dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2008). An example of this is global climate change prediction modelling. Climate research on this scale contains huge datasets from numerous sources and running multiple scenarios on supercomputers or 'gridded' machines (networks of computers working together in a computational grid) across the world (Whitmire, 2013). These largescale revolutionary digital platforms underpin many of today's scientific advances from the initial concept to the production of scientific outputs  (Andronico et al. , 2011; David et al. , 2006; Karasti et al. , 2010; Monteiro et al. , 2013; Schroeder, 2007)  and have been promoted under different labels in different contexts. For instance, in the United States (US) such research infrastructures are referred to as 'cyberinfrastructures', in the United Kingdom (UK) 'e-Science' infrastructures and in Europe 'e-infrastructures'  (Schroeder et al.
, 2006; Karasti et al. , 2010; Schroeder, 2007). In this context, e-infrastructures can be conceptualized as information infrastructures and play an increasing role in the advancement of knowledge and technology and their utilization by allowing interoperability in networked IT-service delivery  (David et al. , 2006; Eriksson and Goldkuhl, 2013; Luo and Olson, 2008; Spyridonis et al. , 2015). Hanseth and Lyytinen (2010, p. 4 ) define the notion of e-Infrastructure as follows: 'a shared, open (and unbounded), heterogeneous and evolving socio-technical system consisting of a set of IT capabilities and their users, operations and design communities'. Abstractly, the building blocks of an e-infrastructures are as follows: (1) the bottom layer includes network services, scientific tools and datasets; (2) the middle layer is the Grid layer containing networked data processing centres and middleware software as the 'glue' of resources; and finally (3) the upper and highest level includes researchers and scientists that perform their everyday activities, work together and share and access data and services, possibly through a science gateway, irrespective of their geographical location.
Research methodology Survey To assist WACREN in the development of regional NRENs and their services, we conducted a survey of user requirements of networked services. Since the study was exploratory in nature and a large number of respondents from across the region had to be recruited, a questionnaire survey was chosen to be the best method for data collection  (Creswell, 2013). To reflect different stakeholder areas (teaching, research, technical management) the questionnaire was divided into three main parts: NREN Services for Education, NREN Services for Research, Networking Technology and Application Management. Respondents were able to fill in all sections if they wished. Based on a review of NREN services, a report was initially produced that described the range of services offered by NRENs across the world  (Taylor and Abbott, 2015).
Data collection Participants were identified on an on-going and iterative search of potential key contacts provided by TANDEM project members including RRENs (WACREN, Ubuntunet Alliance, GE ´ANT and RedClara), specialist research institutes with links across the WACREN region (IRD and CIRAD), specialist African NREN/e-Infrastructure development researchers (Brunel) and African NREN/ e-Infrastructure dissemination specialists (SIGMA). The population size of this study was unrestricted, targeting scientists, scholars, researchers, administrators and higher education students from the West and Central Africa. Participants from other continents were considered on the condition that they had some direct involvement in teaching and/or research and or network administration activities. This on-going recruitment phase produced a sample of more than 2,000 potential participants identified from multiple channels including African Universities and Research Centers, Academic Search Engines, CIRAD and IRD Mailing Lists, the WACREN Community List, ICT4D and Sig Glob Dev Mailing Lists.
Data analysis Data was taken from LimeSurvey and cleansed to verify duplicate, incomplete, incorrect and inaccurate information to ensure quality data and reliable results. Free-text fields were checked for inaccurate/incorrect data. Data was then analysed on a per-question basis and findings were written up as a report. French responses were translated into English (by a French speaking translator) and then analysed. Additionally, data findings from each question were combined and cross-referenced where appropriate. The results were reviewed by the TANDEM project team. Queries arising from the review were followed up and a new report was written. This cycle was repeated several times and resulted in a final report on which this article is based.
Commonly used search engines We asked respondents about their commonly used search engines. Google and Google Scholar were by far the most popular search engines among researchers, accounting for about 44% and 20% of responses respectively. ResearchGate (5. 95%), ScienceDirect (5. 67%) and PubMed (4. 53%) were the last three of the top five most frequent answers. The list of other search engines used by WACREN researchers are shown in Table  4. This also indicates the range (and possible confusion) of what respondents consider to be a 'search engine'. For example, ResearchGate and Academia are academic social networks, ScienceDirect, PubMed, JSTOR, EBSCOhost (Research databases), Web of Science, and IEEE Digital Library are full-text Databases, Wikipedia is a free encyclopaedia on the web and HINARI (Access to Research in Health Programme) is an organization that promotes free or very low cost online access to the major journals in biomedical and related social sciences to local, not-for-profit institutions in developing countries.
