dataesr/scientific-paragraphs-categorization huggingface dataset:
INTRODUCTION Due the complexity of breast cancer, a multi-disciplinary team is needed to make treatment decisions. A breast cancer case may last for years and accumulated information is extensive and heterogeneous, differing greatly from one patient to another depending on the evolution of the disease. Clinical Decision Support Systems (CDSS) based on guidelines may support clinicians during the decisionmaking process, but these guidelines are sometimes too inflexible and do not provide relevant information about the case. It may also happen that there is simply not enough evidence for the current case, limiting their applicability. In order to address these problems, the DESIREE 1 project has been developed; a web-based software that aims to improve the coordination and management of information in the Breast Units (BU).
The DESIREE software includes a novel CDSS based on an advanced digital patient model, which integrates all significant sources of information to support decisions on the case and relevant information from the patient's context  [1]. In addition, the system is able to model the experience, improving and supporting the process of knowledge discovery  [2]. 1 http://www. desiree-project. eu/ Besides a CDSS, it contains a visual analytics module that represents statistical information of the patients that can support clinicians and clinical managers. This system includes an advanced visual analytics tool to study clinical hypothesis based on the acquired real world data in breast cancer. This paper is structured in the following way. In Section II, the state of the art in relation to Digital Breast Cancer Patient (DBCP) model and visual analytics tools in healthcare are presented.
Zhang et al. [5]  described an ontology-based approach to integrate datasets for cancer research. The scope of all these formalizations is to build up a digital formalization of illness or patient models for the best predictive and personalized healthcare. In this context the Digital Breast Cancer Patient (DBCP) model is a shared conceptualization of the domain of breast cancer diagnosis and treatment. It provides a set of inter-related concepts that can be used to describe the knowledge about the domain, which will be the basis of clinical decision making.
This type of analysis, besides allowing to examine the distribution of the attributes, it also allows to analyze the relationship, patterns and correlations between these variables. One of the most popular graphs for displaying multivariate data is the parallel coordinates plot  [9] ,  [10]. In the parallel coordinates plot, each of the attributes is depicted by a vertical axis. These axes are placed in parallel in such way that they are at the same distance from each other. The samples of the dataset are depicted by horizontal lines in a way that they cross the axes of the attributes taking the corresponding value in each case. The lines are typically colored following a criterion defined by the research question to which the answer is to be given. The parallel coordinates plot is ideal for analyzing many variables at one time and finding patterns in the data  [11].
B. Attributes to be studied. 572 real patients of Primary Breast Cancer from four different European hospitals were analyzed in this study. A multidisciplinary team of clinicians selected 194 attributes to use in the dataset, which are reviewed periodically. The majority of these attributes are already in the Electronic Health Records (EHR) but it had been expanded to include additional information of interest to clinicians. The types of attributes found in this dataset are: -Numerical variables: integer and float (e. g. age) -Categorical variables (e. g. gender) -Logical variables (e. g. if the patient smokes) Patients were treated with various therapies grouped in (i) surgical procedures, (ii) radiotherapy protocols, (iii) endocrine therapies, (iv) chemotherapy protocols and (v) clinical trials.
C. Use Case for Pattern recognition This section presents the obtained results for scenario D -After surgery, without neo-adjuvant therapy -and as the criterion to color the parallel coordinates lines the death of the patient (Figure  5 ). As illustrated in Figure  5 , this criterion is of a categorical type and its possible values are: true ("red") or false ("blue"). The plot in Figure  5  shows the attributes with the highest correlation to the selected criterion (i. e. death) obtained in the data mining process. Each of the axes in the parallel coordinates plot depicts one of the most correlated attributes and each of the lines shown horizontally depicts a patient. These horizontal lines are colored in red if the patient has died and in blue if the patient has not died. It is noteworthy that only lines (i.
ACKNOWLEDGMENT The  DESIREE  project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  690238. Additionally, the authors acknowledge support from the four clinical partners involved in the project:  Onkologikoa ,  Eresa Grupo Médico ,  Hôpital Européen Georges-Pompidou  and  Hôpital Saint-Louis.
Effectuation and cognition In their paper on effectual versus predictive logic in entrepreneurial decision-making,  Dew et al. (2009)  examine the behaviour and decision-making of students of an MBA programme on one side and expert entrepreneurs on the other. They noticed that expert entrepreneurs were more likely to think holistically about the business than MBA students  (Dew et al. , 2009). This means that they tackled the given problem by focusing on how to build the venture as a whole. Previous studies in the expertise literature have shown that experts have a larger mental database of actual experiences than novices and they, additionally, have better access to it  (Chase and Simon, 1973; Simon and Simon, 1978; Anderson, 1981; Camerer and Johnson, 1991; Bedard and Chi, 1992; Shanteau, 1992; VanLehn, 1996 , Feltovitch et al.
