dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This research has been supported by the  French National Research Agency. We thank  Jean-Philippe Dedieu  for his useful insights during the preparation of the surveys, and  Marion Mercier ,  Anda David  and  Arouna Sougané  for their help during the data collection. We also thank  Devesh Kapur ,  Catia Batista ,  Cris Beauchemin ,  Jérome Valette  and three anonymous referees for very helpful comments.
Use of these guidelines It was the consensus of the Brighton Collaboration Working Group that the following guidelines are a desirable standard for collection of vaccine safety data in clinical trials involving pregnant women. These guidelines are intended for all parties involved in the planning, evaluation, and implementation of relevant studies including investigators, research networks, ethics committees and sponsors. The ability to implement these guidelines depends on a number of factors, such as the availability of resources, the availability of epidemiological information, the types of vaccines under study and the vaccine trial design. These guidelines are intended to be applicable in diverse geographic, administrative, and cultural regions, regardless of the differences in the availability of health care resources.
The proposed guidelines are not intended to guide or establish criteria for management of ill infants, children, or adults. They are not regulatory in nature and are not mandatory; the data collected in individual clinical trials will be dependent on the pre-specified aims and objectives, study setting and resources. These guidelines are not intended to replace established or mandated processes of adverse event reporting. The intention is to optimize and harmonize the use of data obtained from participants in clinical trials. The scientific purpose is to give added value to the reported results of individual trials by improving data accuracy and comparability. Additional data may be collected, analyzed, and presented as deemed necessary by investigators, ethics committees, regulators, and/or sponsors.
v. Adverse event monitoring data (including maternal, fetal and infant) While all outcomes of a vaccine trial in pregnancy need to be monitored, the Working Group acknowledges that it might not be practical to pre-define and solicit all possible clinical and laboratory outcomes. However, a core dataset should be collected in all vaccine trials in pregnancy, where feasible. Thus, each of these main sections has been further divided into subsections based on two priority levels for data collection: Priority 1: Essential: data considered essential for the understanding of the trial results and/or required by national and/or international regulatory authorities; Priority 2: Complementary: data considered complementary and important but not essential. The Working Group emphasized the need to record essential clinical data, in particular all evidence used to make a specific diagnosis.
For any individual vaccine trial, additional data may be collected depending on the capacities at a given vaccine trial site and the importance of those data for the primary or secondary objectives of the trial as determined by the investigators, research networks, ethics review committees and sponsors. 1. 4. 3. Specific data to be collected-Through the GAIA project a large number of clinical terms relevant to trials of vaccination in pregnancy were identified. Amongst these terms a limited number have been identified that require standardization and a (growing) subset of these have been formally defined  [43]. Investigators are encouraged to review this list and classify adverse events following immunization (AEFIs) according to these case definitions. Additionally, as part of this initiative a data collection matrix for use in clinical trial protocols (a collection of variables to be included in a CRF) has been developed  [40] , as well as an automated case classification tool  [44].
Specific data collection for maternal AEFI-This section applies to the mother and is in addition to the data collected and recorded for all mothers in Section 2. 4. 1. For local adverse events, refer to Brighton Collaboration case definition for data collection for a local reaction at or near the injection site  [49]. For systemic adverse events, refer to relevant Brighton Collaboration case definitions for appropriate guidance on data collection  [43]. For adverse events that might constitute a clinical vaccination failure, refer to the CIOMS case definition  [50]  for additional guidance on data collection.