Reinterpreting this question, this gives more insight into the wide range of tools that researchers use in their research. It is understandable that Google/ Google Scholar are the most used by researchers as they are free-to-use search tools. Although not search engines, some researchers might use the search tools within ResearchGate, ScienceDirect and PubMed to search the full-text databases that they represent. If this is the case, then the question remains as to how researchers can access these facilities in a reasonable time via a good communication network.
Journal articles and datasets access As expected, while a significant proportion of respondents (89%) regularly access online conference and academic articles, the majority of them (75%) indicated that their network causes access problems by disagreeing with the following statement: 'My network never causes me any problems when I access As seen in Table  5 , datasets related to biological sciences were the most accessed archives representing the importance of topics such as molecular biology, biochemistry, and genetics to the West and Central African researchers and medical communities. Agriculture and food science datasets were the second most accessed category which indicates the potential interests of the scientific community in tackling issues such as food insecurity and malnutrition and in utilising genetic resources for the benefit of present and future generations.
The third most popular datasets were related to climate change and energy security. 'Social issues' was possibly incorrectly identified as a dataset and possibly indicates that some end users have a different interpretation of "dataset" to others. Rather than ignoring this need, it indicates that for some end users further discussion is needed to understand their data needs. Both this and the previous search results indicate researchers are active users of 'common' network services. These questions were asked to establish a 'baseline' of internet usage.
Publishing open access research Respondents were then asked about the frequency of publishing Open Access Research, to which 51% of the respondents (15% very often, 36% often) publish their research with open access, 28% of researchers sometimes do this and 21% hardly ever do this (14% rarely, 7% never); 62% of them believed the network was not reliable. As approximately half the respondents use open access publishing there therefore appears to be a need for open access services such as open access data repositories.
Publishing software online Another section of the survey asked respondents whether they published software online. Researchers were not interested in creating and publishing their own software and therefore reliability of their network was not an issue to them in this regard. This might be an expected result as most researchers might be considered as being users of software, rather than creators. It is, however, worth noting that a minority of them (5%) did publish software either very often (2%) or often (3%). Worldwide there is a considerable amount of open source software that is freely available under sharing licences through facilities such as GitHub. Arguably a very small percentage of researchers actually develop and publish software. However, these researchers can make a large-scale impact as they facilitate research in their communities via their software (e.
Software application requirements In order to identify the types of software applications that might increase user productivity, we asked researchers about software they needed to use in their research. Table  6  shows the most needed software by respondents (above 1% popularity) to use in their research. It is evident that West and Central African researchers need to frequently access a wide variety of software ranging from licensed to open source. 'SPSS' (16%) and 'MATLAB' (14%) were by far the most popular and desired applications. This represents the tendency of the surveyed researchers towards statistical analysis, forecasting and modelling tools. The challenge, however, in this regard is that the procurement, upgrades or changes to such software are expensive and often beyond the reach of common investors.
For example, SPSS is an expensive analytical tool costing up to £1,000 for an individual license and £10,000 for multi-use license  (AFCAP, 2012). This is of fundamental importance to service provision to ensure that the above required applications could be delivered to scholars flawlessly, for example via service providers operating within the 'Cloud', similar to a library of applications from which the user chooses appropriate applications, or managed by a third party (via regional license provisioning) to reduce the overall cost of software. This therefore highlights the necessity for national negotiation and procurement of software licenses for use in educational and research institutions in WACREN region.
Use of networked available and desired services For several research-related networked technologies, respondents were asked to identify their use of a service and how useful that service would be in their research, subject to its availability. In so doing, we provided survey respondents with three statements: 'I use this service' and 'I do not use this service' represents the percentage of users who either use or do not use a service (Table  8 ). 'Service is unavailable' is the percentage of users who would like to use a currently unavailable service (e. g. 'desired but unavailable' as indicated below). Accordingly, the most used available services were 'Online Library Resources' (65%), 'Collaboration Tools/Document Repositories' (39%) and 'Web-based Portals for Online Data' (28%). The most desired but currently unavailable services were 'Inter-University Login' (53%), 'Data Centre access' (48%) and 'Video-/Web-based Conferencing Services' (43%).
Model Development As this is an extension of an earlier model  [16] , readers interested in the technical details can refer to the previous paper, which also includes the full model description of the SD component in Powersim language in the supplementary information (S4). The model in digital format is also available from the authors upon request. Here we restrict our explanation to the main features of the model and concentrate on its dynamic extension. Following the convention of the SD approach  [29] , we conduct various model tests as part of the model development (i. e. , boundary adequacy, structure assessment, dimensional consistency, extreme condition, integration error, and sensitivity analysis).
