dataesr/scientific-paragraphs-categorization huggingface dataset:
Producer The producer of the two databases are the two linked projects: -Odyssea, Observatory the Dynamics of Interactions between Societies and their Environments in the Amazon, which received funding from the European Union's Horizon 2020 Research and Innovation program under the Marie Skłodowska-Curie grant agreement no. 691053: https://www. odyssea-amazonia. org; -INCT Odisseia, Instituto Nacional de Ciência e Tecnologia/Observatório das Dinâmicas Socioambientais, no. 16-2014, with funding from CNPq/Capes/FAP-DF: http://inct-odisseia. i3gs. org/.
Community database The first unit of analysis is the community, defined as a common place of living. We did not discuss the physical, social, or institutional boundaries of what "makes a community"; we instead used the local representatives' own delimitations and denominations. The questionnaire applied with the community representatives covered the following topics: historical background (creation, main dates); demographics (past and current population); collective action and local institutions; public infrastructure (access to water, energy, mobile phone signal, garbage disposal), access to schools, access to health services; local problems; land situation; agricultural activities (past and current practices); agroecology initiatives; perception of change (climatic change, social change); perception of agribusiness; difficulties and future perspectives.
Household database The second unit of observation is the family. As for the community level, we let the respondents define the boundaries of this unit. The questionnaire covered the following structuring themes: interviewees' profile (education, migration history, professional activity); household composition and professional activities; participation in social organizations and events; income composition and productive activities; agricultural practices (including use of pesticides); commercialization of production; technical assistance and production strategies; land dynamics of the property; perception of changes and perspectives for the future. In all, there are 786 variables in 9 categories.
Temporal coverage Data was collected between April and July 2019. The community meetings, during which the community questionnaire was applied, took place between April and June 2019, at the rate of approximately one meeting per week. The household questionnaire was applied by the community researchers between April 3, 2019 and July 3, 2019. The data collected refers to the situation of the household and agricultural practices at the time of the interview, with the production being estimated for the past year.
Data processing After applying the questionnaires, two databases were generated by KoboToolbox in Excel with the information gathered from households and community representatives. The databases were migrated to the statistical software SPSS to make it easier to construct indicators and derive analytical results for the study, and to standardize variables to allow sharing of data with external audiences. Data consistency was maintained during three phases of standardization in order to adjust and ensure updates, following the need for concordance and identification in the database: deviation of absolute or relative numerical values, with adjustments of the numerical value or conversion of measurement units. deviation of age or date, period or historical series, such as time period since a given date in years or months, based on the period of the data survey, in pursuit of a finer level of granularity.
deviation regarding the writing and language, corrections of grammatical errors and standardization of answers or grouping of keywords to ensure coherence between different answers of a same variable. The conversion of the units of surface area, mass and volume required a thorough process. In Brazilian rural areas, farmers use a wide diversity of units at the same time: they vary according not only to the interviewee's place of origin, but also to the type of area they describe or the product they refer to. During the interview, in order to facilitate the collection of information and avoid conversion errors, we let the respondents express themselves using the units of their preference. In total, 3 different units of surface, 2 units of mass, and 3 units of volume were collected, which were all converted to the metric system (hectares, kilos and liters) during database standardization.
Pricing of the produce was also challenging. It was undertaken based on complementary research and surveys of commodity prices at fairs and in food shops. Finally, we anonymized the data, deleting the name and georeferenced localization of each respondent. To compensate for the loss of the localization information, we added a calculated variable named "proximity to soy plantation". 6 Description of the database and other files, localization of metadata and conditions of access to data Both databases are available as dataverses (Tab. 4). Along with the databases (. csv format), we make available the questionnaires used and the dictionaries of the variables (. pdf).
Potential use of this co-produced data These databases are licensed under the terms of the Creative Commons Attribution-NonCommercial 4. 0 International License. Use of data for publication is under embargo until July 2022 to enable prior publication by the co-producers of the data. As we have already emphasized, we expect this data to primarily help the local actors in fostering local dynamics for the construction of strategic actions, decision-making, advocacy, or simply for updating their knowledge of local reality. In order to generate familiarity with the data, as also to impart a sense of ownership, we sought to involve the leaders of the farmer unions and the community researchers at all stages of data preparation, collection, and interpretation. We also put emphasis on sharing this knowledge through dissemination material.
