dataesr/scientific-paragraphs-categorization huggingface dataset:
Initial expertise (color of the bar) vs final confidence (y-axis) after the GRU workshop for participants responding to final survey. Example for how to interpret this graphic: the blue color bar at the top indicates that before the workshop roughly 50% of respondents said their knowledge of GEOLocate was "neither high nor low" but after the workshop these same respondents selected "much higher" for their knowledge of GEOLocate. Original locations are sometimes stored, and the less-precise georeferences are displayed. No currently existing methods that we know of offer easy ways to ensure these withheld data get published when finally appropriate to do so, if ever. The strongest finding was that 62% of workshop participants increased their use of QGIS since the workshop and 77% expect to use the software increasingly over time, highlighting the importance of training opportunities for career professionals in all sectors of the biodiversity data realm.
Biocollection workflows are changing Key conversations at the workshop centered around capturing historical versus future biocollections data. All participants, data researchers and data providers noted changes occurring in biocollections data workflows, yet outstanding challenges continue. For all of these reasons and more, there exists a need for ongoing initiatives to address these topics. • Significant quantities of specimen data collected remain on paper specimen labels, in notebooks, catalogs, and field cards, limiting online open access and resulting in patchy datasets.
Importance of access to centralized resources Finding materials and expertise can be challenging and time-consuming. Development of new tools and workflows continues. Participants appreciated consolidated resources like those found at iDigBio and the Biodiversity Catalogue. Aggregation of this information saves time and effort. Some topics and tools covered stood out from the rest for all participants. These include: template generation for data collection, such as the Biocode Field Information Management System, application development with Open Data Kit (mobile-first data collection platforms), and discussion and development of a data validation process (Suppl. material 7).
Improved communication about data quality Data aggregators like GBIF, Atlas of Living Australia, and iDigBio are working through the Biodiversity Science and Standards TDWG/GBIF Biodiversity Data Quality Interest Group to harmonize biocollections data quality (DQ) feedback. Aggregators provide these standardized DQ assertions: 1) to those browsing the data online, 2) to the data providers, and 3) in any downloaded datasets. However, this DQ process, along with the tools, methods, and data standards used during data capture and publishing are not always understood in the research community. For instance, data aggregators currently use taxonomic name assemblages to facilitate searching and indexing of aggregated data. But as a result, the taxonomy may not be what some expect or need  (Mesibov 2018,Franz and Sterner 2018).
Conclusions The Georeferencing for Research Use workshop was a successful workshop based on the discussion, survey results, and issues reflected in the captured conversations. It created an important platform for biocollections data providers to learn directly from the researchers who hope to use the data they provide and vice versa. As we continue to provide data about specimens, and learn to use the data in research, workshops that provide this kind of cross talk will continue to be important learning platforms that will improve the quality of research and data products. Workshops of this type also offer strategic opportunities to discover future leaders and innovators in our community as the role of collection and data managers evolve to support faster data mobilization and more robustly standardized and complete datasets.
With large volumes of data acquired every month, the Copernicus satellites provide essential information for analysing and monitoring our environment. However, technical and knowledge barriers may affect user's uptake of such a wealth of information. The RUS (Research and User Support for Sentinel Core Products) Service (funded by the EC and managed by ESA) began operations in October 2017 and aims to support overcoming such issues. A scalable cloud environment offers the possibility to remotely store and process data by bringing data and associated processing closer to the user. An integral part of the solution is the exploitation and adaptation of the platform, Free and Open-Source Software (FOSS). In addition, technical and scientific support (including training sessions) are provided to simplify exploitation of Copernicus data.