Model Overview Figure  1  is a simplified representation of how the model captures the ecological-economic system of the study site. It displays the key variables only to highlight the main relationships within the ecological-economic model. The economic system is embedded in the ecological system as a sub-system. The ecological system is modeled with Powersim (Powersim Studio 10, http://www. powersim. com/), an SD software, and the economic sub-system (IO) is modeled with Microsoft Excel. We integrate the economic sub-system into the ecological system by utilizing Powersim's function to connect to various datasets, including Excel. The integration is not only capable of transferring data but also allows both Powersim and Excel to run computations internally at each time step of the simulation.
This increase, however, dampens "Sole stock from the internal part of the Seine", and so on. "Soles caught originating from the internal part of the Seine estuary" involves other feedback loops such as that of "Dynamic IO". It is, however, not clear whether the feedback is positive or negative because the impacts of "Final demand for Sole (Foreign and Domestic)" on IO are mixed. 1  is a simplified representation of how the model captures the ecological-economic system of the study site. It displays the key variables only to highlight the main relationships within the ecological-economic model. The economic system is embedded in the ecological system as a sub-system. The ecological system is modeled with Powersim (Powersim Studio 10, http://www. powersim. com/), an SD software, and the economic sub-system (IO) is modeled with Microsoft Excel.
We integrate the economic sub-system into the ecological system by utilizing Powersim's function to connect to various datasets, including Excel. The integration is not only capable of transferring data but also allows both Powersim and Excel to run computations internally at each time step of the simulation. Although most economic variables are modeled in Excel (inside the dashed box), some are modeled in SD (Powersim) for technical efficiency. "Restoration rate" and "Soles caught originating from the internal part of the Seine estuary" are the key variables that connect the ecological system and economic sub-system. Although a higher "Restoration rate" quantitatively improves "Nursery areas (in the internal part of the Seine)", it incurs a "Cost of environmental measures". "Soles caught originating from the internal part of the Seine estuary" involves various feedback loops that are a source of nonlinear dynamics of the model.
50, 1. 00], as described in Section 2. 2. 3. Finally, we select five outcome indicators that represent the system's performance  [9]. The three economic indicators are GDP and disposable income-both aggregate economic values-as well as GOSs that represent sectoral impacts of restoration policies. The remaining two ecological indicators are soles caught, expressed in physical units, and nursery areas, expressed in physical and monetary units. TEV of the nursery area is estimated using the benefit transfer method  [53]. We adopt the value of the estuary in the TEEB valuation database  [54]. To avoid double counting of soles, the TEV includes flood prevention, material, recreation, spiritual, and cognitive services. The unit value is estimated at 85,749 Euro/km 2 /year. The outcomes of the various scenarios have been computed with a zero discount rate, as varying the discount rate from 0 to 5% did not show meaningful insights.
Acknowledgments: We would like to extend our thanks to  Olivier Le Pape ,  Benoit Archambault , and  Etienne Rivot  from  Agrocampus Ouest (UMR ESE) , to  Thomas Poitelon  from  Université de Versailles-Saint-Quentin-en-Yvelines (CEARC-UVSQ) , to  Thomas Lecarpentier  from  La Maison  de l'estuaire, and to  Pascal Galichon  from the  Grand Port Maritime du Havre. Any remaining errors and omissions are our sole responsibility.
Most of the literature on muscle segmentation is dedicated to MRI, whereas reports using CT are sparse  [25,27,41e47]. In several clinical studies and research projects, commercially or publically available software tools such as Slice-O-Matic (http://www. tomovision. com/ index. html), OsiriX (http://www. osirix-viewer. com), or ImageJ (https://imagej. nih. gov/ij/) have been used. OsiriX and ImageJ do not provide dedicated muscle segmentation algorithms, but standard segmentation tools such as thresholding, contouring regions, or volumes of similar signal intensity that define muscle, fat, or organs in combination with morphological operations can be easily combined with corrective operator interaction. Three studies [48e50], in which the three software tools were compared, reported excellent agreement between paraspinal muscle of cross-sectional area (CSA) measurements of abdominal and paraspinal muscles, despite the use of a simple threshold or even fully manual segmentation.
With the widespread uptake of 2D and 3D single molecule localization microscopy, a large set of different data analysis packages have been developed to generate super-resolution images. To guide researchers on the optimal analytical software for their experiments, we have designed, in a large community effort, a competition to extensively characterise and rank these options. We generated realistic simulated datasets for popular imaging modalities -2D, astigmatic 3D, biplane 3D, and double helix 3D -and evaluated 36 participant packages against these data. This provides the first broad assessment of 3D single molecule localization microscopy software, provides a holistic view of how the latest 2D and 3D single molecule localization software perform in realistic conditions, and ultimately provides insight into the current limits of the field.
