dataesr/scientific-paragraphs-categorization huggingface dataset:
A move to Linked Open Data is expected to obviate the need for enlarged metadata by making data more easily discoverable through concept linkage (see paragraph 15 et seq. below on Linked Data). Sustaining the physical infrastructure 14. Appropriate biodiversity informatics tools will generate greater impact than is currently possible from the physical infrastructure of natural history collections, mesocosms, other experimental facilities, long-term ecological monitoring sites and genomics facilities, through much greater digital and on-line access to the facilities than is physically practical. This will enhance the sustainability of the infrastructure, since a large user base is critical for political sustainability. Why do we need vocabularies and ontologies? 16. Common vocabularies are the foundation for both human and machine communication (e.
g. in data sharing, in automated workflows, data integration and analysis). By agreeing on a set of concepts and their definitions within a domain, a community of practice can share data and information unambiguously. Data integration and analysis critically requires semantic consistency as well as syntactic standardisation, the former being more challenging to achieve than the latter. Initially communities will accept a small controlled vocabulary -terms supported by human-readable text definitions. As terms are rarely independent of one another, the vocabulary list evolves into a thesaurus and, as formal relationships between terms are agreed, an ontology  [66]. There are lessons to be learnt by looking elsewhere, for example, Google's "Knowledge Graph"  [67] , the Unified Medical Language System (UMLS), medical informatics)  [68] , AGROVOC (agriculture)  [69]  and OBO (plant and animal phenotypes) stable of ontologies  [70].
It is crucial that future efforts in this area take account of major global initiatives, especially GEO BON, GBIF and Genomics Standards Consortium, as well as novel approaches in eco-informatics, but it is likely that the data models used in these initiatives will also need to be extended  [77]. Existing data must either be transformed in a semantically-aware manner to conform to such standards, or software that is aware of the semantic heterogeneity must work with multiple standards. Beyond sharing and Re-use: the problem of scale 19. It should be straightforward to assemble a dataset on biodiversity and reach conclusions by linking available information. To understand and model processes, such as the phosphate cycle, requires information at the molecular level over seconds (solubility, diffusion and uptake), kilometre level over years (transportation and availability) and planetary level over geological time (mineral formation, extractability).
Hardware associated with sensor and data logging is addressed in Appendix 2. Like other domains, biodiversity informatics will require robustness, stability and persistence, so will likely rely on key institutions with long-term funding. Over the core hardware infrastructure lies a spatial data information infrastructure, the biodiversity component of which is largely the topic of this white paper. The leveraging of information from distinct but adjacent domains will be increasingly necessary in the future, such as digital literature resources, image, environmental and climatic information databases. As molecular methods find ever greater uptake, one particular set of resources will become increasingly important to biodiversity informatics: these are the many biomolecular resources that, within Europe, lie within the purview of the ELIXIR infrastructure  [80].
Section 3: new tools How much of the legacy collections can be digitised? 22. The world's biological collections represent the hard core of biodiversity information. All other uses, from identification and naming onwards, are anchored in them. The collections contain an estimated 2-3 billion specimens but less than 10% have been catalogued in databases and much less captured as digital images  [81, 82]. This means that more than 90% of the collections are essentially unavailable for use through the Internet. Manually digitising collections represents an effort estimated at up to one million person years, but, with today's mass-digitisation methodologies, the task is feasible. As shown by multiple virtual herbarium projects  [83, 84] , the process can be partly automated through imaging techniques.
Developments in mobile communications offer numerous opportunities for innovation (see Appendix 2). Smartphones and tablet PCs with on-board GPS location can be easily taken into the field, creating opportunities both for innovative data collection and user information services. They are also particularly innovative for reference products such as identification keys o. Apps like these can be used to generate image-vouchered, location-tagged observations uploaded to central databases p. Performing science in large virtual communities, where participants have varied levels of expertise requires new techniques for data harvesting, processing, cleansing and validation. How do you find the data you need? 25. Most biodiversity data that now exists are semistructured and can be searched with typical search tools (Google, GBIF, etc.
). However, these are often designed for use by humans rather than for automated data retrieval tasks and may have in-built limitations or constraints. To make better use of general purpose tools, users may need to use more specialised resources as well. GBIF, for example, supports retrieval by species name but the user may also need to use resources such as the Catalogue of Life to provide alternative names for species-based searching. The volume of data now being searched is so large that it is often not possible to refine keyword-type searches sufficiently to recover the needle buried in the haystack, especially in the absence of widely-used vocabularies. Contextualising information (establishing relationships between data elements) in a resource is possible q , but currently difficult and slow.
