dataesr/scientific-paragraphs-categorization huggingface dataset:
In some cases, the values achieved by TIM are comparable but these are instances where all algorithms perform very close to each other and the minimum community probabilities are rather high anyways. Furthermore, on several instances, e. g. , in the case of 10 BFS Fairness vs. E ciency on Email-Networks. We proceed by focusing on the email networks Arenas and email-Eu-core and comparing the fairness achieved by the di erent algorithms with the e ciency, i. e. , expected spread. We again use the BFS community structures with di erent community sizes. In the case of the email-Eu-core dataset, we evaluate the di erent algorithms on the community structure induced by the departments as well. We evaluate the algorithms for increasing values of  = 5, 10, 20, 50, 100. We observe that set_based performs best in terms of fairness among all ex-ante as well as ex-post values both on the Arenas dataset as well as on the email-Eu-core dataset.
In terms of e ciency, we observe that on the Arenas dataset, tim and set_based perform similarly good and much better than the other competitors. For the email-Eu-core dataset, we see that in terms of e ciency all algorithms (even uf_node_based for small values of ) perform almost identical. Fairness vs. E ciency on Co-Authorship Networks. We turn to the two co-authorship datasets ca-GrQc and ca-HepTh and evaluate the fairness and e ciency achieved by the di erent algorithms. Focusing on the fairness values rst, we observe that in a setting with /10 communities, no algorithm achieves a positive ex-post value. Instead the two randomized algorithms set_based and uf_node_based do achieve a signi cantly non-zero ex-ante value, the results of set_based being more than twice as high compared to the values of uf_node_based.
Data collection. Applying the common enquiry framework developed in the initial stage of the research, we collected the following qualitative data for each case: (1) how co-production and sustainability challenges are framed, (2) how co-production is designed and implemented in practice, (3) the rationales underpinning decisions on how to co-produce, (4) capacities which support or hinder co-production and (5) outcomes of co-production. The lead author completed a qualitative spreadsheet in partnership with a key representative of each case. For 29 cases, this was a senior leader in the co-production process, while for 3 cases, this was someone who had extensively researched the case. The lead author interviewed most case representatives for approximately an hour to gather further qualitative information related to written responses in the spreadsheet.
Preliminary analysis and testing of dimensions of difference. The lead author conducted a preliminary analysis by qualitatively coding each of the five aforementioned categories of data using NVivo Software, grounded in the data from and understanding about each case from the case contributors  69. Two rounds of coding of the common enquiry spreadsheets (completed for each case) and additional case documents and interviews led to the identification of numerous qualitatively described dimensions of difference across all cases. These dimensions captured how the sample of co-production cases varied in how they were framed, designed, implemented, supported by certain capacities, and pursued and produced particular outcomes. These findings were then presented in a 13,000-word report and interrogated in an interactive two-day workshop in Colorado, United States (16-17 July 2018) with 14 case contributors and experts in co-production.
The dimensions used in this analysis include different ways that co-production cases were framed (8), designed (16) and implemented (16), capacities that shaped how co-production cases functioned (4), and distinct outcomes that were intended (14)  and produced (14). Each of the 14 outcomes were additionally scored on the extent to which they were assessed, if assessment had occurred. If the assessment method was evaluated by the lead author (in consultation with the case contributor) as being too poor of quality to assess particular outcomes, the achievement score was recorded as missing in the dataset. Supplementary Table  4  presents the typical methods that were used to assess outcomes across the cases, including some of the most innovative approaches. We employed fuzzy-set social science methods  70  to devise a scoring process to indicate the extent that cases exhibited particular dimensions.