Introduction In November 2016 the monthly volume of data acquired by the three operational Copernicus satellites (Sentinel-1a, Sentinel-1b and Sentinel-2a) accounted for 150 TB  [1]. With the launch of Sentinel-2b, Sentinel-3a and Sentinel-5P this volume of data has at least tripled, implying that with a download speed of 15 Mps (average connection speed in Europe  [2] ), almost 8 years would be needed to download one month of all observations. In addition, a very performant computing power is needed to process such data (whose size, in the case of Sentinel-1 might be larger than 1 GB/product and 500 MB/product for Sentinel-2). Finally, besides "physical barriers" there might be "knowledge barriers" related to the complexity of the image information, understanding of the formats, the applicability of the data to specific applications or reluctance to absorb the data into pre-existing routines managed by the user.
ICT Solutions To overcome physical issues (e. g. , downloading, storing and processing), the service exploits Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). IaaS includes network access, Virtual Machines (VM) with Computing Processing Units (CPU) and a scalable storage capacity. The PaaS includes data access (direct access to Copernicus Hub), communication tools (mail, chat, audio-conference and video-conference with the Helpdesk), processing and viewing tools, development tools, collaboration tools, as well as all necessary and relevant documentation and internet links. FOSS is pre-installed on demand on the VM, however users are free to install their Commercial Off-The-Shelf (COTS) software on the machine. The infrastructure relies on several types of virtual environments: • Collaboration environments hosting a platform to offer collaboration services such as video-conference and chat, the Front Desk, the Administration Desk and the Service Management Desk.
• User environments hosting the development and processing platform: each RUS user could have access to a dedicated cluster of user environments. Thanks to this environment, RUS Users can access Sentinel data using the data platform, develop algorithms and process this data using their dedicated cluster and benefit from interactive support from RUS Operators through services offered by the collaboration platform. Use of Copernicus datasets as the main source of information is a prerequisite to access the RUS Service, but non-Copernicus data (EO and other data) can also be freely used and imported by the users. The VMs provided by RUS work on a Linux environment where either FOS or COTS can be installed and also includes programming and scripting environments. Default Processing libraries account for: GDAL, Sentinel Toolboxes, Orfeo Toolbox and SNAPHU; pre-installed processing tools include QGIS and SNAP, whereas current software development utilities are: Oracle JDK 1.
8, Apache Ignite, Eclipse, GCC, CMAKE, Maven, GIT, Python 2. 7/3. 5, and R 3. 3. The ICT for the user is defined following an analysis of the received service request; such analysis defines the scaling of the work environment in terms of duration, disk space and size (number of Virtual Machines, number of cores per machine, RAM per core). Considering resource constraints, the RUS Service can be offered to each user for a limited amount of time and including ICT/Expert/Data resources compatible with declared uptake objectives and current user demand. Three pre-defined work environments are typically proposed: 1-4 cores with disk space up to 1 TB for 3 months, 1-10 cores with disk space up to 10 TB for 6 months or up to 40 cores with disk space up to 50 TB for 6 months. More information about the RUS Service and access to the VM can be found at: https://rus-copernicus.
Building Knowledge To complement the ICT offer, training and outreach activities aiming to create a critical mass of Copernicus data users and focusing on a large portfolio of applications, are side supporting activities surrounding the service pyramidal layers. Use of the RUS Virtual Machines with pre-installed FOSS facilitates handling of such events, where participants can use their own laptops to manage the processing. The use of the same configuration for each VM in fact discards any pre-existing difference between the used laptops (and operating systems), facilitating the smooth running of the event. Face to face events are organized to meet the requirements of small groups of users which receive specific training on EO theory and then are guided step-by-step by the trainers, in the application of the learned theory in practical case studies.
The assigned VM remains accessible to the user for several months after the training, so as to allow repeating or completing the exercises (or performing other processing activities). Large Webinars organized every month aim to attract new potential users by providing in a condensed format, the instructions to perform some basic processing steps to exploit Sentinel data for a specific application. They are closed by Q&A sessions, offering the participants the possibility of interacting with the trainer. The Webinars are recorded and made publically available for re-play on a dedicated YouTube channel. Users interested in repeating the exercise can either use FOSS installed on their computer or ask RUS for access to the pre-configured VM with all the material needed to perform the exercise.
