dataesr/scientific-paragraphs-categorization huggingface dataset:
Paleozoic glaciated margins A trio of papers by  Melvin (2018) ,  Tofaif et al. (2018)  and  Hirst & Khatatneh (2018)  provide detailed insights into the Late Ordovician glacial shelf and margin record of Arabia. These papers provide much needed data on the Arabian plate because most (although not all) research in the last 15-20 years has focused on the extensively exposed (and until recently comparatively easy of access) Saharan outcrops. Melvin (2018)  provides a detailed and thorough analysis of deformed, diamictite-rich strata, which are extensively exposed over wide areas of the Arabian Peninsula, both at outcrop in Saudi Arabia and in core datasets acquired in both hydrocarbon and water exploration. This paper characterizes the suite of subglacial deformation structures that developed as ice sheets advanced to the palaeoshelf margin beyond the present Persian Gulf, providing criteria for their recognition.
Acknowledgements The editors especially wish to thank  Angharad Hills  for her seemingly unending patience with this project, and are very grateful for the assistance of  Sarah Gibbs ,  Rachael Kriefman  and  Bethan Phillips  in guiding us through the latter stages of the publication process. We extend our thanks to all the reviewers who made this volume possible, namely,  Don Aldiss ,  Fred Kamona ,  Julien Moreau ,  Jean-François Ghienne ,  Jean-François Buoncristiani ,  Flavia Girard ,  Sarah Greenwood ,  Edward Fleming ,  John Isbell ,  Nicolas Beukes ,  Carlota Escutia ,  Dominic Hodgson ,  Colm Ó Cofaigh ,  Denise Ruther ,  Philip Hirst ,  Jörg Lang ,  Richard Dixon  and  Donald Aldiss , together with a large number of anonymous reviewers to whom we are equally thankful. All of these people gave up a large amount of their time to improve the quality of the submitted papers.
While RCTs are the gold-standard to evaluate new therapies, they are often restricted to highly selected populations and may not reflect daily practice. 27 In contrast, registries are poorly suited to determine treatment efficacy or safety but provide important data on large number of patients from routine clinical practice in terms of describing patient characteristics, management and outcomes and identifying gaps between evidence and practice. Guidelines on secondary prevention of chronic patients are available, but largely rely on relatively old data pertaining to trials done in MI survivors. 28, 29 ] [33]  Despite the size, scope, and quality of the CLARIFY registry, this analysis has several limitations, some of which are inherent to observational studies. Outcomes were not adjudicated and, given the difficulty in ascribing death to a specific cause in long-term outpatient studies, the incidence of cardiovascular mortality, in contrast to total mortality, should be taken with caution.
Instrumentation Pre-treatment of the aptamer (heating at 90 • C for 8 min followed by immediate cooling to 4 • C for 4 min then 15 min at RT) was done on thermocycler mastercycler (Eppendorf, Le pecq, France). All the electrochemical measurements were performed on an Electrochemical Workstation "Biologics" equipped with EC-Lab software. Lyophilized aptamer was diluted in phosphate binding buffer (PBB 50 mM). PBB was composed of 2 mM KH 2 PO 4 , 8mM Na 2 HPO 4 , 135 mM KCl, 60 mM NaCl, and 5 mM MgCl 2 in deionized water. The pH of the buffer was adjusted to 7. 4.
Impedimetric Measurements Impedance measurements were obtained at an applied potential of 100 mV (vs. Ag/AgCl reference electrode), within a frequency range from 10 KHz to 0. 5 Hz, an AC amplitude of 10 mV, and a sampling rate of 50 points. All measurements were performed in PBB containing a redox probe (1:1 mixture of 2 mM K 3 [Fe(CN) 6 ]/K 4 [Fe(CN) 6 ]). Impedance data was registered after each modification step of electrode surface in the following order: (i) bare electrode; (ii) diazonium salt deposition; (iii) carboxy-amine PEG immobilization; (iv) aptamer immobilization steps; and (v) PAT detection. The impedance spectra were presented as Nyquist plots, (-Z im vs. Z re ) and fitted to the Randles equivalent circuit model, as shown in Figure  S1 , with EC Lab software. The parameter R s, corresponds to the solution resistance.