The implementation of PIDs (see  Paragraph 7)  would make the construction of metadata portals much easier. A mature search mechanism that contextualises rather than simply indexing would be far more powerful. A number of newly developed techniques exist, and some are under development, that make extensive use of visualisation methods to detect patterns and issues in data collections. These could be useful for quality and fitness-for-use assessment, especially in very large datasets such as the LTER-Europe data index or the GBIF index and taxonomical nomenclators. Data publishers need to go further in helping users find the data that match their requirements, with the use of PIDs, vocabularies and KOS (see paragraph 17). How do you extract the data you need? 26. In publications, either paper or PDF, information is often embedded in text blocks or tables in a way that inhibits its re-use.
A general VRE for wetland studies can be customised to a specific geographical area and populated with relevant datasets. A VRE specialised for a single scientific objective e. g. to find an optimal way of sequestering carbon in a forest would be equipped with workflows based on highly specialised simulation tools such as Biome-BGC  [97]. For a successful uptake of VREs, they must generate immediate benefit for their users. For casual users the interface(s) must be simple and intuitive. For developers, there must be a usable pool of services and other resources that can be linked simply (e. g. BioVeL [98] ). VREs must perform functions that people find useful. VREs as envisaged here, also act as social networking applications and have a central role in making some of the available technology described above usable or better, invisible to the majority of users.
It may be possible that international organisations, such as GBIF, or large national institutions, such as natural history museums, will agree to underwrite data services on a care-and-maintenance basis, while the underlying software goes open source but with institutional oversight. Ultimately, to build the crucial user confidence, service managers need to invest far more than they have thus far in marketing to create new social norms. Who owns what? 32. The traditional citation system provides a mechanism to measure impact and provenance but it applies to a publication unit rather than code from a software library or data from a repository. Systems are required that will generate comparable metrics from the new open science resources (see paragraph 15). Data contributors would benefit by knowing the number of people and/or projects who used those data (impact).
Environmental metagenomics "Grind and find" techniques allows the study of many organisms in a sample at the same time, presenting the challenge of scaling biodiversity observation from the molecule to the planet  [107]. For example in November 2011, the Beijing Genomics Institute (BGI) launched its "Three Million Genomes Project", an ambitious effort consisting of three sub-projects: "Million Plant and Animal Genomes Project", "Million Human Genomes Project" and "Million Micro-Ecosystem Project". In the latter, genomes of more than 600 microbial species, including over 3,500 strains and 1,800 metagenomes have already been completed. Projects like these are generating in the order of 20 petabytes of data per year. With the unlimited influx of sequence data being a real possibility, archives operating under the INSDC (International Nucleotide Sequence Database Collaboration) face a new situation in which it is no longer possible to archive all components of all datasets.
Indeed, a community discussion is underway around decision-making that is informed by scientific and economic arguments about the aggressiveness to which different classes of sequence data should be compressed  [108]. Given the value of samples from temporal environmental genomic studies with time-point specific elements (e. g. Ocean Sampling Day 2014 http://www. microb3. eu/ work-packages/wp2) and limited or no opportunity to resample, contributions to the sequence compression debate from the biodiversity informatics community are essential.
Recall= RIS PRI We consider a community of ten users (students) where a sample is represented in the folowing. Travel details are extracted from the web portal www. transilien. com. Every user performs six requests (queries). For each query, the traveler estimates their preferred itineraries. Then we cite the retrieved solution by the system as well as relative precision and recall values. Next we define the selected solution and new preferences weights calculated based on the proposed learning process. All this data are collected in Table1. Fig. 3  shows that the users request number affects the system prediction. In fact, when a request number increases, the precision increases. This may be explained by the fact that when the user performs a certain number of requests, the system starts to learn better the user' preferences and to retrieve only pertinent solutions.
It is worth noting that Eq. (  4 ) differs from the conventional EXAFS equation (1) 1 in the fact that the exponential factor containing the DW has been replaced by a sum over a high number of MD structural arrangements (snapshots) which account for the thermal and structural disorder. This change has fundamental implications because the need of a model or a reference structure disappears when a simulated spectrum is computed. 22 In this study, a N MD value of 250 has been used. Snapshots were extracted from the CP-MD simulation of EDO-Pt in water already published. A higher number of snapshots (500) was used to check that no observable changes appear into the simulated spectrum. EXAFS calculations were performed with the FEFF8 code (version 8. 4). 23 The Hedin-Lundqvist exchangecorrelation potential was used to compute the electron density distribution within a self-consistent-field (SCF) approach.