Ship Detection Ship detection with Sentinel-1 enables detection of vessels not carrying an Automatic Identification System (AIS) or other tracking systems on board, such as smaller fishing ships or ships that might be in the surveyed area illegally (illegal fishing, piracy etc. ). As SAR is not reliant on solar illumination and is rather independent of weather conditions, frequent monitoring is possible. The exercise exploits ESA's Open Source Sentinel-1 Toolbox to process Sentinel-1 data, detecting targets larger than 30 m in the Gulf of Trieste. Final output can be visualized in Google Earth (Figure  1 ) or be exported as a point layer to an Open source GIS (QGIS). RUS VM are used for running the exercise. More information about the use of Sentinel-1 data for maritime surveillance can be found in  [3].
Discussion In the first two cases presented, lack of in situ observations simultaneous to the acquisition do not allow thorough validation of the results, therefore they should only be considered as examples of well-established methodologies. In the case of the deformations associated to the November earthquake, the distribution of fringes and estimated line of sight motion are well in agreement with studies carried out by other authors with the same datasets, as well as with different data  [5]. Tutorials to reproduce the results described above for exploiting the RUS service are being made freely available on the dedicated RUS YouTube channel https://www. youtube. com/channel/UCB01WjameYMvL7-XfI8vRIA. Furthermore upcoming training events are announced through social media, such as Twitter (@RUS_Copernicus) and Facebook (https://www.
The algorithm then alternates between assigning each point to the closest centroid and updating each centroid to be the mean of its corresponding points (lines 4-6). This procedure is analogous to using a clustering algorithm (such as k-means  [37] ) to find k clusters in a dataset that contains many well-spread points. Therefore, constructing a CVT can intuitively be seen as forcing the k sites to be well-spread in the space of interest. Algorithm 2 MAP-Elites algorithm 1: procedure MAP-ELITES([n 1 ,. , n d ]) 2: (X , P) ←create empty archive([n 1 ,. , n d ]) 3: for i = 1 → G do Initialization: G random x 4: x = random solution() 5: ADD TO ARCHIVE(x, X , P) 6: for i = 1 → I do Main loop, I iterations 7: x = selection(X ) 8: x = variation(x) 9: ADD TO ARCHIVE(x , X , P) 10: return archive (X , P) 11: procedure ADD TO ARCHIVE(x, X , P) (p, b) ←evaluate(x) if P(c) = null or P(c) < p then 15: P(c) ←-p, X (c) ←-x CVT-MAP-Elites partitions the d-dimensional feature space into k Voronoi regions and then attempts to fill each of the regions through a selection-variation loop.
Fig. 7 : Best performance (walking speed, thus, higher is better) for each algorithm-descriptor pair in the undamaged case and the 6 damage conditions which correspond to removing a different leg of the hexapod robot. The box plots show the median (black line) and the interquartile range (25 th and 75 th percentiles) over 20 solutions; the whiskers extend to the most extreme data points not considered outliers, and outliers are plotted individually. The performance of the best solutions found by MAP-Elites diminishes as the dimensionality of the descriptor increases. In contrast, CVT-MAP-Elites retains its performance with the increased dimensionality, thus, it scales significantly better than MAP-Elites. In both the undamaged and damaged conditions, the expected best performance of MAP-Elites decreases with each increase in descriptor dimensionality, whereas the one of CVT-MAP-Elites remains relatively constant (MAP-Elites vs CVT-MAP-Elites; median gaits given in m/s): undamaged: [6D: 0.
