dataesr/scientific-paragraphs-categorization huggingface dataset:
Sharing and publishing Galaxy datasets, histories, workflows and visualizations can all be shared with individuals, or they can be shared via the Web using links such as https://usegalaxy. org/u/jeremy/h/ hpac-exome-analysis Galaxy objects can also be published; published items are listed together and available to all users. Galaxy Pages are interactive web documents that include embedded and interactive Galaxy objects. Pages are often used for interactive methods supplements to papers and tutorials. Pages can be shared and published just like all other Galaxy objects.
Advances in analysis tools Updated and large tool suite. Over the last year, we have increased the breadth and quality of tools available on the Public Galaxy server by adding 400 new tools to the server. More than 650 tools now enable users to analyze a wide variety of different genomic data. Very simple tools perform text manipulation and statistical operations, but the majority of tools on the server are for analysis of high-throughput genomic datasets from current DNA sequencers. Highthroughput sequence analyses that can be run using Galaxy include quality control, genotyping and variant identification, motif finding, RNA-seq, ChIP-seq, and metagenomics. In addition to newly available tools, Galaxy recognizes and can process 300 well defined data formats (e. g. bam, bcf, bed, bedgraph) and offers visualization options and convenient conversion between different formats, as well as performance features like splitting inputs into small chunks and distributing them more efficiently over the computer cluster; we have also added EDAM (11) ontology identifiers.
We now use the Tool Shed  (12)  as the source for all new tools and version updates; the Tool Shed greatly simplifies tool deployment and ensures versioning and reproducibility. The number of Galaxy tools contributed by the community has greatly risen as a result of the Tool Shed. The Intergalactic Utilities Commission (IUC; https://wiki. galaxyproject. org/IUC), a volunteer community group, has emerged as the workhorse behind current tool development. Tool authors, including core Galaxy developers, can and often do submit tools to the IUC for review and approval via GitHub (https://github. com/galaxyproject/tools-iuc). Tool user interface. All Galaxy tools use a single interface to simplify tool integration and maximize usability. In the past year, we have rewritten the tool interface to be significantly more responsive and dynamic, and we believe that these changes have substantially increased the interface's usability.
Working with analysis histories Multi-history viewer. A new history management interface provides an intuitive and scalable way to manage histories and datasets (Figure  3 ). This interface enables all histories to be viewed together, allowing users to see the entirety of their work. Histories are shown with their complete list of datasets using the same interface as the main analysis page. All normal history and dataset operations can be used in this interface, such as annotating, tagging or deleting a his- tory and searching or previewing datasets. Datasets can also be copied between histories by dragging and dropping. History search and current state. Users can now search through their entire catalog of histories and datasets for partial text, data formats, references used, notes and tags (Figure  3 ).
This data manager turns our public server into a large reference data repository that can be used to populate reference data in local Galaxy servers. Data libraries. Galaxy data libraries provide a convenient method to share data with collaborators or the general public. We have recently developed a dynamic client-side user interface for working with data libraries that is, on average, five times faster than the previous server-side interface. While the previous interface would not work for libraries with tens of thousands of items, this interface can be used for browsing these very large libraries. We also continue to expand the datasets in our libraries on the main Galaxy server: there are now more than 2000 datasets publicly available.
Scaling to large analyses Dataset collections. Today it is common to analyze multiple datasets derived from different individuals, conditions, treatments, environmental sites or tissues types. To enable analysis of many datasets simultaneously, we have developed Galaxy dataset collections (Figure  4 ). A collection contains related datasets and defines their relationships. A Galaxy history with a paired-end DNA re-sequencing dataset from 28 individuals contains 56 files (each green box is a file). It is difficult to understand this history because there are so many files and because forward (R1) and reverse (R2) reads are unordered. As these files are analyzed and more outputs/files are created, it becomes very difficult to navigate around the history and understand how files are connected as inputs and outputs of particular tools or analyses.
Dataset collections make analysis of this mix of files straightforward by grouping all files into a collection that can be analyzed as a single unit. This example demonstrates using collections paired end data, but collections can be created for any set of files. Galaxy currently supports two types of collections: a simple collection of multiple datasets and paired collection containing dataset pairs, such as those generated by paired-end or mate-pair sequencing. For Galaxy users, collections can be used just as single datasets are used. When a user executes a read alignment tool on a collection of 100 pairedend datasets, the tool will run 100 times to align each dataset pair. Improved workflows. Galaxy workflows have been extended in several important ways. Galaxy now tracks the state of a workflow throughout its evaluations so that workflows can be paused and restarted.