Data collection The data we collected was gathered through a series of exploratory interviews. The reason why we opted for this type of interview is because of the exploratory nature of our research work. For futurist practitioners we used an interview guide which served as a support to the principal themes from the literature review, such as cognition, participatory nature and networks, attitude towards the future, action as well as tools and approaches that are used in foresight practice. Moreover, we also conducted interviews with certain entrepreneurs in France. The reason why we chose to limit our research to French entrepreneurs is because we had an easier access to local entrepreneurs. At the same time, they are at the center of our research. We focused our analysis mostly on entrepreneurs whose project is at the startup level.
Interviews The data collection through interviews was carried out in the time period late April -mid-May 2016. In this period, 11 interviews were conducted in total. Four of them were conducted with entrepreneurs from Alsace, France while the other 5 interviews were conducted with 3 prospectivists from France, 1 interview with one futurologist from France and 3 interviews with international futurists, all experts in their fields. Most of the interviews with futurists were performed over the telephone because of the geographical distance 3. Only one interview with an entrepreneur was conducted over the phone. The rest were done in person. The interviews were designed to last 45 to 60 minutes. They were all recorded after which we immediately proceeded to verbatim transcription.
Selection and profiles of the informants The selection of the informants in the field of forward-thinking was based on a wide sample of practices. Through literature review, we were able to learn more about different practices in France and abroad. The informants were selected on the basis of their experience in the foresight field as scientific researchers, but also as practitioners. Regarding the profiles of the entrepreneurs, we selected them in accordance with the stage of maturity of their projects. As we explained above, we looked for projects in the startup stage in order to understand the way they had proceeded in starting their entrepreneurial activity. Below, we describe the profiles of both futurists and entrepreneurs in detail and we present a table with an overall presentation of the informants, type of data collection and duration of the interviews (see.
Different imaging modalities store their acquired images in various data formats such as DICOM, NIfTI, etc. These formats may not be sufficient for efficient data manipulation, analysis, and image processing. Similarly, manufacturers of medical imaging systems use different proprietary formats to store images in digital form. The problem of different file formats is even bigger for preclinical imaging devices. These format differences pose significant challenges for multimodal imaging studies particularly in registration, fusion, analysis, and curation of image datasets. Another difficulty with the difference in formats occurs if the utilized analysis software becomes obsolete, inaccessible, or undergoes repeated substantive changes that are not backward compatible  [9, 10]. Post-processing software tools for kinetic modeling, spectral unmixing, and relaxometry require exact information about time points and channels.
The X, Y, and Z dimensions provide the orthogonal spatial dimensions while dimensions T and C define time and channel dimensions respectively in this work. The X, Y, Z, T, and C dimensions are represented by 64-bit signed integers to support very large images. The voxel size is of type double and is measured in millimeters. As an option, we store scale factor and offset values to be applied to the voxel intensities. These values are stored as part of the structure. If these attributes are set to true, then the voxel intensities should be converted to float type by the software that reads the data. Our format stores all data in little endian format on the file.
Geometric Transformation In order to analyze a given image dataset, a geometric transformation between voxel space and world space is needed. This transformation is used for co-registration of images obtained from two different modalities. The proposed file format supports the use of a rigid body transformation, a subset of the general affine transformations. In rigid body transformations, voxel sizes dictate the geometric scaling to be used, solving the scaling problems encountered in the use of rotation, scaling, and translation (RST) transformations. To move from data coordinate to world coordinate, we apply the formula below: w x w y w z 2 4 3 5 ¼ R v x *d x v y *d y v z *d z 2 4 3 5 þ t where R is an orthonormal 3 × 3 rotation matrix; d and v are voxel indices and sizes to be transformed by t, a 3 × 1 translation vector.
Compression The proposed file format supports raw data (i. e. , the uncompressed voxel data) and zlib compression. We chose zlib due to its independence of CPU type, operating system, file system, and character set; its usability for interchange; and also due to its free usage without any patent issues  [17]. In using the zlib compression method, we suggest using compression level 2 as a compromise between compression speed and compression factor. Each slice of the volume is compressed separately to enable parallel compression and decompression. Then, these compressed slices, their respective starting addresses and their individual lengths in memory, are serially saved to disk in a single file as shown in Fig. 3.
Example Images Used for the Experimentation and Evaluation Two image datasets from five different mice were used. These are 3D μCT image datasets and segmentation image datasets as shown in Fig. 5. The images were obtained from experiments conducted for assessing the sensitivity and accuracy of hybrid fluorescence-mediated tomography in deep tissue regions  [19]. The average sizes of the μCT and segmentation image datasets are 338 MB and 169 MB, respectively. The μCT scans and segmentation files were visualized and analyzed using the Imalytics Preclinical software (Gremse-IT GmbH, Germany)  [20].