While this approach proved to be relevant for descriptive statistics, it limits the ability to make correlations. However, since our objective was to empower the farmers to make use of the information, and not to conduct sophisticated analyses, this has not been a significant limitation for us, but might be so for other users of the database. Second, regarding the use that can be made of this data by the farmers, we realized that even with important translation efforts, the comprehension of the information is not straightforward. The demand from the farmer unions to produce quantitative information determined the format of the results, which were presented as tables, graphics, and maps. We were aware that the farmers might find these formats difficult to read and so we organized several workshops to explain how to read graphs, provide an introduction to basic statistics (what is a mean value, what is a percentage, etc.
involving researchers September 2018 First planning of the data collection campaign and the main topics to be addressed December 2018 First list of the general questions the farmers wanted addressed January 2019 Participatory territorial zoning to define the zones to focus on Validation in a plenary session of the main categories to be investigated February 2019 Elaboration of the detailed elements to be taken into account in the community and household questionnaires March 2019 Formulation of the questions and digitization in KoboToolbox Early April 2019 Discussion of the two surveys during the training session of the community researchers, to rephrase the questions one by one so they would correspond to the farmers' vocabulary and representations Mid-April 2019 Further adaptation of the questions after the first application of the surveys April-July 2019 Application of the surveys by the community researchers Regular follow-up and debriefing regarding problems arising in the field First week of July Researchers processed all the results from the survey to calculate mean values July 2019 First reactions regarding the field experience and the results of the survey: What was learned? What was surprising? What were the difficulties? September-October 2019 Researchers deepen the analyses of the results, and cross variables with the support of a statistician October 2019 Debate on detailed results: what results are the most important and deserve to be presented? To whom should they be presented? How to present them? What narratives? December 2019 Validation by the research team of the final version of the database and of the dictionary of variables systematized by the statistician open to social sciences was recruited to offer further support all through the data collection and analysis process.
Acknowledgements. We are so grateful to the farmer leaders for their trust and partnership, to the young community researchers for their enthusiasm and to all the farmer families for their warm welcoming. Many academic researchers contributed in this project, the authors are those who coordinated the process and formalized the databases. We thank all our colleagues for the rich debates with them, with special credit to  Vincent Bonnal  for the map elaboration. Finally, we thank the two anonymous reviewers for their valuable contributions. The project leading to this article has received funding from the  European Union 's  Horizon 2020 Research and Innovation programme ODYSSEA  under the  Marie Skłodowska-Curie grant  agreement  N°691053  and the project INCT n°16-2014 ODISSEIA, with funding from  CNPq/Capes/FAPDF.
Experimental Design and Parameters Tuning The MILP developed for the multi-supplier, multi-customer, multi-RTI closed-loop supply chain was first solved to optimality for small-and-medium-size instances using the Branch-and-Cut solver of CPLEX 12. 9 (academic version). The objective was to check the model's validity, representativeness, and exact solving approach limitations. To implement the matheuristic, we used Python 3. 7 and Pytorch interfaced with CPLEX. The approach was first tested on the same instances optimally solved using CPLEX. The objective was to assess its performance. We ran the AIS algorithm without the learning process (AIS) and compared the improvement provided by the deep Q-learning when coupled with the AIS algorithm (AIS-DQL). We also compared the algorithm's performance with a pure genetic algorithm (GA) and its improved learning version (GA-DQL).
Conclusions and Perspectives This paper considered a deterministic, multi-supplier, multi-customer and multi-RTI inventory routing problem with delivery and pickup in a collaborative supply chain in which empty RTIs inventories are virtually pooled among suppliers. We developed an MILP and solved it using CPLEX. Experiments showed that the virtual pooling of RTIs significantly reduces new RTI procurement costs as well as inventory and transportation costs compared to dedicated and shared modes. Moreover, to handle the combinatorial complexity of the problem, we developed an artificial-immune-system-based algorithm coupled with deep reinforcement learning tailored to the mathematical program. We implemented our resolution approach using Python and Pytorch and compared it to the CPLEX solver and three metaheuristics, namely, AIS without deep learning and GA with and without deep learning.
With the proliferation of data sharing agreements and widespread adoption and diffusion of standardized interfaces for data exchange, so-called Application Programming Interfaces (APIs), firms are increasingly able to leverage the portability of data to access and create value with data by training unique machine learning models, without necessarily owning and controlling the training data. Once a machine learning model has been trained, it is able to function independently from the training data with which it was fed and developed. Growing availability of open datasets and emerging markets for data with built-in appropriation regimes and guardrails for quality control and data provenance also contribute to the shift away from a focus on exclusive ownership of data which creates significant challenges for privacy protection  (Thomas, Leiponen, & Koutroumpis, 2020).