To identify distinct modes of co-production, we conducted hierarchical cluster analysis using the eight approaches to co-production outlined above (and in Fig. 2 ). The NbClust 72 package in R identified six clusters as the optimal number, based on the peak of the Dindex second differences plot. The R packages tidyverse  73  , cluster  74  , factoextra  75  and dendextend  76  were used to conduct the hierarchical cluster analysis, using Euclidean distance as the dissimilarity matrix coefficient and Ward's method of hierarchical clustering to minimize the error in sum of squares  58. The R package fmsb  77  was used to generate the radar charts displayed in Fig. 3. The R stats package  71  heatmap function was used to generate visual diagrams to show how case clusters differ (Fig. 4 ). The qualitative descriptions associated with the scores for each case were also reviewed to help describe each cluster (that is, mode).
The qualitative analysis confirmed that all modes were well defined by important differences, and that even though one mode was especially small (mode 3), its highly unique nature merited its separation from other modes. Unique features of each mode were examined by testing for significant differences between mode means for each descriptive dimension, using the stats package  71  to conduct Kruskal-Wallis tests. Pairwise Wilcoxon signed-rank tests revealed which pairs of modes were significantly different for each dimension. Relatively few significant differences emerged between mode 3 and the other modes due to there being only two cases in that mode. To reduce the large number of descriptive dimensions, principal component analyses were conducted on three different sets of highly intercorrelated dimensions using R packages stats  71  and ggbiplot  78.
Study description The data are mixed method, and include qualitative descriptions of how the 32 initiatives using collaborative approaches for sustainability differ for 72 dimensions. These 72 dimensions were defined based on in-depth qualitative analysis. Each case was then quantitatively judged on a 7-point likert scale to indicate the extent they express each of the dimensions, based on fuzzy-set social science methods and providing qualitative justifications that were then validated by each case contributor. Quantitative data were then used to conduct statistical analyses, and the patterns were interpreted through additional qualitative analyses. Research sample 32 collaborative initiatives were selected for this study, with the aim to maximize diversity in both approach and geographical and scalar context.
Data collection Data was collected by collaborating with each case contributor. Each contributor sent relevant materials to learn about their cases and the lead author interviewed each contributor twice over skype. For all interviews, the conversation was recorded and transcribed (with the permission of the interviewee). The interviews and materials were then used to fill quantitative and qualitative data into an excel spreadsheet template with 72 dimensions that were identified as being interesting from the participatory workshops and literature review. Each case contributor reviewed the spreadsheet pertaining to their case to validate the data, and small revisions were made, as necessary. The data from participatory workshops was recorded by the participants themselves as they wrote their ideas onto post-it notes and large pieces of papers, and then image files of these materials were stored.
Timing The initial scoping workshops took place in May and November of 2017. All data was collected on the 32 cases during February-July 2018, followed by two additional participatory workshops in July and August, 2018. Further data was collected about each case during August 2018 -October 2019 to complete the secondary analysis, as it took at least a week to collect and analyze each case in depth in relation to the 72 dimensions.
Acknowledgements This project and paper were supported by the  Luc Hoffmann Institute  and  MAVA Foundation. We acknowledge the  Center for Collaborative Conservation, PECS , the  Cambridge Conservation Initiative  and  The Pew Charitable Trusts  for hosting our workshops. We thank  J. Lokrantz at Azote  for improving the graphics design. J. M. C. received support from the  Economic and Social Research Council  (grant  RG97777 ). J. J. C. was funded by a  Rhodes University   Postdoctoral Fellowship  and acknowledges the contributions of  P. Singh  and  S. Behngu  to analysing the Durban Research Action Partnership case. H. Ö. was funded by the  Walton Family Foundation  (grant  2018-1371 ), the  David and Lucile Packard Foundation  (grant  2019-68336 ) and the  Gordon and Betty Correspondence  and requests for materials should be addressed to  J.
A. -I. H. -M also acknowledges support from the  Leverage Points  project practice partners and all project team members. J. M. was supported by the  Leverhulme Trust. J. G. Z. was funded by the r4d programme of the  Swiss Programme for Research on Global Issues for Development (grant   400440 152167 ). Elements of this work were undertaken while J. G. Z. was a visiting scholar at the  Department of Geography, University of Cambridge  (May 2018-April 2019), supported through Scientific Exchange funding from the  Swiss National Science Foundation  (grant  IZSEZ0_180391 ).