R et is the resistance in electron transfer between the electrode surface and the redox marker, whereas C is the double-layer capacitance and W (Warburg parameter) is associated with the diffusion of the redox probe. For all fittings, performed on EC-Lab software, the chi-square goodness-of-fit test was carefully checked to verify calculations (with 80. 73 at a 92% confidence level). In this work, we focused on the variation in electron transfer resistance (R et ). In order to minimize the electrode to electrode variations and to obtain reproducible and independent results, relative and normalized signals were required. Thus, the ∆ ratio parameter was calculated for all the electrodes. ∆ ratio is the ratio of R et of the bare aptasensor to the R et of the aptasensor with a particular amount of analyte.
Acknowledgments:  Akhtar Hayat  would like to acknowledge the project;  HEC (Higher Education Commission of Pakistan)-Technology Development Program (TDF)  project; No. 028 ,  2017. S. B. A. acknowledges the  University of Tunis El Manar (Tunisia ) for the mobility grant " Bourse d' alternance", as well as the  Tunisian Ministry of Higher Education and Scientific Research for LR99ES15 Lab funding.
This article explores a dark side of the current enthusiasm for compiling large datasets in support of evidence-based conservation, at various scales. We use a series of concrete examples to show how data gathered in biodiversity databases can be poorly informative for the design and implementation of effective conservation strategies and actions. This is due to two mechanisms. The first is the use of ill-advised formats, compelling contributors to fill in data which are at odds with the ecological information that they are truly able to capture. The second corresponds to the fact that, unless knowledge gaps are explicitly and emphatically highlighted, the elaboration of databases bestows visibility on knowledge and invisibility on knowledge gaps. Given these risks, we call for a cultural shift among conservation practitioners, consultants and others, to embrace the idea that documenting and acknowledging knowledge gaps and uncertainties is just as important as compiling data and taking known information into account.
Introduction Fifteen years ago,  Sutherland et al. (2004)  published a manifesto calling for a revolution in conservation decision-making. They argued that little evidence on the effects of conservation actions was collected, let alone compiled in databases providing feedback to others, and that most conservation decisions were consequently based on "myths". The latter could refer either to ideas originating from purely theoretical reasoning rather than from empirical evidence (echoing the classical philosophical opposition between idealism and empiricism), or more prosaically to beliefs enshrined in conservationists' minds, based on common sense or on anecdotal evidence. Sutherland et al. (2004)  advocated that conservationists should set-up databases gathering information on conservation practices and base their decisions on the evidence such databases contain.
Witnessing the success of this approach in environmental policies over the past 15 years, there has been a proliferation of publications dedicated to evidence-based conservation (e. g. Dicks et al. , 2014; Matzek et al. , 2014 , and even a new journal, Conservation Evidence), but also of databases aimed at gathering conservation evidence (www. conservationevidence. com, www. environmentalevidence. org) and, more broadly, environmental data, such as the databases of the  IUCN Red List (www. iucnredlist. org) Although many such conservation databases provide valuable support to conservation policies  (Hoffmann et al. , 2008) , they have been criticized on several counts, mainly by researchers concerned with improving them  (Boitani et al. , 2015; Troudet et al. , 2017; Trull et al. , 2018). An important issue are the numerous biases plaguing these databases  (Yang et al.
, 2013; Beck et al. , 2014; Meyer et al. , 2015; McRae et al. , 2017; Proença et al. , 2017). Many of the databases focused on species ecology, distribution and conservation status have gaps in species coverage (global IUCN Red List, LPI; GBIF) or uneven data coverage among species (PanTHERIA, FishBase; GBIF), which makes them highly biased towards developed countries, better studied species groups, charismatic and economically valuable species  (Troudet et al. , 2017). For example, although it covers most of the species in some groups such as birds, the global IUCN Red List contains information on less than 50% of fish species (16,803 species in IUCN Red List as compared to 34,100 species in FishBase, or 35,604 species in the Eschmeyer's Catalog of  Fishes; Fricke et al. , 2019) , and those included are highly biased in ways described above.
, 2017). Despite these limitations, databases such as the IUCN Red List or KBA are central to many conservation decisions, including those made by the private sector  (Bennun et al. , 2018). Together with other databases, they form the basis for biodiversity risk screening by investors, either directly (e. g. through IBAT -https://www. ibat-alliance. org/) or through ad-hoc software such as LEFT  (Long et al. , 2018)  or TESSA  (Peh et al. , 2013)  which compile and analyze global datasets. In this article, we explore a dark side of this enthusiasm for data gathering, which has been neglected by the above literature. Based on a review of the academic and grey literature, our own experience as practitioners and discussions with other practitioners, we show that, in some cases, important information is lost because of the chosen format and an impression of knowledge ends up hiding knowledge gaps.