Interestingly, such a routine could be designed in a way that places more centers (thus, more variation) along certain dimensions. To ease deployment, we provide a python script for generating the centroids (see Appendix B). The computational complexity of the method we provide for constructing the CVT (in Alg. 1) is O(ndki), where n is the number of d-dimensional samples to be clustered, k is the number of clusters and i is the number of iterations needed until convergence. From our experiments, we noticed that the clustering does not need to be very precise when using such a large number of clusters (i. e. , thousands), thus, the number of iterations i can be fixed to limit the overall complexity of CVT-MAP-Elites. Nevertheless, one could resort to other CVT construction methods, which could offer significant speedups  [35].
Furthermore, finding the centroid closest to a given behavior descriptor can be accelerated using data structures such as k-d trees  [38]  or others that exploit the characteristics of the Voronoi tessellation (e. g. , see  [43] ,  [33] ). A key factor that enabled CVT-MAP-Elites to scale to 1000 dimensions in the maze experiments is the proper sampling of trajectories to generate the CVT and, as a consequence, the generation of centroids that more closely approximate trajectories that could be followed by the robot. A naive approach for sampling trajectories would sample each element along the trajectory from [0, 1000] 2 in the 1000 × 1000 arena. This, however, would result in unrealistic centroid trajectories, because the robot cannot move in a single time step to any point of the arena (it is constrained by its maximum speed).
Introduction The Web of Data, and in particular Linked Data  [10] , has seen tremendous growth over the past years, leading up to the availability of a large amount of RDF datasets 1 on the Web, where a recent crawl 2 of linked datasets retrieved over 1000 datasets alone, including 1 For readability, we use the terms "RDF dataset" and "dataset" interchangeably within this survey. 2 http://linkeddatacatalog. dws. informatik. uni-mannheim. de/state/ over 8 million explicit resources and an estimated 100 billion triples  [66]. RDF datasets and their inherent subgraphs vary heavily with respect to their size, topic and domain coverage, the resource types and schemas as well as the dynamics and currency. To this extent, the discovery of suitable RDF datasets, which satisfy specific criteria, has become a challenging problem for a variety of applications including entity linking, entity retrieval, distributed search and query federation, just to name a few examples.
This prevalent problem is underlined by the strong bias towards using established and well-known reference 0000-0000/16/$00. 00 c 2016 -IOS Press and the authors. All rights reserved knowledge graphs such as DBpedia  [4] , YAGO  [71]  or Wikidata  3  , although there exists a long tail of potentially suitable domain-specific yet under-recognized datasets. We begin by providing definitions of several central concepts of our study. In this survey, an RDF dataset is defined in accordance with the dataset definition in the VoID Vocabulary 4  stating: "A dataset is a set of RDF triples that are published, maintained or aggregated by a single provider"  5. According to VoID, this definition reflects the social dimension, such that a dataset represents a meaningful collection of triples as envisioned by its provider, such that this dataset would benefit from descriptive metadata.
A Dataset Profile Feature is a metadata element describing a certain attribute of the dataset. For instance, "dataset dynamicity" is a dataset profile feature providing information on the temporal variation of the dataset. Descriptive metadata consisting of a collection of dataset profile features constitute a dataset profile. A dataset profile is a substantial building block in facilitating effective application-oriented dataset discovery and usage. An RDF Dataset Profile is a formal representation of a set of dataset profile features. A dataset profile characterizes the dataset and aids dataset discovery, recommendation and comparison with regard to the represented characteristics. A dataset profile is extensible with respect to the features it contains. Usually, the relevant feature set is applicationoriented and depends on the envisaged application scenarios.
A number of popular dataset registries have emerged, which tackle the problem of dataset discovery through the curation of light-weight dataset descriptions, often also exposing structured metadata according to the state-of-the-art vocabularies such as DCAT  6  or VoID. Popular examples include DataHub  7  or DataCite  8  , while the LinkedUp Catalog  9  represents a domainspecific example. However, while such metadata is usually edited and curated manually, it is often sparse, not in sync with the constant evolution of the actual datasets and prone to errors. On the one hand, as the Web of Data as a whole is evolving along with the constant evolution of individual datasets, manual assessment and representation of a large variety of dataset features is neither feasible nor sustainable. On the other hand, a wide variety of competing as well as complementary approaches exist, aimed at automatic assessment and description of arbitrary datasets.