In the absence of common reference datasets and reliable assessment procedure of 3D software performance, it is not possible to objectively assess how different software affects final image quality, or which algorithmic approaches are most successful. Crucially, end-users cannot determine which 3D SMLM software package and imaging modality is optimal for their application. We therefore ran the first 3D localization microscopy software challenge, to assess the performance of 3D SMLM software. We generated synthetic datasets for three popular 3D SMLM modalities: astigmatic imaging, biplane imaging and double helix point spread function microscopy. We also ran a second 2D localization microscopy software challenge, to reassess the 2D SMLM software state-ofthe art on new, tougher, more realistic datasets.
Our simulations incorporate experimentally acquired point spread functions for maximal authenticity, used signal and noise levels based closely on common experimental conditions, and incorporated a realistic 4-state model of fluorophore photophysics  18. Our synthetic data was designed to mimic two common classes of cellular structure: narrow line-like microtubules (MT) and larger tubes similar to the endoplasmic reticulum (ER) or mitochondria. Our simulations also included conditions with low density (LD) of active fluorophores, used experimentally to obtain maximal resolution, and with high density (HD) of active fluorophores, used experimentally for fast or live cell imaging.
Competition design We established a large committee from within the SMLM research community, including experimentalists and software developers, to define the scope of the challenge, ensure realism of the datasets and define analysis metrics. We further opened this discussion to the whole community, through an open forum, discussing best practices for the implementation of this contest  19. Thirty-six software packages have been entered in the competition thus far (Table  S1 ). Excitingly, participation in the competition actually led at least 8 teams to their software to support additional 3D SMLM modalities, showing how competition fosters microscopy software development. In 2016, we ran a first round of the 3D SMLM competition with explicit submission deadlines, with 30 competitor teams, culmination in a special session at the 6th annual Single Molecule Localization Microscopy Symposium (SMLMS 2016).
Realistic 3D simulations Testing super-resolution software on experimental data lacks the ground truth information required for rigorous quantification of software performance. Therefore, realistic simulated 3D SMLM datasets are required. After comparison of simulated microscope PSFs with multiple experimental PSFs from SMLM microscopes around the world, we observed that a critical challenge to realistic 3D SMLM simulations was to accurately model the experimental microscope PSF for each 3D modality. Even experimental 2D PSFs showed significant aberrations away from the focal plane (Fig  S9 ). 3D SMLM inherently involves addition of aberrations to the microscope PSF to encode the Z-position of the molecule. For the PSF models included in the competition: 2D, astigmatic (AS), double helix (DH), and biplane (BP), we observed that the PSFs showed complex aberrations not well described by simple analytical models (Fig  S9 ).
We thus combined experimental 3D PSFs with simulated ground truth by performing simulations using PSFs directly derived from experimental calibration data (Fig  1 , Methods). The experimental PSFs used to generate the simulated data are available online (Methods). As the goal of this study was to compare software obtained on typical SMLM microscopes, we deliberately chose PSFs representative of common implementations of each 3D modality. However, additional PSF engineering should improve results of any specific modality, for example adaptiveoptics corrected astigmatism  21  , or reduced Z-range, higher SNR DH-PSF designs  22. For the 3D competition, we simulated synthetic 25 nm diameter microtubules (Fig  1 ). For the 2D competition, in addition to synthetic microtubules (MT), we simulated larger diameter 150 nm cylinders, designed to approximate larger cellular structures such as mitochondria and the endoplasmic reticulum (ER) (Fig  1 ).
We incorporated a 4-state model of fluorophore photophysics, including a transient dark state (dye "blinking") and a bleaching pathway (Fig S1C ). As performance at different density of active emitters is a key challenge for SMLM software, we generated 3D competition datasets at both sparse emitter density (0. 2 mol. [molecule] μm -2 ) and high emitter density (2 mol. μm -2 ). We additionally generated a very high density dataset (5 mol. μm -2 ) for the 2D competition. We generated data at three different signal-to-noise ratio (SNR) levels, based on real signal to noise levels encountered under common SMLM experimental scenarios: fixed cells antibody labelled with organic dye  10  , fluorescent protein labelling  1  , and live cell affinity dye labelling  23, 24. Together, these simulations closely resemble experimental 3D and 2D data under a range of challenging conditions of SNR, spot density, axial thickness and test structure summarized in Table  S2.
2. Ability to successfully detect fluorescent molecules. In addition to localization precision, SMLM image resolution also depends critically on number of localized molecules  26  , so it is crucial for SMLM software to accurately detect a large fraction of molecules in a dataset, and minimize false localizations. For every frame, we identified the localizations that are close enough to a ground-truth position as true-positives (TP), the spurious localizations as false-positives (FP) and the undetected molecules as false-negatives (FN). We then computed the Jaccard index (JAC, %), which measures the fraction of correctly detected molecules in a dataset: The average JAC, lateral RMSE and axial RMSE measured the performance of a software. A very good RMSE should always read in context of the Jaccard index to check if good RMSE is not obtained only for the brightest molecules.