Research area & data collection The Alps host more than one-third of the world's total and 80% of the world's major 1  ski resorts, with the Alpine countries France, Austria, Switzerland and Italy leading the European ranking in terms of skier visits (cf. Vanat, 2020). The empirical data presented in this paper result from two different surveys conducted among Alpine ski resorts in the course of the PROSNOW project: Livigno (IT). One of the main aims was to gain insights into the pilot ski resorts' WTP for a service like PROSNOW®, using some kind of choice experiment that is able to provide meaningful estimates even in the case of small sample sizes (see section 3. 2. 2). The questionnaire was sent to the contact persons at the pilot ski resorts as a completable PDF form, with project members offering telephone support in case of any ambiguities or questions.
Acknowledgements The authors would like to thank the PROSNOW pilot ski resorts and the broader group of Alpine ski resorts responding to survey A, the  PROSNOW User Advisory Board  for spreading the survey link, all ski tourism organisations who made this research possible and the whole  PROSNOW  project consortium, with special thanks to those partners who provided feedback to the surveys and were involved in spreading them (particularly  Franziska Koch ,  Jens Schärer ,  Michael Rothleitner ,  Fabiano Monti , and  Sébastien Bruyère ).
The continuous accumulation of multi-dimensional data and the development of Semantic Web and Linked Data published in the Resource Description Framework (RDF) bring new requirements for data analytics tools. Such tools should take into account the special features of RDF graphs, exploit the semantics of RDF and support flexible aggregate queries. In this paper, we present an approach for applying analytics to RDF data based on a high-level functional query language, called HIFUN. According to that language, each analytical query is considered to be a well-formed expression of a functional algebra and its definition is independent of the nature and structure of the data. In this paper, we investigate how HIFUN can be used for easing the formulation of analytic queries over RDF data. We detail the applicability of HIFUN over RDF, as well as the transformations of data that may be required, we introduce the translation rules of HIFUN queries to SPARQL and we describe a first implementation of the proposed model.
Introduction The amount of data available on the Web today is increasing rapidly due to successful initiatives, such as the Linked Open Data movement (http://lod-cloud. net/). More and more data sources are being exported or produced using the Resource Description Framework (https://www. w3. org/RDF/) (or RDF, for short) standardized by the W3C. There are thousands of published RDF datasets (see  [1]  for a recent survey), including cross-domain knowledge bases (KBs) (e. g. , DBpedia  [2]  and Wikidata  [3] ), domain specific repositories (e. g. , DrugBank  [4] , GRSF  [5] , ORKG  [6] , WarSampo  [7] , and recently COVID-19 related datasets  [8] [9] [10]  as well as Markup data through schema. org. Figure  1  shows the general picture of access services over RDF. Apart from Structured Query Languages, we have Keyword Search systems over RDF (like  [11] ) that allow users to search for information using the familiar method they use for Web searching.
We can also identify the category Interactive Information Access that refers to access methods that are beyond the simple "query-and-response" interaction, i. e. , methods that offer more interaction options to the user and also exploit the interaction session. In this category, there are methods for RDF Browsing, methods for Faceted Search over RDF  [12] , as well as methods for Assistive (SPARQL) Query Building (e. g. ,  [13] ). Our work falls in this category, specifically we aim at providing an interactive method for analytics over RDF. Finally, in the category natural language interfaces there are methods for question answering, dialogue systems, and conversational interfaces. As regards structured query languages, RDF data are mainly queried through structured query languages, i. e. , SPARQL (https://www.