Acknowledgments We wish to acknowledge members who participated in previous Brighton Collaboration working groups that lead to earlier versions of these guidelines: 2008:  Carol Baker ,  Thomas Cherian ,  James Crowe ,  Cornelia Dekker ,  Kathryn Edwards ,  William Gruber ,  Natasha Halasa ,  Michael Hudson ,  Gudrun Maechler ,  Rose Kambarami ,  Lakshmi Katikaneni ,  Najwa Khuri-Bulos ,  Noni Macdonald ,  Nuria Martinez-Alier ,  Wolf-gang Maurer ,  Peter McIntyre ,  Margaret Nakakeeto ,  Patricia Nell ,  James F Oleske ,  Ines Schoendorf ,  Ann Schwartz ,  Paul Shurin. 2012:  Hans ML Spiegel ,  Katrin S Kohl ,  Jane Gidudu ,  Paul Heath ,  Jan Bonhoeffer. We are grateful for the valuable review and comments received in the frame of the reference group review and to the members of the  Brighton Collaboration Science Board (Jim Buttery ,  Barbara Law ,  Kathryn Edwards ,  Michael Gold ,  Miriam Sturkenboom ,  Steven Black ,  Hector Izurieta ,  Egeruan Babatunde Imoukhuede ,  Heidi Larson ,  Daniel Salmon ).
Maternal medical and obstetric history-This section defines data that should be collected and recorded for all vaccine trial participants. Permission to review the maternal prenatal obstetric and general medical records should be included in the consent document. For prior pregnancies, document dates of delivery or termination of pregnancy, history of multiple pregnancies, pregnancy complications, history of cesarean section (elective or emergency), pregnancy outcome(s) (live birth, still birth or abortion) and history of previous early-onset neonatal infection (Priority 1)2Infections: results of any routine antenatal screening tests for infections (Priority 1). Additionally, laboratory investigations should be performed for infections that may have impact on the immunogenicity, efficacy and safety of pregnancy vaccines or aid interpretation of events that occur in the mother, fetus, neonate or infant (Priority 1).
Acknowledgments The authors would like to acknowledge the support of the  EPSRC  for funding  A I M Greer  and  I Vasiev through DTAs  (( EP/P505534/1 ) and ( EP/J500434/1 ) respectively). Nanoimprint moulds were fabricated in the  James Watt Nanofabrication Centre (JWNC) at The University of Glasgow. We are grateful for the support and training provided by the JWNC technical staff. We are grateful to  Micro Resist Technology  for supplying the nanoimprint resist for this study, in particular  Manuel Thesen  and  Martin Messerschmidt  for their consultations.
Thus, computer modeling has found applications in FRA of transformers  [16]. The first study on frequency response analysis was reported in 1978  [17]. However, the application of FRA method on power transformers became industrial practice approximately a decade ago. Now this technique has been standardized  [13]. However, there are still many challenges regarding the interpretation of the test results obtained by this method. There is no standard available to qualitatively identify the type of fault in the windings and quantify the degree of severity of the winding displacement. Thus, there are no clear guidelines for interpreting FRA data to unambiguously detect the faults in the transformers. A few researchers have used mathematical models to determine the resonant frequencies to relate frequency response data to the transformer mechanical structure and to quantify significant winding changes.
The company MSD India initiated a programme to educate patients and their families about hepatitis C and to help to manage the cost of treatment; its Project Sambhav was launched in the Punjab in 2012 with two elements: disease management and microfinancing. The Onom Foundation in Mongolia, working closely with the national Government, has instituted a prevention campaign that includes prevention, screening and early diagnosis, on-site rapid testing projects, lobbying, and the creation of a national database on viral hepatitis. The International Decision Support Initiative was created by the UK's National Institute for Health and Care Excellence, with support from the UK Government, the Bill & Melinda Gates Foundation and the Rockefeller Foundation, in order to guide decision-makers in LMICs to effective and efficient resource allocation strategies for improving people's health.
Acknowledgement We would like to thank all participants who contributed to the success of this roundtable. This meeting was organized by the  VHPB  and the related project was supported by the  IFPMA (International Federation of International Federation of Pharmaceutical Manufacturers & Associations ). The  Viral Hepatitis Prevention Board  is supported by unrestricted grants from  GlaxoSmithKline Biologicals ,  Sanofi Pasteur MSD ,  Sanofi Pasteur ,  Merck ,  Gilead ,  Abbvie  and several European Universities and other institutes.