We present in this paper the Model Based System Synthesis (MBSS) approach for the design of complex systems that are correct by construction. Where the usual Model Based System Engineering (MBSE) approach offers formalisms and tools to represent a candidate system, to analyze it, to simulate it and even to optimize it, MBSS proposes to represent the global design problem using a problem representation language and then to solve it by using adapted synthesis tools producing one or several solutions necessarily satisfying the expressed requirements. The two approaches are therefore complementary; the MBSS being more adapted to the preliminary design and system integration stages. After presenting the different categories of problems encountered in system design (sizing, configuration, allocation, architecture generation), MBSS and MBSE will be positioned in relation to each other.
The main concepts of MBSS will be detailed in order to understand the specific representation needs of the approach. The structural and behavioral notions related to the sub-definite systems will be explained as well as the links to be established with the functional and non-functional requirements. The approach is illustrated using the DEPS design problem specification language and the DEPS Studio modeling and solving tool on a system design case study. The DEPS language combines structural modeling features specific to object-oriented principles and ontology definition capabilities for engineers with problem specification features from constraint programming. DEPS Studio is an integrated modeling and solving environment designed to model and resolve system synthesis problems. It allows the engineer to edit, compile, debug and solve problems expressed in DEPS.
This point of view is equipped with a formal problem modeling language called DEPS and with an integrated modeling and solving environment called DEPS Studio. The observation made by several authors is that the current MBSE tools do not allow a correct by construction approach and we detail in this paper our proposal to lift this lock. This paper is organized as follows: After presenting the typologies of system design problems and their characteristics, we present the case study that will be used to illustrate our point of view. Then the MBSS approach is detailed from the point of view of the methodology, the modeling and the tool-based problem solving. A comparison with the MBSE will also be proposed before a conclusion.
The sub-definite architecture can be decomposed in a tree-like manner down to the sub-definite components. The same reasoning applies to each sub-definite behavior. The system, behaviors and sub-definite architecture are composed of a set of unknown values inside domains and a set of properties to be satisfied. The problem modeling aspects of the MBSS approach are available by using the DEPS language. DEPS (Design Problem Specification) is a declarative problem modeling language for system design problems  [12]. The distribution and evolution of the language is now supported by the DEPS Link non-profit organization  [13]. Its grammar is defined with a "context free" BNF (Backus-Naur-Form) notation. DEPS combines some structural modeling features of objectoriented and ontology description capabilities for engineers with problem specification features from constraint programming.
From the former, it has borrowed the characteristics of structuring and abstraction that allow us to represent the components and the (possibly partial) architecture of the system under study. From the latter, the possibility to describe ordinal and cardinal quantities used by the engineer (current, voltage, length, angle. ) were borrowed. From the third, the logical-mathematical concepts necessary to solve the engineer's problems were borrowed. This combination of declarative paradigms leads to a declarative language that allows the designer to model the organization of the studied systems and the properties that govern them. DEPS addresses problems of sizing, configuration, allocation and more generally of architecture generation or synthesis encountered in system design. The systems can be physical systems, software intensive or mixed systems (embedded or cyber-physical).
The main feature of the language is the Model. A Model encapsulates constants, variables, instances of other Models called Elements and properties. Models have a signature and can be overloaded, extended, shared (aggregation) and composed (composition) In the case of the problem of configuration and positioning of the camera, we describe in DEPS the architecture model of the camera (cf Fig. 6 ) consisting of one or two channel, a channel being composed of a sensor, a CPU and a transceiver. The latter are models derived from an abstract component model (cf Fig. 7 ). The behavioral models of the sub-definite camera architecture will then be realized (cf Fig. 8 ). The architectural requirements will be set in terms of properties on the camera, while the behavioral requirements will be expressed in behavioral models.
If the solver finds one or more solutions, it guarantees that they all satisfy the set of constraints expressed. It is also possible to minimize or maximize variable values. DEPS Studio  [12] , the environment associated with the DEPS language includes a model editor, a project manager, a compiler and a solver. Experience shows that the first specification of a system design problem is never the right one and that many modeling errors are only detectable by calculation. DEPS Studio integrates a dedicated constraint programming solver on mixed domains so that the solution finding contributes effectively to the adjustment of the problem modeling process. This is a rapid model development approach (analogous to a RAD approach) which, contrary to a model transformation approach, reduces the execution time of the model development loop.