Tours run completely within Galaxy and can include their own sample datasets. Analysis exercises. We have developed Galaxy Pages that provide hands-on exercises for scientists to learn about how to use Galaxy for a variety of analyses. Exercises for ChIP-seq, RNA-seq, variant calling, and metagenomics provide sample datasets, descriptions of each analysis step and questions to answer as the analysis is completed. These exercises have been used as assignments in university bioinformatics courses (https://wiki. galaxyproject. org/UniversityCourses) and in Galaxy training workshops (https://wiki. galaxyproject. org/Events). Community knowledge portal. We have created a custom BioStar (  17 ) question & answer Website to respond to and archive Galaxy user questions (https://biostar. usegalaxy.
Community and culture The Galaxy community is a vital component of the project. We have taken several steps to strengthen and grow in the Galaxy community in recent years. The Galaxy Training Network (https://wiki. galaxyproject. org/Teach/GTN) highlights training materials and workshops hosted by community members. We have also expanded opportunities for the community to gather online and in person. The GalaxyAdmins group meets online every other month, hackathons and regional Galaxy events (both virtual and in-person) happen throughout the year and around the world, and the yearly Galaxy Community Conference gathers over 200 community members for 5 days of training, hacking, presenting and networking (see https://bit. ly/gxyevents). Galaxy is an open-source project (https://github. com/ galaxyproject/galaxy), and in recent years we have taken several steps to make project participation more open and inclusive.
We introduced a formal process for project procedures and expanded the core development team to include members from seven different institutions. Through these processes and increased diversity, we now provide a transparent and documented way to help new developers start contributing to Galaxy and to enable strong contributors to become core team members. We have also implemented approaches to improve code quality, including a formal code review process for all pro-posed modifications. We use two continuous integration services (Travis CI [https://travis-ci. org/] and Jenkins [https: //wiki. jenkins-ci. org/]) which run a series of tests including code conformance, unit tests, functional tests, application programming interface (API) tests on each new pull request, effectively preventing most software regressions.
(B) Creation of a paired collection from the history shown in panel A. Because dataset names use a uniform nomenclature for forward and reverse reads, the collection creation form can automatically determine pairings. (C) Pairing these datasets generates a single item (a Collection) in Galaxy's history. (D) Clicking on this newly created Collection expands it and shows its content (only first three datasets are shown). (E) Galaxy's BWA interface takes the entire dataset collection as a single input.
ACKNOWLEDGEMENTS Over the years numerous individuals have contributed their time, talents and skills to building this project. These include the members of the Galaxy user and developer communities and organizers of Galaxy Community Conferences. We are indebted to these helpful people. The Public Galaxy site is located at the  Texas Advanced Computing Center (TACC at the University of Texas ). We are extremely grateful to both TACC and CyVerse for enabling Galaxy to serve thousands of researchers worldwide. This project is supported through grant number  HG006620  from the  National Human Genome Research Institute, National Institutes of Health  as well as grants  HG005133 ,  HG004909  and  HG005542  and  NSF  grants  DBI 0543285  and  0850103. Additional funding is provided by  Huck Institutes  for the  Life Sciences at Penn State  and, in part, under a grant with the  Pennsylvania Department of Health using Tobacco Settlement Funds.
INTRODUCTION In the last decade, database research has witnessed much interest in the W3C's Resource Description Framework (RDF), which is considered to be the most appropriate knowledge representation language for representing, describing and storing Semantic Web data and associated meta-data. This graph data model makes it possible to represent heterogenous Web resources in a common and unified way, taking into consideration the semantic side of the information and the interconnectedness between entities. SPARQL is the standard query language for querying RDF data. RDF data are usually composed of large heterogeneous data including various levels of quality e. g. , over relevancy, trustworthiness, preciseness or timeliness of data (see  [Zaveri et al. , 2014] ). It is then necessary to offer convenient query languages that improve usability of such data.
A solution is to integrate user preferences into queries, which allows users to retrieve data in a more flexible way. Motivations for integrating user preferences into database queries are manifold  [Hadjali et al. , 2011]. First, it appears to be desirable to offer more expressive query languages that can be more faithful to what a user intends to say. Second, the introduction of preferences in queries provides a basis for rank-ordering the retrieved items, which is especially valuable in case of large sets of items satisfying a query. Third, a classical query may also have an empty set of answers, while a relaxed (and thus less restrictive) version of the query might be matched by some items. Introducing user preferences in queries has been a research topic for already quite a long time in the context of the relational database model.
In the literature, one may find many flexible approaches suited to the relational data model: top-k queries  [Bruno et al. , 2002] , the winnow  [Chomicki, 2002]  and Best  [Torlone and Ciaccia, 2002]  operators, skyline queries  [Borzsony et al. , 2001] , Preference SQL  [Kießling, 2002] , as well as approaches based on fuzzy set theory  [Tahani, 1977]  [  Bosc and Pivert, 1995]  [Pivert and  Bosc, 2012]. The literature about preference queries to RDF databases is not as abundant since this issue has started to attract attention only recently. In this paper, we present an overview of approaches that have been proposed to make SPARQL querying of RDF data more flexible, followed by a classification of these approaches. The paper is organized as follows. Sections 2 and 3 briefly introduce background notions, namely the RDF data model and the SPARQL language.