Discussion In this work, we proposed and developed a simple image file format that is usable for all in vivo imaging modalities generating volumetric datasets with regular grids. This new file format is particularly suitable for multimodal imaging including CT-FMT, PET-CT, SPECT-CT, and PET-MRI. We provided an implementation with few dependencies from the zlib library, OpenSSL library (optional), and Oliver Gay's version of sha256  [17, 21]. The software is platformindependent. The simple implementation is contained in two C++ header files. The file format was developed in close collaboration with device manufacturers including Inviscan (Strasbourg, France), Molecubes (Ghent, Belgium) and MILabs (Utrecht, the Netherlands) and a software supplier Gremse-IT (Aachen, Germany) to improve the interface between imaging hardware and analysis software.
Lossy compression algorithms, though results in smaller sizes, fail to be the choice for clinical use and preclinical research since they may eliminate certain critical information needed for diagnosis, analysis, and legal purposes  [27, 28]. In this work, we employ the lossless compression implemented in the widely adopted and freely available zlib library. File formats characterized by properties such as complete and open documentation, platform-independence, and lossless compression among others have been identified to have the capability of preserving their contents and functionality over a long-term  [29, 30]. Metadata controls most steps of the curation process, from preservation to access and reuse  [5, 31]. However, its usage raises concerns about anonymity in clinical research  since it may include subject's personal information and other traceable information that could raise privacy concerns  [32].
Due to this and other complexities, certain image file formats such as NIfTI-1 provide limited support for the storage of the various image acquisition parameters as opposed to what is available in DICOM  [33]. Also, writing a single large file to disk is comparatively more efficient and cost-effective than writing several small files to disk. [34]. Hence, in our file format, data is written as single file per modality as opposed to other file formats. To assess the performance of our file format, we measured the rates, time, and ratios of compression and decompression and also measured time and rates of both writing and reading on a network drive. We took particular interest in the network drive analysis since it happens to be the most heavily used drive in research centers, institutes, and clinical centers.
The creation and checking of timestamp introduces some overhead in the writing and reading of files, respectively. However, considering the benefit of the timestamp and even the negligible time overhead for hash creation observed in our measurements, it makes sense to go for the timestamp and achieve the integrity goal. It is important to note that the inclusion of the timestamp is optional and could be added later (e. g. , in cases where internet connectivity is a problem). We also make the source code and sample datasets freely available for use and reproducibility purposes and also as a step in cutting down on replication costs  [37]. Reproducibility of science is an aspect of credibility that requires researchers to provide proper and sufficient information as well as accurate documentation to enable the verification of their work  [38, 39].
Funding information. This research was supported by the  German Academic Exchange Service (DAAD) ,  German Research Foundation  (DFG;  GR 5027/2-1 ),  German Higher Education Ministry (BMBF)  ( Biophotonics/13 N13355 ),  Federal Government of North-Rhine Westphalia (EFRE) , and the  European Union  ( FP7) , and a grant from the  Interdisciplinary Centre for Clinical Research  within the faculty of Medicine at the  RWTH Aachen University  ( E8-13 ).
Recent technological advances have transformed the research on physical activity initially based on questionnaire data to the most recent objective data from accelerometers. The shift to availability of raw accelerations has increased measurement accuracy, transparency, and the potential for data harmonization. However, it has also shifted the need for considerable processing expertise to the researcher. Many users do not have this expertise. The R package GGIR has been made available to all as a tool to convert multi-day high resolution raw accelerometer data from wearable movement sensors into meaningful evidencebased outcomes and insightful reports for the study of human daily physical activity and sleep. This paper aims to provide a onestop overview of GGIR package, the papers underpinning the theory of GGIR, and how research contributes to the continued growth of the GGIR package.
The package includes a range of literature-supported methods to clean the data and provide day-byday, as well as full recording, weekly, weekend, and weekday estimates of physical activity and sleep parameters. In addition, the package also comes with a shell function that enables the user to process a set of input files and produce csv summary reports with a single function call, ideal for users less proficient in R. GGIR has been used in over 90 peer-reviewed scientific publications to date. The evolution of GGIR over time and widespread use across a range of research areas highlights the importance of open source software development for the research community and advancing methods in physical behavior research.
, 2013). However, this technological advance is counterbalanced by the large amount of data collected per measurement (typically 2•10 8 data points per week of measurement) and the necessity to process the data to obtain meaningful variables that can be used in standard statistical analysis and software. Many physical activity and sleep researchers do not have the expertise necessary to process and analyze raw accelerometer data. The GGIR software presented in this paper facilitates the processing and extraction of insightful physical activity and sleep variables of the data collected with these so called raw data accelerometers from three widely used sensor brands  (Wijndaele et al. , 2015). The individual algorithms that are embedded in GGIR have been described across a number of published papers.