Acknowledgments We cordially thank  Y. Iwashita  for deeply considering the possibility to introduce the permanent-magnet-based dipole at IP in the electron transport line to avoid Møller's scattering in advance. K. Homma appreciates  E. Milotti  and  C. Curceanu  for discussions on the derivation for the QED-based cross section. He also acknowledges for the support by the  Collaborative Research Program of the Institute for Chemical Research, Kyoto University.
6: Update un+1 = un+1/2 + t 2 ün+1. 7: end for Algorithm 3 Initialization for the implicit and explicit time-stepping procedure described in Algorithm 1 and 2. 1: Given initial conditions u 0 , u0 and α -1. 2: Reinitialize the damage α 0 = argmin q u 0 (•) subjected to constraints 0 ≤ α -1 ≤ α 0 ≤ 1. 3: Initialize the acceleration M ü0 = F 0 ext -F int (u 0 , α 0 ). 0. The initial step 2 thus renders it compatible with the initial displacement condition and the energy minimization structure. Both the implicit and explicit time-stepping Algorithms 1 and 2 are implemented as a Python package named "FEniCS Dynamic Gradient Damage". It is based on the FEniCS Project  [26]  for automated solution of partial differential equations.
Acknowledgments The authors thank  UPMC ,  CNRS ,  IUF  ( MM & LF ),  ANR CREDOX  ( ANR-10-BLAN-0701 ) &  Radicaux Verts  ( ANR-06-BLAN-0309 ) for financial support. Special thanks to  Lise-Marie Chamoreau  for the X-ray analyses of 17 and 27a.
We investigate the problem of aligning two RDF databases, an essential problem in understanding the evolution of ontologies. Our approaches address three fundamental challenges: 1) the use of "blank" (null) names, 2) ontology changes in which different names are used to identify the same entity, and 3) small changes in the data values as well as small changes in the graph structure of the RDF database. We propose approaches inspired by the classical notion of graph bisimulation and extend them to capture the natural metrics of edit distance on the data values and the graph structure. We evaluate our methods on three evolving curated data sets. Overall, our results show that the proposed methods perform well and are scalable.
INTRODUCTION Identifying references to the same real-life entity is one of the most fundamental concerns in databases. It plays an important if not crucial role in virtually all non-trivial data processing tasks from computing join of two tables to removing duplicate entries in data cleaning  [15]  to combining data objects in multiple databases in data integration  [3]. This problem comes in a number of flavors depending on the type of data used to identify the entity represented by a given data object. Ideally, as in the case of a well-designed stand-alone database, a consistent system of unique identifiers supports the linkage of objects in a manner that is reliable and efficient. However, independent databases may use different and often incompatible schemes of unique identifiers. Consequently, linking their contents may require using other methods, based on data values and the structure of the databases to match corresponding identifiers  [8].
In this paper we study an instance of this problem that arises in the context of evolving RDF graphs: for two consecutive versions of an RDF graph we wish to construct an alignment that connects pairs of nodes in the two versions that represent the same entity. RDF is essentially an edgelabeled graph that uses URIs (Unique Resource Identifiers) as nodes and edge labels but also has blank nodes, which are not persistent identifiers, as well as literal nodes, which store (unique) data strings. Because of the varied types of nodes aligning two RDF graphs presents a number of interesting challenges. While it is reasonable to assume that two nodes labeled with the same URI represent the same entity, the converse is not necessarily true. Indeed, the same entity may be represented in different versions with different identifiers, for instance, as a result of changing the scheme of attributing URIs.
Even more problematic are blank nodes, which although discouraged are often misused when using reification for purpose of representing data structures such as lists and records  [5]. Because blank nodes are not persistent identifiers, we require methods to establish an identity for a blank node based on a description by its neighborhood in the graph. This however is a nontrivial task because both the data values and the connections may undergo modifications in the subsequent version of the RDF graph. Finally, constructing an alignment between two RDF graphs presents a significant computational challenge: RDF graphs tend to be large, which quickly renders infeasible any method that attempts to perform pairwise comparison between all pairs of nodes of the two graphs. We investigate a number of methods of aligning RDF graphs inspired by the classical notion of bisimulation for graphs.