A set of RDF triples can be modeled by a directed labeled graph (called RDF graph or simply graph in the following) where for each triple s, p, o , the subject s and the object o are the nodes, and the predicate p corresponds to the edge from the subject to the object. RDF is then a graphstructural data model that maintains the basic notions of graph theory (such as node, edge, path, neighborhood, connectivity, distance, in-degree, out-degree, etc. ). Example 1. Let us take the following example extracted from the Internet Movie Database (IMDb)  RDF provides a schema definition language called RDF Schema (RDFS), which allows to specify semantic deductive constraints on objects, subjects and properties of an RDF data graph. It permits to declare objects and subjects as instances of given classes, and inclusion statements between classes and properties.
SPARQL SPARQL  [Prud and Seaborne, 2008]  is the standard query language promoted by the W3C for querying RDF Data. It is a declarative query language based on graph pattern matching, in the sense that the query processor searches for sets of triples in the data graph that satisfy a pattern (i. e. , set of triples containing variables) expressed in the query. A SPARQL query has the general form given in Query 1, where the optional clause prefix is for abbreviating URIs, the clause select is for specifying which variables should be returned, the clause from defines the datasets to be queried, and the clause where contains the triple of the researched pattern. SPARQL also provides solution modifiers, which make it possible to modify the result set by applying classical operators like order by, distinct, limit, projection, or offset.
Example 2. Query 2 is a simple SPARQL query taken from the IMDb database that aims to retrieve the names of the movies of the genre Drama featuring George Clooney, sorted in ascending order of their release date, and limited to 10 responses. The variables are the terms prefixed by the question mark symbol. prefix dc : < http :// purl. org / dc / terms / > prefix rdfs : < http :// www. w3. org /2000/01/ rdf -schema # > prefix movie : < http :// data. linkedmdb. org / resource / movie / > select ? Name where { ? Actor movie : actor_name " George Clooney ". ? Movie movie : actor ? Actor. ? Movie rdfs : label ? Name. ? Movie dc : date ? Date. } order by asc (? Date ) limit 10} Query 2: Simple SPARQL query In the following, we present the contributions that extend SPARQL by the integration of user preferences in queries.
PREFERENCE QUERIES ON RDF DATA In the Semantic Web domain, some authors called for integrating the notion of preference into the SPARQL language in order to express requirements that faithfully reflect the needs of the users and produce discriminated answers. The proposed approaches may be classified into two categories according to their qualitative or quantitative nature. In the latter, preferences are expressed quantitatively by a monotone scoring function (the overall score is positively correlated with partial scores). In the qualitative category of approaches, preferences are defined through binary preference relations. Typical representatives of this category are approaches based on Pareto order, aimed at computing nondominated answers (whose set constitutes a so-called skyline). Note that these approaches only yield a partial order, contrary to the quantitative ones.
, 2010]. where FT denotes a fuzzy term and θ denotes an operator or comparator which can be fuzzy (such as close to (around), at least, and at most) or not (such as >, <, =, >=, <=, ! =, between and not between). The optional parameter [with α] specifies the smallest acceptable membership degree in the interval [0, 1]. Example 3. The fuzzy query "Retrieve recent movies with George Clooney" is formulated by the f-SPARQL Query 3. If the IMDb RDF database is queried, then the movie entitled The American (cf. Figure  1 ) belongs to the answer, with a satisfaction degree of 0. 25, which corresponds to the degree of membership of value 2010 to the fuzzy term recent (see Figure  2 ). The other movie from Figure  1 , released in 2007, does not belong to the answer as it is not recent according to Figure  2.
Now let us assume that the database of the running example embeds a rating value for each movie, through a property named dc:rate connecting a movie (URI resource) to a rating value (a label). When a user wants to express preferences on several attributes (e. g. , date, rating, etc. ), he/she may assign an importance to every partial preference. If no importance is specified, it is implicitly assumed that the partial degrees are aggregated by means of the triangular norm minimum which is commonly used in fuzzy logic to interpret the conjunction. In  [Cheng et al. , 2010] , the authors propose to use a weighted mean in order to combine the partial scores coming from the different atomic preference criteria: score(A) = n i=1 µ(Ai) × w(Fi), where F = (F1,. , Fn) is the set of filter conditions, Ai is the property concerned by Fi in the candidate answer A, µ(Ai) denotes the membership degree of the answer for Fi, and w(Fi) denotes the weight assigned to Fi, assuming w(Fi) = 1.
