dataesr/scientific-paragraphs-categorization huggingface dataset:
However, the SMRT architecture could accommodate such an enhancement with little change; only an adequate solver needs to be added (e. g. ,  Lacroix et al. , 2008). Finally we acknowledge that the Python implementation of SMRT bears some peculiarities. By extensively using Python dynamic capabilities, the model computation is probably less efficient than specialized code, even though numerically critical code is delegated to optimized libraries through SciPy. Because of Python, the model may be inadequate for high-performance computation. In this case SMRT may still be useful for prototyping and determining the optimal subset of formulations that could then be implemented in compiled language since a numerical reference greatly helps to achieve such an optimization step. Moreover, it is worth noting that the Python ecosystem for high-performance computing is fast improving and that SMRT code may be parallelized in the future.
Regarding HUT, SMRT contains no sufficiently similar configuration to perform a validation. Nevertheless the language binding to the HUT code has been included for future comparisons with other configurations. Not all SMRT configurations and available microstructure representations have been tested in this study because of the large number of possible combinations; this is left to future work. Several limitations of SMRT version 1. 0 have been outlined that can be readily overcome by model extensions which are supported by modularity. The developed code is highly structured for each step of the radiative transfer calculation. The model is designed to facilitate future developments of existing and new formulations without changing existing code, which should foster community-based contributions and consolidate SMRT as a repository of the community knowledge.
As a consequence only the first three components need to be computed in practice. In the passive microwave mode the situation is even simpler. The incident energy comes from the atmosphere (downwelling atmosphere emission), which is considered azimuthally symmetrical, so that only the m = 0 mode is nonzero and as both the forcing (incident radiative) and the source (emission) have an azimuthal symmetry, the m > 0 mode equations all have a zero solution. As a consequence for passive microwave, only the m = 0 mode needs to be solved and only the first two components of the Stokes vector are nonzero. The SMRT DORT code is built to accommodate a variable number of Stokes components which allow optimized computations. All boundary conditions provide 4 L l=1 N (l) equations linking the unknowns x (l),m for each of the L layers since the boundary conditions only connect successive layers; the system of equations takes the form of an almost block diagonal matrix.
Methods We recruited local people with self-owned Android smartphones installed with open-source survey software (OpenDataKit (ODK)) and open-source navigation software (OpenStreet-Map Automated Navigation Directions (OsmAnd)). Surveyors were paired with local motorbike drivers to travel to eligible villages. The collected mapping data were validated by checking for duplication and comparing the village names against a pre-existing village name and location list using a geographic distance and text string-matching algorithm.
Between May 2014 and April 2016, Tonkolili District reported 406 EVD cases including 162 EVD deaths [MSF, personal communication]. Tonkolili District did not have an Ebola Management Centre (EMC) until December 2014, when Medecins Sans Frontières (MSF) constructed an EMC in Magburaka. The EMC was constructed in 12 days, with a 100-bed capacity, medical and hygiene team, an on-site laboratory, epidemiology, water and sanitation, health promotion, outreach and mental health counselling teams. With each new admission to the Magburaka EMC, MSF staff recorded all patient information, including the origin of the patient, through patient interview or from the ambulance team if the patient was unconscious. This information was entered into a line list by MSF data encoders based in the Magburaka EMC.
Mobilisation of local people We recruited survey teams in Magburaka town by selecting local residents as surveyors based on their ownership of a fully functional Android smartphone, fluency in both Temne and Mende (local languages) and a good command of English. To transport the surveyors, we selected local commercial motorbike drivers known as 'Okada' drivers. Okada drivers are a popular mode of transport in Sierra Leone due to their affordability, in-depth knowledge of the villages and ability to navigate the sometimes difficult terrain in remote areas of Tonkolili District and neighbouring chiefdoms in Bombali District. One surveyor was designated as survey team leader based on their natural leadership of the team during training, rapid acquaintance with the technology and knowledge of the areas to be surveyed in Tonkolili and Bombali chiefdoms.
Training and technology sources used We used smartphones with Android, rather than Symbian or other operating systems, due to their reasonably high market penetration and the availability of high-quality Android-compatible open-source software. The Android smartphones of the survey team members were installed with open-source software for data collection (OpenDataKit (ODK) www. opendatakit. org) and navigation (OpenStreetMap Automated Navigation Directions (OsmAnd) www. OsmAnd. net). Both are freely available for download from the Google Play store, but could also have been installed on Android smartphones via USB. The survey teams were trained how to collect the survey data using ODK and the village location coordinates using OsmAnd. As all Android smartphones were self-owned, no training was required for device use.