We also show that any pair aligned with this method is also aligned with the similarity method we wish to approximate. The main contributions of the present paper are summarized as follows: 1. We formalize the problem of RDF graph alignment and present a methodology of aligning RDF graphs with partitions of the nodes. 2. We propose RDF alignment methods based on the standard notion of bisimulation for graphs that handles blank nodes and changes in ontology (URI naming schemes). 3. We propose a natural measure of node similarity that yields an RDF alignment method robust under editing operations and extend the bisimulation approach to approximate the proposed similarity method. 4. We evaluate the accuracy and effectiveness of these methods on widely used databases presented in RDF.
Related Work. The similarity measure we define bears some resemblance to the similarity flooding approach  [12]  with an important difference on on how similarities are propagated: when defining the similarity of two nodes, the similarity flooding takes a weighted average over the Cartesian product of sets of outgoing edges of the two nodes while our approach identifies the optimal matching among the outgoing edges. We believe this approach to be more appropriate in the context of evolving graphs and incorporates edit operations on the edges. Still, the inherent high complexity of both methods limits their scalability, and the aim of our research is development of scalable methods for identifying similar objects. There is extensive work on entity resolution in the context of relational databases, however a comparison with that work is problematic.
Because we are using RDF and because we are placing predicates on the same footing as other URIs the problem we are setting ourselves is equivalent to finding an alignment between two versions in which one (a) changes all the table names and column names and (b) changes all the key values. All that is kept are the non-key data values and the foreign key constraints. In other words are trying to find an alignment between two versions of a relational database in which one applies a bijective map to all the column names and to all the key values as well as making some updates to one of the versions. There is also extensive literature on graph alignment  [2]. Constructing an alignment between two graphs is virtually equivalent to constructing their delta  [20] , a description of changes occurring between the two graphs.
Data model RDF graphs are typically represented as sets of triples of URIs, literals, and blank nodes. Because we are dealing with two graphs that may contain the same URI we need a more general model that uses node identifiers and treats the URIs and literals as labels: we assume an enumerable set of node identifiers N and a set of labels I = U ∪ L ∪ { b}, which consists of URI labels U, literal values L, and a special blank value b used to label blank nodes. We assume that U and L are disjoint and neither contains b. Definition 1. A (triple) graph is a tuple G = (NG, EG, ℓG), where NG ⊆ N is a finite set of nodes, EG ⊆ NG × NG × NG is a set node triples (edges), and ℓG : NG → I is a node labeling function. The URIs of G, URIs(G) are those nodes n ∈ G for which ℓ(n) ∈ U. Literals(G) and Blanks(G) are defined similarly.
EXPERIMENTAL RESULTS In this section we report on experimental evaluation of the proposed solutions on three practical data sets: EFO -Experimental Factor Ontology  [11]  supported by the European Bioinformatics Institute, GtoPdb -The Guide to Pharmacology database  [4]  supported by the International Union of Pharmacologists and the British Pharmacological Society, and a subset of DBpedia with Wikipedia category information. A brief word about the first two databases. EFO provides a systematic description of many experimental variables available in other databases and combines parts of several biological ontologies. It is expressed in OWL, which is in turn reasonably directly expressed in RDF. GtoPdb is a relational database that contains curated information from hundreds of experts about drugs in clinical use and some experimental drugs, together with information on the cellular targets of the drugs and their mechanisms of action in the body.
We converted GtoPdb into RDF using a standard (W3C recommended) approach  [18]. Both databases are evolving; new versions are released every few months. Both databases are usually viewed through a Web interface and despite their internal representions have a similar general nature consisting of classification hierarchies along with a rich annotation. However their representation in RDF is very different. For example, in EFO the notion of a subclass is directly represented, while in GtoPdb it is inferred from the relational database.