The same principle was initially proposed in  [Bosc and Pivert, 2000]  in the context of relational databases to process SQLf (fuzzy) queries. Some of the authors of  [Cheng et al. , 2010]  proposed a variant of f-SPARQL called fp-SPARQL  [Wang et al. , 2012]  that involves (i) an alternative way of interpreting modified fuzzy terms (i. e. , an atomic fuzzy term modified by an adverb such as extremely, very, rather and so on), and (ii) an alternative way of interpreting compound fuzzy conditions where atomic predicates are assigned a priority.
Qualitative approaches In the relational database domain, qualitative approaches to preference queries have attracted a large interest, in particular skyline queries  [Borzsony et al. , 2001] , which aim to filter an n-dimensional dataset S according to a set of user preference relations and return only the tuples of S that are not dominated in the sense of Pareto order. Let us consider two tuples t = (u1,. , un) and t = (u 1 ,. , u n ) from S (reduced to the attributes on which a preference is expressed). Tuple t dominates (in the sense of Pareto order) tuple t , denoted by t t , iff t is at least good as t in all dimensions and strictly better than t in at least one dimension. This may be represented by: t t ⇔ ∀i ∈ {1,. , n}, t. ui i t. u i and ∃j ∈ {1,. , n} such that t. uj j t. u j Example 6.
Let us assume that a user is looking for a good movie to watch, preferring a movie which is recent and high rated. Consider three movies M1 (date 2015, rating 5. 8), M2 (date 2013, rating 4), and M3 (date 2014, rating 8). Movie M1 is more recent and has a higher rating than M2. So, M1 dominates M2. Nevertheless, M1 does not dominate M3 since M1 is more recent than M3 but has a worse rating than M3. Hence, the skyline result is {M1, M3}. In the literature, some works  [Siberski et al. , 2006 , Gueroussova et al. , 2013]  have dealt with the expression and evaluation of skyline queries in a SPARQL-like language. In  [Siberski et al. , 2006] , SPARQL is extended with a preferring clause in order to support the expression of multidimensional user preferences. This extension is based on the principle underlying skyline queries, i.
DISCUSSION In the previous section, we have presented a survey of approaches from the literature that aim to extend the SPARQL language with preferences. We now further discuss these approaches and outline a few open research perspectives. Our first observation concerns the limited expressiveness of the approaches. Indeed, all of them are straightforward adaptations of proposals made in the relational database context: they make it possible to express preferences on the values of the nodes, but not on the structure of the RDF graph (structural preferences may concern the strength of a path, the centrality of nodes, etc. ). A still open research perspective is thus to define a flexible extension of SPARQL including constructs from preference query languages proposed in a graph database context, see e.
g. ,  [Pivert et al. , 2014] , taking as a basis, one of the (nonflexible) extensions of SPARQL involving navigational functionalities, e. g. ,  [Pérez et al. , 2010] ,  [Alkhateeb et al. , 2009] , etc. There is also a real need for a flexible SPARQL that takes into account RDF graphs where data is described by intrinsic weighted values, attached to edges or nodes. This weight may denote any gradual notion like a cost, a truth value, an intensity or a membership degree. For instance, in the real world, the information stored on the Web, as well as its metadata are far from being perfect and are represented by vague/imprecise knowledge. An imprecise information may be of the form "A movie is recent with the degree of truth 0. 9". The RDF data model should be enriched in order to represent such information, and new query languages should be defined.
To our knowledge, none of the existing approaches aims to define a general purpose flexible version of SPARQL to weighted RDF databases, which remains an open research perspective. Another related research perspective is SPARQL extensions for data-quality-aware preference queries. As far as we know, this research problem has not been tackled yet except for  [Hartig, 2009] , which only considers the trustworthiness dimension. Table  1  summarizes the main features of the approaches presented in the survey, as well as the few others briefly tackled in the discussion.
OECD meta-scanning: mapping the contours of the next production revolution To prepare for unpredictable and potentially disruptive change, it is necessary to look beyond the continuation of statistically strong trends and identify trend bends and breaks, the start of new trends that are already emerging in the present. This is enabled through scanning the horizon, that is, looking beyond the usual timeframe of decisionmaking and beyond the extrapolation of today's trends that are relevant when using a strategic frame of full employment and paid jobs. In 2014, the OECD Strategic Foresight unit piloted a globally, wide and deep horizon scan of multiple sources of data (a meta-scan) to help frame and inform a new strategic and horizontal work programme on the Next Production Revolution. The meta-scan inputs were three-fold: (1) Targeted search for recently published (within 5 years) Foresight reports by member and partner governments; (2) An extensive search of academic databases;  (3)  An open scan of internet sources and social media.
Acknowledgments This article presents a personal perspective and does not represent an official view of the OECD, or the views of its individual member countries. It has been prepared with the assistance of  Dr Betty Flowers  and  Joshua Polchar. Open Access This article is distributed under the terms of the Creative Comm ons Attribution 4. 0 International License (http:// creativecommons. org/licenses/by/4. 0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.