This body of work is spanning several research communities and includes works in fields such as dataset characterisation, data summarisation, dataset assessment or dataset profiling. While this problem is of particular importance in the context of Linked Data, it has been identified and approached already in related fields, such as general database and data management research. Emerging from the aforementioned works, a wealth of tools, methods, vocabularies and applications for assessing, describing and profiling of datasets has become available throughout the past years, where a comprehensive overview and classification is still missing. A myriad of terms and notions does co-exist, whereas a clear distinction, classification and comparison is still required. Only recently, first efforts  [24]  have been made to bring together such disparate yet closely related fields.
The aim of this survey is to provide researchers, dataset providers and application developers with an overview of dataset profiling and closely related approaches, including dataset profile features, feature extraction methods and tools, vocabularies and applications to encourage experimentation and facilitate broader use of RDF datasets. Being the first comprehensive study in this area, we provide a thorough analysis and definition of related terms and typical dataset profiling features. Furthermore, we provide a systematic study of the available methods and tools for assessing and profiling structured datasets and survey state-of-the-art vocabularies for representing structured dataset profiles. While some of the discussed works are dedicated to profiling of graph-based RDF datasets in particular, works of relevance from other related fields are also discussed.
It should be noted that the authors are aware that domain-specific approaches to profile and annotate datasets exist. However, to ensure high relevance and applicability, this survey addresses exclusively cross-domain approaches, which are agnostic to the domain of the profiled data. In summary, in this survey we provide the following contributions: a taxonomy of dataset profile features, including semantic, qualitative, statistical and temporal feature categories; a systematic overview of dataset profile feature extraction approaches and tools discussed in the context of our dataset profile feature taxonomy; an overview and a classification of available vocabularies for representing dataset features and profiles; an illustration of the use of dataset profiles in several application scenarios.
The remainder of the survey is organized as follows: In Section 2, we present the adopted methodology to collect and organise the publications included in this survey. Next, we provide a comprehensive set of commonly investigated dataset features (Section 3), based on the existing literature in the field of dataset profiling and organize these features in a taxonomy. Then, we provide an overview of the existing approaches and tools for the automatic extraction of dataset profile features (Section 4). Following that we provide an overview of the existing RDF vocabularies for the representation of certain dataset profiles and features (Section 5). Where feasible, we also provide suggestions on the vocabulary use and offer vocabulary recommendations suitable for representing particular dataset profile features.
Literature Review Each category of the dataset profile features taxonomy covers a large range of works in the Semantic Web field and can be surveyed in a separate paper. In this article, we provide a pivotal guide for readers to obtain a global view on the various dataset profile features illustrated by examples. For this purpose, we focused our review on the existent surveys in each category of the dataset profile taxonomy, while providing some examples for: (i) the identification of the feature extraction methods (cf. Section 4), (ii) the identification of vocabularies for datset profiles representation (cf. Section 5), and (iii) the identification of some application-driven profiles (cf. Section 6). Of all the criteria considered, this was the one that produced the sharpest cut down on the number of the articles to be reviewed in detail.
Paper Selection and Review By applying a careful review and paper comparison, we obtained a final list of 85 papers to be included in this survey ranging from 1996 to 2016 with about 70% of articles originating from  [2010] [2011] [2012] [2013] [2014] [2015] [2016]. The selected works are retrieved from different journals, conferences and workshops, mainly in the Semantic Web field as follows: Journals  To sum up, in this article, we intend to give the reader a bird's-eye view on the RDF datasets profiling problem (whether or not referred to explicitly by using this term) while providing some examples of worm'seye view especially in terms of feature extraction methods, application-driven profiles and vocabularies for dataset profiles representation.