Open Data Kit (ODK) collect software and data collection ODK is an open-source tool to create and manage mobile data collection. To maintain the security of collected data, our methodology involved some advanced setup, including changing the ODK default settings from connecting to a public website, to instead, routing the connection to a local password protected web-based aggregation platform operated by MSF. To collect information, we designed a structured survey form with questions, prompts and cascading functions using an XLSForm, which is a form standard that simplifies authoring and modification of forms for ODK in Microsoft Excel (http://xlsform. org/). The XLSForm was then converted to an XForm, which is an open form standard supported by ODK and other data collection platforms. The finished survey form was uploaded to the MSF web-based aggregation platform, from where only authorised Android smartphones could download it (S1 Fig).
A how-to guide for use of open-source software to collect mapping and survey data using smartphones can be found in S1 Text. The survey form automatically captured information about the survey start and end time, assigned a device identification number for each Android device used, an unique identifier for each form filled and finally the time the survey was uploaded to the server. Upon manual prompt, the survey captured village geolocation (S2 Fig). The surveyors then interviewed the village chief or head of village and manually entered the following information: village name (and common variants); district, chiefdom, ward, constituency and zone of village; name and contact information of village chief and local health-care worker (HCW); approximate number of households in the village as proxy of population estimate.
Ethics statement This study was carried out as part of an operational outbreak response to enable improved and targeted health response during the Ebola outbreak. The study was approved as not requiring formal review from the MSF Ethics Review Board (ERB) by the Medical Director of MSF Operational Centre Amsterdam (MSF-OCA), as it did not involve clinical data or human subjects research  [6, 7]. However, it was recommended that usual standards of care surrounding consent, data protection, and confidentiality, and harm/benefit ratio were adhered to. Consent was obtained by verbal permission from each village head. Personal data collected during this study, such as name and telephone numbers, were not collected to be analysed as part of the research, but rather for administrative reasons. Data confidentiality and security were maintained by storing the collected data on a password protected web-based aggregation platform operated by MSF.
Validation of mapping data We calculated proportions using the non-missing values of each variable as denominator. We performed the descriptive analysis of the collected survey data using Stata 14. 2 software  [9]. For initial validation of the mapping data, Okada drivers and Tonkolili District Health Management Team members were involved during data collection, as they have in-depth knowledge of village names in the districts. The second validation step was carried out using an algorithm. We focused our analysis on the chiefdoms extensively visited by the survey teams. The list of villages collected (survey list) was compared to a pre-existing 2004 Statistics Sierra Leone list. This was done using a matching algorithm developed internally in the opensource statistical analysis program R  [10] , which produced the top three possible matches between two lists of villages according to a weighted combination of the results of both geographical distance between the GPS coordinates and the string distance between the two village names (Fig  1 ).
Descriptive analysis of survey data collected Each team surveyed 8-10 villages per day, collecting data from 809 villages in Tonkolili District and 82 villages in Bombali District (Table  1 ). Along with the official name, 219 (24%) villages had an alternate village name recorded (209 in Tonkolili District, 10 in Bombali District). The names of 887 village chiefs were recorded, of which 841 (95%) provided a mobile number. The surveyors collected the names of 836 local health workers or the name of the local responsible person, if the village did not have a health centre. They also collected the mobile phone number for 708 (85%) health care workers or local responsible people (Table  1 ). As additional variables were added to the survey in the middle of the data collection process, a complete set of data is not available for all items (Table  2 ).
Discussion This project is an example of an innovative and rapid approach to obtain vital mapping and village population data using accessible technology in genuine partnership with the local community in the context of emergency response. The time required to train the surveyors was minimal and no device training was necessary, as all Android smartphones were self-owned. The pairing of local Okada drivers and surveyors allowed rapid data collection and helped overcome possible language barriers. The survey was conducted in less than two weeks, and the geographic information from 891 villages was applied to create up-to-date maps of Tonkolili District. Following completion of this project in February 2015, the number of EVD cases in Tonkolili district was decreasing  [12]. However, at that time, it was unclear if the EVD outbreak would continue to decline.
Voluntary provision of this information was high during the survey. We shared the geolocation data with the District and National Ebola Response Centres. We also submitted our data to OSM, who updated their mapping database with the collected geolocation and village name information, thus providing a freely available updated map of Tonkolili District. This survey contributed to improved maps of EVD affected areas produced by the Missing Maps project (http://www. missingmaps. org/), a collaboration involving MSF, the American and British Red Cross, the Humanitarian OSM Team and volunteers working to create free, digital maps for countries affected by Ebola  [14]. In 2016, the survey healthcare and geolocation data were used by MSF in collaboration with the MoHS and partners in Tonkolili District to revitalize the IDSR system  [15].