Related work From the point of view of the MBSS approach as we have explained and formalized previously (cf fig 5), we have not found any equivalent work. Concerning the modeling formalisms, the authors of  [21]  underline the difficulties of using formalisms such as SysML (System Modeling Language), initially built for representing fully defined systems to model problems. They propose in their work to use as us a dedicated formalism called the Clafer associated with the Choco constraint programming library to model and solve a problem of allocating computers to embedded tasks. Nevertheless, Clafer remains a language dedicated to the configuration of software product lines with discrete variables and constraints. On the other hand,  [11]  proposes to add a first level of variability to SysML.
The approach is coupled with the Choco constraint programming library to solve simple configuration problems. However, since these first research works, this initiative has not progressed significantly because it remains difficult to introduce more structural variability in a system definition language. The current limitations of the approach are the low level of variability that can be taken into account, the use of a solver dealing mainly with discrete constraints and a weak coupling between the formalism and the solver. This last point means that the debugging of the models is done, in case of bug or modeling problem, in the host language of the solver, in this case Java and not in the SysML language. In this paper we have proposed a possible evolution of MBSE summarized in figure  10. The MBSS approach supports the idea of modeling the problem rather than the solution candidate and then solving the problem instead of evaluating the performance of a solution candidate.
The 10 pillars of acceptability Sustainability and therefore the acceptability of management decisions are umbrella concepts and can be assessed through the combination of multiple criteria  (van der Voet et al. , 2014). Based on a systematic review of the literature published during the past 30 years, this section presents a comprehensive list of criteria that may be used to understand the evolution of public attitudes toward aquaculture. Literature searches were conducted on such databases as Google Scholar and Scopus, using keywords such as social acceptability, social license, aquaculture, social perception, attitudes, and synonyms. Once an exhaustive list of criteria has been identified, another search was made by each criterion, for example, aquaculture and impact on landscape, aquaculture and competition for space, and so forth.
Second, platforms often capture rich information about users (on both sides of the platform) that is itself valuable beyond the immediate transaction. More information creates more intelligence (ie, matches that become smarter with each learning cycle). Well-known examples of such positive feedback loops include Google's increasing dominance in search because of its many learning cycles and accumulating data. Yet, less well-known examples abound as well. The Chinese software company Qihoo gave away its anti-virus software for free, triggering the generation of more learning cycles from more users and ultimately creating dominance over traditional providers  (Zhu & Furr, 2016). More information about users can also provide valuable insights into serving customers in novel ways or with new products.
The latter means coordinating with a platform partner such as by revealing internal data and considering coopetitive dynamics  (Brunswicker, Almirall, & Majchrzak, 2019). For example, established firms (eg, banks struggling to leverage their treasure trove of data due to legacy IT infrastructure or cultural misfit) may find that partnering with a platform to jumpstart their digital journey is an effective strategy. As Ozcan and Zachariadis (2021) find, working with BigTech platforms can be a "win-win" strategy for traditional banks that would otherwise face the daunting task of overhauling their IT infrastructure to create a platform. Creating vs joining a platform also requires attention to short-vs long-run tradeoffs. For small and medium enterprises, partnering with platforms like the Mastercard Farmers Network in Africa or Pinduoduo in China, often leads to benefits over both the short and long run due to access to previously unreachable tools or markets.
For example, digital interfaces can be embedded in physical devices like French firm Babolat's connected tennis racket and BMW's connected car platform. Blockchain can connect previously unconnected industries, such as in Maersk's global shipping platform  (Furr & Shipilov, 2018). Application programming interfaces (APIs) can securely connect databases and functions, for example, allowing fintech firms to connect to larger financial institutions to share data and innovate together  (Zachariadis & Ozcan, 2017; Ozcan & Zachariadis, 2021). The tension between firms and ecosystems largely arises from differences in their underlying economics. Firms create and capture value by relying on a value chain that they mostly control. They may work with suppliers, but not in complicated constellations of firms.
qualtrics. com) through third party. All the contacted persons responded to the questionnaire. All data were saved anonymously on the Qualtrics platform database. Certain quotas included in the design of the survey have ensured that the sample collected is representative of SMEs in each of the four countries, including the wide geographical distribution of companies in each country. The questionnaire included questions on both the assessment of sustainability performance (i. e. , economic, environmental and social), as well as items associated with the circular economy fields of action implemented in these companies using the functions of closed loop supply chain (see Appendix B). The conceptual model for CE and sustainability performance proposed in this paper is tested through the SEM approach  (Bollen, 1989).