In view of the above challenges, there is a need for a simple conceptual model able to guide data analysis over one or more linked data sets that demands no programming skill. Motivated from this need, we are investigating an approach based on a high-level query language, called HIFUN  [14] , for applying analytics to RDF graphs. We study how that language can be applied to RDF data by clarifying how the concept of analysis context can be defined, what kind of transformations are required and how HIFUN queries are translated to SPARQL. Please note that with the translation approach that we focus on, we can apply analytics to RDF sources, without having to transform the RDF data to relational ones, nor to copy them. The idea was first introduced in  [15]. The current paper is an extended and enriched version of that work presenting (a) a more complete related work, (b) a detailed analysis of the applicability of HIFUN over RDF data, (c) the detailed algorithm for translating HIFUN queries over RDF data, (d) the first implementation of an algorithm that makes that translation.
In view of this limitation, several approaches were proposed. These approaches can be divided into two major groups: (i) those assuming that the multidimensional data (MD) i. e. , data related to more than two dimensions, has already been represented in the RDF format and (ii) those that do not. Our approach, as well as the works in  [22] [23] [24]  are related to the first group. On the other hand, the work in  [25]  considers that the multidimensional data has been stored as non-RDF data sets. In particular, it declares that the data cubes are retrieved from a relational database with SQL queries and then get triplicated. The representation of MD data in RDF can further be organized in two categories: (i) those that are based on specialized RDF vocabularies  [23, 26]  and (ii) those that implicitly define a data cube over existing RDF graphs (https://team.
The work in  [30]  presents a method for applying statistical calculations on numerical linked data. It stores the data in arrays and performs the calculations on the arrays' values. Nevertheless, contrary to our work, it requires deep knowledge of SPARQL for formulating the queries. To overcome one's difficulty in background programming knowledge, high-level languages have been developed for data analysis, too. However, there has not been much activity in introducing high-level languages suitable for analytics on RDF data. While general-purpose languages, such as PIG Latin  [31]  and HiveQL  [32]  can be used, they are not tailored to address the peculiarities of the RDF data model. Even though,  [33, 34]  present high-level query languages enabling OLAP querying of an extended format of data cubes  [23] , they are only applicable to data already represented and published using a corresponding vocabulary.
As a consequence, they fall short in addressing a wide variety of analytical possibilities in non-statistical RDF data sources. In addition,  [31]  proposes a highlevel language that supports semantics. However, it is targeted at processing structured relational data, limiting its use for semi-structured data such as RDF. Furthermore, it provides only a finite set of primitives that is inadequate for the efficient expression of complex analytical queries. A survey that is worth mentioning is  [35] , which introduces warehouse-style RDF analytics. There are similarities with our approach, since each analytical schema node corresponds to an RDF class, while each edge corresponds to an RDF property. Nonetheless, since the facts are encoded as unary patterns, they are limited to vertices instead of arbitrary sub-graphs (e.
g. , paths). Other related work includes  [24]  that focuses on how to reuse the materialized result of a given RDF analytical query (cube) in order to compute the answer to another cube, as well as recent systems for analytics over RDF such as Spade  [36]  that suggests to users aggregates that are visually interesting. In brief, in contrast to the aforementioned works, in this paper, we focus on developing a user-friendly interface, where the user will be able to apply analytics to RDF data without dealing with lower-level technicalities of SPARQL. Indeed HIFUN is more simple for formulating analytic queries. We focus on the support of analytics over any RDF Data (not only over data expressed according to RDF Data Cube), and we focus on a query translation approach, i. e. , an approach that does not require transforming or transferring the existing data; instead it can be directly applied over a SPARQL endpoint.
Any finite subset of T constitute an RDF graph (or RDF data set). RDF Schema. RDF Schema (https://en. wikipedia. org/wiki/RDF_Schema) (RDFS) is a special vocabulary which comprises a set of classes with certain properties using the RDF extensible knowledge representation data model. Its intention is to structure RDF resources, since even though RDF uses URIs to uniquely identify resources, it lacks semantic expressiveness. It uses classes to indicate where a resource belongs, as well as properties to build relationships between the entities of a class and to model constraints. A class C is defined by a triple of the form ăC rdf:type rdfs:Classą using the predefined class "rdfs:Class" and the predefined property "rdf:type". For example, the triple ăex:Product rdf:type rdfs:Classą indicates that "Product" is a class, while the triple ăex:product1 rdf:type ex:Productą that individual "product1" is an instance of class Product.