The fast advances in wearable sensor technology over the last decades comes with the price of mandatory development of scientific software to ensure a good valorization of the newly available sensors  (Seinstra, Wallom, & Keahey, 2015). However, scientific software instruments need to be subjected to a peer review process as it is the case for other methodological components (e. g. , algorithms and study protocols). Previous publications related to GGIR focused on specific algorithms, such as sleep detection, and their validity. However, those publications did not focus on GGIR as a generic piece of scientific software that connects all these algorithms and adds a range of essential extra functionalities (e. g. , time zone and daylight-saving time handling) to provide value to an entire research community far beyond the specificities of those studies.
How Open Source Software Can Contribute to Advances in the Field of Physical Activity Measurement GGIR contributes to scientific discovery by enabling researchers to study (human) physical activity and sleep using accelerometers without pre-required programing expertise. GGIR is appropriate for use across a wide variety of study designs (e. g. , variations in measurement duration, in sample frequency, instructions given to the participant) and study populations. Applications of GGIR have been reported in over 90 peer reviewed journal publications since its first release in 2013, with 24 in 2017 and 48 in 2018 based on looking up the citations to the key journal publications underlying GGIR (see also: https:// github. com/wadpac/GGIR/wiki/Publication-list). Additionally, nine methodological papers were written to motivate and evaluate parts of GGIR.
Previous accelerometer software has been commercial (e. g. , Actilife) and/or restricted to one brand of accelerometer (e. g. , Actilife, GENEActiv PC combined with Excel macros). GGIR facilitates the processing and analysis of data from three of the most widely used brands of research-grade movement sensors (GENEActiv by ActivInsights Ltd, ActiGraph by Actigraph LLC, and Axivity by Axivity Ltd) using open source generic brand agnostic methods, potentially providing a means for harmonization of data from large surveys globally  (Rowlands, Mirkes, et al. , 2018). Further, GGIR is continually updated to include innovations developed by the software team and/or users expediting the application of novel analytics in research  (Rowlands, Edwardson, et al. , 2018; Rowlands, Mirkes, et al. , 2018; van Hees et al.
, 2014 van Hees et al. , , 2015 van Hees et al. , , 2018)). GGIR is very much a community-driven development as testified by the multiple contributors from both health research and technological backgrounds, the fact that many publications that use GGIR are not co-authored by the development team, and the existence of a support and maintenance service by V. van Hees where GGIR users can hire van Hees' time as freelancer to help address specific needs from the user-community (www. movementdata. nl). This service fueled a range of package upgrades in 2018, and is one of the possible ways to sustain open source software like GGIR. The availability of a user forum makes it possible for users to reach out to each other (see Table  1 ). Previously, accelerometers were typically either used to assess waking physical activity or sleep and circadian rhythms, but rarely tailored for both research areas.
The new generation of accelerometers, worn night and day, allows the measurement of both physical activity and sleep using the same tool. Moreover, in several studies, such as UK Biobank  (Doherty, Jackson, et al. , 2017) , a sleep diary was not used to define a sleep window as commonly done in sleep research. The GGIR package allows differentiation of waking from sleep periods and provides sleep quality parameters. Using the GGIR package, the Colaus study reported better sleep efficiency among those more physically active  (Gubelmann, Heinzer, Haba-Rubio, Vollenweider, & Marques-Vidal, 2018). Several papers using UK Biobank data have now been published and show the advantage of using accelerometer instead of questionnaire data to identify, for example, the genetics of sleep  (Jones, Lane, et al.
The data file is then downloaded with the same desktop software from the accelerometer manufacturer. The file size typically ranges between 0. 5 and 1. 5 GB depending on specific format, sensor brand, and recording duration. GGIR can either run on a local computer or be integrated in parallel processing tasks on a computing cluster when working with large numbers of datafiles. GGIR generates reports in csv-format that can then be loaded in the researcher's preferred statistical software (e. g. , R, SAS, SPSS, or Stata).
Related Work Software. A few other software tools exist to work with multiday raw accelerometer data (Table  2 ). Actilife (ActiGraph, Pensacola, FL, USA) is a closed-source commercial software designed for the accelerometer hardware developed by the same company. The GENEActiv PC software by the developer of the GENEActiv (ActivInsights Ltd, Kimbolton, UK), in combination with freely available Excel macros (available from https://open. geneactiv. org), is designed for the accelerometer hardware developed by the same company. GENEAclassify is an open source R package primarily aimed at facilitating the segmentation and classification of accelerometer data produced by the GENEAactiv accelerometer  (Campbell, Gott, Langford, & Sweetland, 2018). OMGUI (Open Movement, Newcastle University, UK) by Dan Jackson and colleagues is an open source GUI implemented in C# and developed for the open source hardware AX3  (Jackson, 2018b).