The advantage of this survey was the minimal set of requirements necessary to securely collect data: one laptop; a server, such as a small mobile device which can operate via internet and range in cost from €10-25; a local web-based aggregation platform; ODK, OsmAnd and QGI-S 1 open source software; Android smartphones and affordable local transport. The opensource software used in this survey is known to be virus-free, does not have any advertisement or need for payment, and the collected data belongs to MSF and is secured on an MSF password protected server. In 2014, over half (55%) of Sierra Leonean households surveyed owned a mobile phone  [11] , therefore using smartphones was feasible by selecting local residents who owned one, and were willing to participate in the study. Thus, there was no need to purchase devices for short term use, and no device training was required.
Tonkolili and Bombali districts were stable in January 2015  [18]  and collection of data by the surveyors was widely accepted in the communities visited, as everyone recognised their involvement in an important project that contributed to their own community. For the surveyors, it was also an opportunity for work experience in data collection and financial gain. Our study had several limitations which can serve as lessons learned for future use of this method. First, more knowledge from Okada drivers, locals and satellite imagery could have been used in the final validation of village names. However, this mapping was an emergency operation to meet Ebola response needs for improved mapping information. The data were collected within 11 days under strict curfews and travel restrictions. For future surveys, where possible, more local involvement would be advantageous.
Acknowledgments We thank the Okada drivers and surveyors for their hard work and dedication and all those who willingly participated in this survey. We thank the  MSF Field GIS Officers (MSF-OCG)  for producing the updated maps;  Sarah Venis  ( MSF UK ) and  Sidney Wong  ( MSF-OCA ) for their useful comments and the  MSF  staff in Magburaka, in particular,  Stanley Bockarie , for coordination of the survey teams.
There are also a few academic papers related to general guidance for ecosystem service assessment (e. g. Gómez-Baggethun et al. , 2016; Jacobs et al. , 2016; Pascual et al. , 2016; Seppelt et al. , 2012). In addition, some guidance documents have been developed through major international initiatives such as The Economics of Ecosystems and Biodiversity (TEEB) (see  TEEB, 2011; 2013)  and the Intergovernmental Panel on Biodiversity and Ecosystem Services (IPBES) guidance on the diverse conceptualisation of multiple values of nature and its benefits  (IPBES, 2016a). The majority of these guidance documents describe an overall ecosystem service assessment approach broken down into steps and/or checklists sometimes with associated indicators and/or methods. Several websites provide access to multiple ecosystem service methods or tools, for example, the Ecosystem Knowledge Network's Tool Assessor 1  , the NEAT Tree Short Tool Reviews 2  and the ValuES Project Methods Database 3.
For example, advanced matrix approaches such as GreenFrame  (Kopperoinen et al. 2014 ; see Table  1 ) involve multiple datasets representing different types of values which are related to ecosystem service provision potential through a stakeholder process. Furthermore, some methods aim to integrate different types of data and values for a more comprehensive assessment, such as multi-criteria decision analysis and Bayesian belief networks. Finally, we also recognise that methods are not completely independent of each other. For example, there can be advantages from combining methods which build upon each other or from applying similar methods within a single case study to better capture uncertainties associated with particular methods. These issues are discussed in detail in  Dunford et al.
Method Overview Simple matrix mapping Simple matrix mapping links a spreadsheet of ecosystem service supply/demand indicators by land cover category to a GIS map, to generate maps of ecosystem service supply, demand and balance (supply minus demand). The indicators can be derived from scientific data or can be scores based on local or expert knowledge (e. g. Burkhard et al. , 2012). Advanced matrix mapping Advanced matrix mapping approaches build on simple matrix mapping approaches through incorporating multiple sources of spatial datasets. An example of such an approach used in OpenNESS is GreenFrame which was developed to assess spatial variation in ecosystem service provision potential of green infrastructure in spatial planning  (Kopperoinen et al. , 2014). The method utilises an extensive set of spatial datasets grouped into themes combined with both scientific experts' and local actors' scorings.