3-16 Å -1 k-range with a k 3weighting. The remaining scattering paths have low contributions to the EXAFS signal. In our previous study,  21  we have shown that the solvation structure could hardly be detected, only on the simulated spectrum, at a distance of 4. 14 Å from platinum. The solvent contribution is too low to be detected on our experimental structures. A new set of experimental data for oxaliplatin in solid and solution recorded on the same beamline under equivalent conditions should be carried out to undertake this question. Thus the water molecules are neglected in this study. In the second step, E is set to the value extracted from the first step. Due to implicit correlations among the different paths, in this second step, constraints were applied on distances, in order to limit the number of fitted distances to 3.
ACKNOWLEDGMENTS We would like to thank  Dr. Jacques Moscovici ,  Dr. Luca Olivi ,  Professor Gilberto Vlaic ,  Professor Adela Muñoz-Paez , and  Dr. Stéphanie Belin  for their contribution to data collection at ELETTRA,  ESRF , and SOLEIL. We would also thank  Professor Christophe Den Auwer  for useful discussions. The  European Community Research Infrastructure Action  under the  FP6 Structuring the European Research Area Program  (through the  Integrated Infrastructure Initiative integrating Activity on Synchrotron and Free Electron Laser Science)  and the  Spanish Ministerio de Cienca e Innovación  ( CTQ2011-25932 ) are gratefully acknowledged for financial support. E. C. B. acknowledges  Junta de Andalucía  for a postdoctoral fellowship ( P06-FQM-01484 ). We also acknowledge the  University of Paris -East Creteil  which funded part of this work by providing a visiting professor position to  Professor Enrique Sanchez Marcos.
Indwelling urethral catheters are periodically replaced about 30 days of use; to not interfere with current clinical practice, the policy of each centre (or the investigator criteria) for catheter replacement and removal is considered valid for this study. Blinding This study is not blinded and the patient and health staff will know whether the patient is allocated to intervention or control arms. Differences in the shape and color of urinary catheters made study blinding too complex. To prevent bias the primary outcome will be evaluated by a data adjudication committee blinded to the intervention. Randomization The random sequence for the two comparison groups is computer generated ensuring equal allocation ratio. Blocked stratification for study centre is used to ensure that the two groups do not differ between centres.
On-line central randomization ensures that intervention allocation is properly concealed. Due to the characteristics of the included patients, it would be possible that they still require the use of an indwelling urinary catheter after their participation in the study. The study allows the participants to be randomized more than once if all the inclusion and exclusion criteria are still valid. Between first and subsequent participation in the study, patients should use standard catheters. Data collection All data is collected through an on-line electronic data capture system (http://ensayoescale. com). Secure access to this on-line platform is restricted to each centre investigator and designed collaborators and ensured by individual passwords. The study on-line platform collects from each participant the inclusion and exclusion criteria, baseline clinical and demographic data, the allocated intervention (size, reference number, and whether the aseptic conditions are observed), any sign and symptom which may be suggestive of acute UTI, results from urine and/or blood analysis (if performed), antimicrobial susceptibility test (if performed), any treatment given, adverse events as well as the data and reason for urinary catheter replacement or removal.
Statistical analysis Primary efficacy analysis will be performed on the intent to treat population set, including those participants correctly randomized and willing to participate in the study. Per protocol analysis will also be performed including those participants correctly randomized, willing to participate and with no major protocol violations. A further exploration of the sensitivity will be performed with these two sets of population to check they lead to the same conclusions. Primary and secondary outcome analyses including dichotomous data will be analyzed using Chi-squared test (or Fisher's exact test if analysis sample size is small). Socio-demographic and baseline clinical data will be described providing mean and standard deviation (or median and range) and compared using ANOVA test (or Kruskall-Wallis test) depending whether the data fulfil the application requirements.
In addition a regression analysis depending on gender, lesion level, re-inclusion and indwelling urethral catheterization carriers, first-ever catheter or another significant or clinical relevant outcome will be considered. No interim analysis is planned. The level of significance will be the usual 5% (alpha = 0. 05), two-sided. All the analysis will be performed with SPSS (V19. 0). Power calculation: The sample size was calculated on the assumption that 24% of patients will suffer from an UTI related to urinary catheterization, and a relative reduction of 35% with the use of catheters with antiseptic silver alloy coating, which is considered clinically relevant. A total sample size of 742, 371 in each group (allowing for 5% attrition) have a minimum of 80% power to detect these differences between intervention groups.