Using HIFUN as an Interface to RDF Dataset There are several ways in which HIFUN can be used, such as for studying rewriting of analytic queries in the abstract  [14]  or for defining an approach to data exploration  [39]. In this paper, we use HIFUN as a user-friendly interface for defining analytic queries over RDF data sets. To understand the proposed approach, consider a data source S with query language L (e. g. , S could be a relational data set and L the SQL language). In order to use HIFUN as a user interface for S, we need to (a) define an analysis context, that is a subset D of S to be analyzed, and some attributes of D that are relevant for the analysis and (b) define a mapping of HIFUN queries to queries in L. Defining a subset D of S can be done using a query of L and defining D to be its answer (i.
e. , D is defined as a view of S); and similarly, the attributes that are relevant to the analysis can be defined based on attributes of D already present in S. However, defining a mapping of HIFUN queries to queries in L might be a tedious task. In  [39]  such mappings have been defined from HIFUN queries to SQL queries and from HIFUN queries to MapReduce jobs. The main objective of this paper is to define a user-friendly interface allowing users to perform analysis of RDF data sets. To this end, we use the HIFUN language as the interface. In other words, we consider the case where the data set S mentioned above is a set of RDF triples and its language L is the SPARQL language. Our main contributions are: (a) the proposal of tools for defining a HIFUN context from the RDF data set S and (b) defining a mapping from HIFUN queries to SPARQL queries.
It can be used for specifying a path (i. e. , a sequence of properties p 1 , p 2 ,. , p n etc. ) and treat it as an individual property p. These features can be used for deriving a new RDF dataset that will be analyzed with HIFUN. This transformation can be done by using SPARQL CONSTRUCT queries: Suppose that the pair pR, Fq expresses a context, where R is a set of resources and F the set of the features, the objects in R have. Then, the resources R, as well as the features F, can be defined by the triple patterns i. e. , "?s ?p ?o" in the CONSTRUCT clause of a SPARQL query, i. e. , the bindings of "s" (or "o") can correspond to the resources, whereas the bindings of "p" to the set of features. Alternatively, these features can be defined by queries, but instead of constructing the triples, the definition of the features can be included in the analytic queries in the form of nested queries (subqueries); in general any query translation method for virtual integration  [41]  can be used.
5. Optionally, if any restrictions re are applied to the final answers Q ans , then we create the string format of the condition expressing these constraints, restr(Q ans ) = right(m i ) re as described in Section 6. 3. To give an example suppose that we would like to find he total quantities by branch and brand only for the month of January, by considering only (a) the invoices with quantity greater than or equal to 2, and (b) the branches with total quantity greater than 1000. This query would be expressed in HIFUN as ptakesPlaceAt b pbrand ˝deliversqq{ month"01 , inQuantity ą"2 , SU M{ ą1000 q and (by following the above translation process) in SPARQL as: The pseudocode of the algorithm that defines the variables retVarspgEq, opEpmEq, triplePatternspgEq, triplePatternspmEq, retVarspgEq, and restrpQ ans q, for the simple case, where the HIFUN query does not contain compositions and pairings, is given in Algorithm 1.
Cases Where the Prerequisites of HIFUN Are Not Satisfied Let us now discuss the problem of translation for the case where the requirements for applying HIFUN (as described in Section 5. 1) do not hold, as well as when features (as described in Section 5. 4) are required. Consider that the running example of Figure  2  contained a property "ex:birthYear" with domain the class "ex:Person" and range an integer-typed literal. Now suppose that we would like to compute a single number being the average birth year of the founders of the products that were sold. Here the problem of incomplete information arises, i. e. , the dataset may not contain information about the founders of all products, let alone their birth year, therefore the path delivers. brand. f ounder. birthYear will not be "applicable" to several invoices.