Instead, they were used in the project to provide a broadscale context in which the case study applications took place. A set of detailed guidelines were then written for all methods explaining the types of problem the method can be used to study, its data requirements, its constraints and limitations, the steps required to apply the method within a case study, worked examples of the practical application of the method, and further reading for use by the case studies in implementing their selected method(s). These method factsheets have been uploaded to an online platform for sharing information on natural capital, ecosystem services and nature-based solutions known as Oppla (see http://oppla. eu/marketplace). This written guidance was supported by a dedicated 2-day training workshop and supplemented by various case study visits by method experts, and method clinics and specific training sessions at project meetings.
Reasons for method selection The surveys revealed a wide variety of factors that were considered when case studies selected a method. The reasons for selection could be broadly grouped into four categories: methodologyoriented; research-oriented; stakeholder-oriented; and decision-oriented. The latter category can be sub-divided into reasons related to the overall purpose of the case study and the ecosystem services at stake. Note that the results from the surveys only cover those methods applied in the case studies from Table  1 , i. e. not the methods shown in italics which were only evaluated by the method experts (see Section 3. 2). Methodological reasons included whether data required by a method was available in the case study, whether expertise was available either in the case study team or in the OpenNESS project, the amount of time a method took to apply, and the financial resources required to apply a method.
, 2017). Methods which promote stakeholder participation or incorporate local knowledge can be useful in such circumstances, such as deliberative or participatory mapping, narrative analysis, deliberative valuation, MCDA (Table  2b ) and advanced matrix approaches (Table  2a ). Deliberative methods can facilitate social learning by creating a space for dialogue and reflection between different stakeholder groups, leading to the co-creation of knowledge. Some approaches can be implemented with or without stakeholder input, such as simple matrix-based approaches where the scoring of datasets for their ability to supply ecosystem services can be undertaken by an expert or stakeholder group depending on the study design. How easy a method is to understand or communicate is also highly relevant to method selection, particularly for studies which aim to foster social learning, inform decision-making or simply raise awareness of the importance of ecosystem services.
The decision tree also shows that outputs from some types of models (e. g. integrated assessment models that simulate land use change) can be used as inputs to ecosystem service models. Outputs from all model types can also be used in BBNs in addition to other types of data to infer relationships between different system components. It should be noted that only broad types of modelling approaches are indicated in the biophysical decision tree with a few examples as many models exist under each category, which have been reviewed elsewhere (e. g. IPBES, 2016b). The mapping branch of the biophysical decision tree asks the user what they want to map, either individual or a limited number of ecosystem services, or multiple ecosystem services. The latter leads to matrix-based approaches which vary in their complexity in terms of the number of datasets that are combined to estimate service provision.
, this issue). Finally, the commercial nature of software was seen as a barrier to uptake by some case studies for ecosystem service models such as QUICKScan (although it was made freely available during the course of the project). This highlights that, for some methods, factors that act as advantages and disadvantages are often two sides of the same coin. The matrix approach, for example, is widely selected for its ease of use, its speed of application and the fact that it is spatially-explicit and can involve stakeholders. However, these strengths are also its weaknesses: it was seen by some case studies to depend too strongly on expert knowledge and simplistic generalisations, which one workshop participant stressed gave it a 'false impression of completeness'. Similarly, BBNs are shown to be well suited for handling uncertainty in a flexible, participatory manner and giving a choice (instead of a singular output).
The decision trees were based on empirical data from the OpenNESS case studies. These case studies were mainly located in Europe, but they were set up in highly variable contexts to encompass a wide range of socio-cultural, ecological and policy contexts. The four non-EU case studies of OpenNESS increase the applicability of the decision trees outside of Europe, although we recognise that they are probably not sufficient in number or variety to assume that the decision trees are widely applicable throughout the globe. Furthermore, most case studies in OpenNESS were landscape level or local scale studies, so the choice of methods largely reflects experience with local to regional scale assessment and valuation choices. To overcome these potential limitations and ensure that the decision trees were generic enough to apply to other cases and regions outside of the OpenNESS project, their design was based on an iterative process that combined the empirical findings from the case studies with the expert judgement of method experts.
Acknowledgements The authors are extremely grateful to all the case study teams and case study advisory boards who provided input to the surveys and project workshops [add case study leader/contributors names]. The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no  308428 ,  OpenNESS Project (Operationalisation of Natural Capital and Ecosystem Services: From Concepts to Real-world Applications  (www. openness-project. eu). M. Garcia-Lorente was funded by a grant from the  Spanish National Institute for Agriculture and Food Research and Technology , co-funded by the  Social European Fund (Doc-INIA CCAA). D. Tuomasjukka  was funded by grant agreement  308393 ,  OPERAs  project (Operational Potential of Ecosystem Research Applications; http://operas-project.