Procrustean beds: creating uninformative data through ill-advised formats A first mechanism is the use of poorly designed formats, compelling contributors to fill in data which are at odds with the ecological information that they are truly able to capture. We term such cases "Procrustean beds", echoing the Greek mythological figure of Procrustes, a rogue who used to stretch people or cut their legs off to make them fit a bed. In the policies we analyze, experts' insights are stretched and cut off to make them fit in a database. A first illustration is given by the GWERN software (hwww. forum-zones-humides. org), which is now used to standardize wetland mapping across France. Using the software and database is a precondition imposed by several public agencies to finance conservation actions targeting wetlands.
Experts using this software have to assess if the various wetlands perform "hydraulic functions", such as "natural regulation of flooding events" or "protection against erosion", "biological functions", such as "carbon storage", among numerous other functions, and if these functions are performed to a "low", "middle", "high" or "non-significant" degree. Due to the imposed format, uncertainties are silenced and reporting the reason for choosing one answer over another is optional. Rigorously filling out the information would require measurements and investigations that experts in the field are never in a position to perform. The result of the generalization of this software is that a large amount of data on wetlands is now organized as a database with a coherent structure, which is certainly positive, but its relevance for evidencebased conservation action is questionable.
Similar (albeit less formalized) approaches are implemented in other countries, such as Portugal (C. Pinto Gomez, personal communication) and Switzerland (R. Delarze, personal communication). The Standard Data Forms (SDF) used to collect information on Natura 2000 sites (http://ec. europa. eu/environment/nature/natura2000/sites/index_en. htm), similarly illustrate the problems associated with databases asking experts to report synthetic assessments rather than concrete data. Experts are required to provide synthetic assessments of the "degree of conservation" of the "structure", "function" and "restoration possibility" of habitats. Such parameters are not directly observable. They can be obtained by aggregating information observable in the field and through GIS, or through expert judgments.
The methodologies used to choose and compile these information are not standardized  (Tsiripidis 2018) , and SDF do not ask countries to report information on the observable data used to compute them (if any). As a consequence, users do not have access to the concrete ecological parameters measured and there is an incentive for Member States to use cheaper, unsupported expert assessments. The difficulty of capturing meaningful ecological information is similarly trivialized in some usages of the EUNIS habitat classification across Europe. EUNIS is based on different habitat typologies, which were combined and structured through series of expert meetings  (Rodwell et al. 2018). Habitat typologies are a prerequisite to prioritizing habitat types for protection, and our point here is not to discuss their scientific credentials.
Empty boxes: creating the appearance of knowledge A second mechanism is the construction of large-scale databases which are largely empty. Our point here is not to denounce knowledge-gaps in such databases. Instead, our point is that, given the existence of such knowledge-gaps, if the latter are not emphatically highlighted, gathering information in a database tends to render knowledge visible and knowledge-gaps invisible. The European Union Natura 2000 database (https://www. eea. europa. eu/data-andmaps/data/natura-9) which gathers the information contained in SDF, illustrates that the existence of the database itself creates an illusion of knowledge. The database contains a total of 399 210 assessments for species and 148 444 for habitats. However, the data are reported to be of "good" quality for less than 42% of habitats, and less than 20% of species.
In poorly-known groups such as invertebrates, only 12. 3% are of "good" quality. Besides, the information given on the quality of data is based on vague categories that can be variously interpreted: 'Good' is defined as "based on surveys"; 'Moderate' as "based on partial data with some extrapolation"; 'Poor' as "rough estimation". No references or even explanations are provided to support quality assessments. The database elaborated as a part of the periodical European-wide evaluation of the Natura 2000 policy for 2007-2012 (https://www. eea. europa. eu/data-and-maps/data/article-17database-habitats-directive-92-43-eec-1/article-17-database-zipped-ms-access-format/article-17database-zipped-ms-access-format), illustrates a similar problem. Among the 3117 evaluations of "structure and function" of habitats, only 359 are based on "Complete survey or a statistically robust estimate", and it is unclear how to distinguish, at a technical level, between a "Complete survey or a statistically robust estimate" and an "Estimate based on partial data with some extrapolation and/or modelling".
