer pack was developed to inform new sites of required information and an accompanying letter provided, to enable PIs to request primary outcome data from colleagues within the new units. The SD28 window was extended from +/-3 days to -5/+10 days of SD28. This was considered by the neonatologists on the TMG, and the independent members of the TSC, to possess the same clinical validity. If no scan had been obtained within the new extended timeframe, our medical experts, or, in some cases, independent expert, were given approval to impute the primary outcome given sufficient supporting clinical evidence.
Result and Conclusions This monthly report system provides the information to allow the trial manager to contact the site teams a few days before each randomised baby reaches SD28 to remind them of the need to perform a CUSS and this has proved very effective in optimising the data completeness. This piece of work shows the value of the statisticians and data team on the trial management team working together to improve the scientific integrity of the study. Our missing primary outcome data currently stands at approximately 1. 4%. Liaising closely with research site teams and maintaining good relationships is crucial to trial success.
P9 Data monitoring committee overseeing multiple international randomised controlled trials Virginia Chiocchia 1 , Susan Dutton 2 , Rutger Ploeg 3   1   Centre for Statistics in Medicine (CSM) and Surgical Intervention Trials Unit (SITU), University of Oxford; 2 Oxford Clinical Trials Research Unit (OCTRU) and Centre for Statistics in Medicine (CSM), University of Oxford; 3 Nuffield Department of Surgical Sciences, University of Oxford Correspondence: Virginia Chiocchia Trials 2017, 18(Suppl 1):P9 The COPE (Consortium for Organ Preservation in Europe) include three clinical trials to improve preservation and reconditioning strategies for kidneys and livers procured for transplantation aiming to increase the number and quality of grafts used. Despite the trials being led at different centres, they are centrally managed from one main centre where the Principal Investigator, the project's governance and management are based.