Because our methods focus on the outgoing neighborhood of a node, they make errors by incorrectly aligning URIs that are used as predicates only: these URIs typically are present as subject in one triple that declares the type of the URI (and uses rdf:type as predicate). The number of such incorrectly aligned predicates is relatively small (< 15). A better solution would identify URIs that are predominantly used as predicates and use a different refinement process, for instance, one that incorporates the colors of the subject and the object in any triple that uses the given predicate. Finally, we found the quality of the hybrid and overlap alignments to be overall satisfying: very few URIs undergoing changes are missed and no URIs are aligned in error. Unfortunately we cannot precisely evaluate it because we lack the appropriate ground truth for the EFO dataset and we present a more detailed discussion in the appendix of the complete paper.
Guide to Pharmacology database (GtoPdb) We used 10 versions of the GtoPdb relational database, which we exported to RDF following the W3C Direct Mapping recommendation  [18]  using the D2RQ platform. The mapping works as follows: 1) every tuple is identified by a URI which is constituted from a given URI prefix, the table name table, and the attribute values of the primary key, 2) (non-referential) value attributes are translated to edges consisting of the tuple URI, the attribute name and a literal for the attribute value, 3) referential attributes are translated to edges pointing to the URI of the referred tuple. While this experimental setting has been designed to evaluate the hybrid and overlap alignments, we believe it captures a common situation in which a relational database is exported to RDF at different times by different services using similar export schemes (e.
DBpedia To evaluate scalability of our methods, we report in Figure  16  the running times on a subset of DBpedia containing category information (including hierarchical information and Wikipedia article categorization). We used versions 3. 0 through 3. 5, run our experiments on a MacBook Pro with 2. 3 GHz Intel Core i7, 16 GB RAM, and 512 GB SSD, our (single-thread) implementation was in Python 2. 7. The RDF graphs progressively grow from 2. 6M nodes and 7. 6M edges to 4. 2M nodes and 13. 7M edges. The performance of our methods fluctuates mainly due to the varying number of overlapping nodes between two consecutive versions. The general trend appears proportional to the size of the input graphs. Furthermore, the execution times are in line with those presented in  [16] , which suggest that our methods should scale to larger datasets, using methods such as MapReduce.
While our methods are relatively straightforward, they have been designed with simplicity and possible extensions in mind. In the future, we would like to explore variants of our approach where only selected parts of the outbound neighborhood are used, for instance specified by a notion of a key for graph, possibly allowing to align nodes of graphs following different structure, or even a broader context of the node involving its inbound neighborhood and the triple where the node is used as predicate, possibly allowing to better align them. Our experiments show, however, that the presented methods perform very well in the scenario of evolving RDF database. An interesting question arises: can the (constructed) alignments be used to construct compact representations of all versions of an RDF database? One way of approaching this would be to decorate triples with intervals that represent versions where the triple was present.
Acknowledgements We are grateful to  Simon Jupp  and  Tony Burdett  for discussions on the EFO database and to  Joanna Sharman  and  Jamie Davies  for the GtoPdb data. The referees also made many useful comments. This work was funded by the  EU DIACHRON project , the  EPSRC   SOCIAM  project and  NSF IIS 1302212 : Citing Structured and Evolving Data.
Belgeo, 3 | 2016 Table 3. Regional and high-speed train service at Châteaucreux station, as of September 2015. Data collected by the author, from the SNCF and TER websites Source: adapted from Wikipedia Commons 33 However, real estate developers consider the TGV as an asset for business purpose, as it provides a direct service to Paris, the capital city of a still monocentric France. The first train arrives in Paris at 9 am and the last train leaves Paris at 7 pm: it allows a round-trip journey on the same day, which saves the additional cost of a night in a hotel. Conversely, the first train from Paris arrives in Saint-Etienne before 10 am and the last train leaves Châteaucreux station after 6 pm, which also enables a round trip journey on a day from Paris. From a business point of view, the high-speed connectivity then makes up for the distance, as it provides a direct and rather fast service between Saint-Etienne and France's major financial centre.