The woody component along field edges often provides the only permanent elements of structural and biological diversity in landscapes that have lost much of their naturalness to agriculture and urban use. The main aims of this study were (i) to investigate how four hedge stand types, i. e. , systems of management distinguished on the basis of the natural and managed growth forms of trees and shrubs (low single-storied, high single-storied, two-storied and multi-storied), differ in their biometric parameters and (ii) to identify the primary management-related determinants of native woody species richness. The study used a large dataset of hedgerows (n=538) sampled in seven sites in Northern Italy. The four hedge stand types exhibited highly significant differences in size and biometric parameters.
The present paper introduces and validates, through a post-analysis of biometric differences among the groups, a criterion for classifying rural hedge stands based on the relative contribution of trees, coppice, pollards and shrubs to the hedgerow. Based on this criterion, primary management-related determinants of native woody species richness are identified. Specifically, a large dataset was used, including data from more than 500 hedges located in the Padua-Venetian intensive peri-urban agricultural landscapes. The use of this shorthand recording system of hedge stand types, which incorporates biometric values and woody species growth form categories, increases the applicability of the existing hedgerow structure classification scheme to planning and management.
Data analysis Differences in biometric parameters between hedge stand types were tested using a one-way ANOVA. When model residuals deviated from the normal distribution, response variables were log-or square roottransformed before analysis. To test the effects of area, hedge stand type and basal area on the number of native woody species present, a linear mixed model was applied. The model included the variables hedge area, stand types and basal area, their interactions as fixed effects, and site as SH =-∑ i=1 n p i ln ( p i ) GI = 1 2 X n(n-1) ∑ i=1 n (2i-n+1) X i a random effect to account for spatial nestedness in sampling. The potential interaction between hedge area and stand type was examined to determine whether hedge stand type affects the slope of the species-area curve. The potential interaction between hedge stand type and basal area was also examined to determine whether the effect of increasing basal area on native woody species richness varies with hedge stand type.
Acknowledgements We are grateful to  A. Berardo  (Nogara),  C. Bernardi  (Roncade),  E. Cantarello  ( Frassinelli and Peseggia ),  L. Milan  (Canda), and  A. Rizzi  ( Montegalda and Piove di Sacco ) who performed field sampling in the sites indicated in parentheses. We thank  D. McCollin  for his useful comments and constructive suggestions on earlier versions of this manuscript. TS and GT are also grateful to  F. Viola  for his valuable guidance and expertise during their academic work. The present research was partially supported with funds provided by the  University of Padova  (ex 60% 2009 project code  60A08-4001/09  " Analisi ecologica dei sistemi arborei della pianura Padana orientale ") and by the municipality of  Piove di Sacco.
We collected information on sub-groups who cook and eat together, residential arrangements and sources of revenue within the household and some financial and support flows to and from other households. These data were recorded on household grids, but with many supplementary observations and qualitative comments. Ethical review of this project was conducted by the lead author's institution (UCL). Details of the methods used are available at www. householdsurvey. info. Each method generates a different perspective on survey and census data. Key informant interviews provide information on the different country-level priorities in terms of selecting which aspect of the household definition is most important, in cases where sleeping under one roof, eating together and authority of a head of household do not necessarily encompass the same individuals.
Furthermore, they highlight situations in which different players in the data collection process prioritise different criteria. These interviews also indicate areas where, within one country -or even within one institution -there may be different understandings of key demographic concepts which develop out of individuals' training, their personal experience or the languages they speak. They suggest different ways in which unfamiliar concepts might be approached in diverse contexts and, by drawing on respondents' personal experiences, present a diversity of context-specific approaches to matching demographic questions and concepts with local realities. We also systematically investigated different types of people, populations and situations which respondents had found to be problematic in their own personal experience of collecting, analysing or using demographic data.
In each country we interviewed a number of survey enumerators and several supervisors and demographic analysts in both academia and National Statistics Offices who had also worked as data collectors earlier in their careers. The nature of our research project meant that we deliberately targeted experienced interviewers who had worked on a range of data collection exercises, because we wanted them to reflect on their experience in different contexts and using different definitions. A consequence of this is a selection bias: most of our respondents were both good at their job and enjoyed the challenges it posed; they had therefore developed skills which enabled them to obtain good and coherent data from their respondents. Two interviewers (one in East and one in West Africa) had much less experience, and in both cases they struggled to understand our questions about ambiguous definitions or concepts.