The database does contain a field where "published data sources" should be reported, but this is an unstructured field gathering methodological reports concerning the various aspects of the evaluation, multiple data sets and even historical surveys of indirect relevance to the assessment. Another database provides an even more striking illustration: the French database of areas of particular conservation interest (ZNIEFF: Zones Naturelles d'Intérêt Ecologique Faunistique et Floristique: inpn. mnhn. fr/programme/inventaire-znieff/presentation), analogous to "Key Biodiversity Areas". ZNIEFF sites are largely seen as a nation-wide reference and are routinely used to elaborate and spatialize conservation strategies such as sub-national "green and blue vein" ecological corridors  (Amsallem et al.
, 2010)  or "no net loss" goals applicable to development projects  (Quétier et al. , 2014). The ZNIEFF database provides species and habitat data for 18 185 sites and an assessment of the state of knowledge about various groups for each site. For the two best known groups (mammals and birds), that knowledge is considered "good" for only 2. 5% of the sites (N=462). Unsurprisingly, the figures are even lower for less popular groups: 0. 1% for mosses (21 sites) and 0. 07% for non-insect invertebrates (12 sites). Outside Europe, the REBIOMA database (http://data. rebioma. net), which includes an index of current and likely future habitat suitability for a large number of species in Madagascar, gives the impression of there being a lot of adequate and useful data on species occurrence for the purpose of conservation planning.
MaxEnt (a software predicting species niches and distribution through maximum entropy modeling) is used to produce continuous distribution models based on occurrence records and an apparently generic set of relatively coarse-scale environmental data layers. While this may result in useful outputs for many taxa, especially well-surveyed ones, the data for many others appears inadequate for the purpose. For example, the habitat suitability models for Asteropeia rhopaloides and Sanzinia madagascariensis respectively appear to be based on 4 and 6 records only, yet they are used to map habitat suitability at the national scale. Our point is not to recall the trivial fact that habitat niche models can be misused when input data are insufficient, but to emphasize that gathering the results of such models in a database can give the illusion that a good level of knowledge on current and future distributions is actually available.
Although the known records and models are accessible for each species, these are not made obvious to a user who is only looking for the maps, which are also not clearly marked with any uncertainty or reliability factor, based on the underlying data This makes it difficult to distinguish between robust outputs and those that are less reliable. Moreover, databases aimed at supporting conservation policy and management also often contain pooled, aggregated data, where detailed information is lost. This is illustrated by the species distribution data in DAISIE, which is in most cases provided only at the country-level, as presence/absence. It lacks information on data reliability, species coverage, differences in data availability among countries and regions, and often the identity and experience of assessors.
Data in databases are in some cases accompanied with references to source publications. This may give the impression that the problem of uncertainty and data-gaps is accounted for, but this is not necessarily true in practice as sources cannot be readily checked for reliability and uncertainty, especially for large databases comprising thousands or millions of data and related sources. In such cases, the data that can be used to account for uncertainty is essentially provided, but the dataset size will render their use impossible. This problem is present in most of the existing databases used for conservation decision-making, including those discussed previously in the text. For such information on data sources to be usable, it would have to be extracted and provided to users in a ready-to-use format, with all the ancillary information, including the estimate of data reliability and uncertainty.
In some cases, relevant and detailed data can be included in the database, but not made freely available to users. For example, IUCN Red List assessments are commonly based on input by multiple species experts ("contributors"), ranging from evidence-based estimates to basic ruleof-thumb approaches, which are in turn collated and aggregated by one or several expert coordinators ("assessors") and, together with data from the available literature, used to estimate species status and trends. However, the detailed data used for assessments is not made available to the users, who are provided only with the final outcome of the analysis and a general overview. Detailed information on the contributors and the data used for estimates is stored in the internal database that is generally off limits to general users.
The way forward: facing the scarcity of conservation information The various databases mentioned above play important roles in environmental policies and conservation at various scales. In spite of the problems unveiled above, they are still very valuable in providing current available knowledge to conservation science and practice. Our point is neither to discredit these initiatives, nor to argue that they should be discarded. We rather aim to suggest avenues to address the risks we have highlighted. Valuable progress could be achieved by improving the process through which data formats are produced and integrated into public procurement rules, in particular by including experienced field experts in this process. In the case of wetland assessment, numerous methods based on field measurement are elaborated (e.
g. Gayet et al. , 2018a) and could compete with databases such as GWERN in the years to come. The UK's National vegetation survey methodology (http://jncc. defra. gov. uk/page-4258), which pursues objectives similar to EUNIS cartographies, illustrates how the requirement for the elaboration of vegetation maps that "each surveyor should be a competent botanist" (http://jncc. defra. gov. uk/page-2468, p. 14) is not unrealistic, even if not always effectively applied or sufficient (e. g. Wawrzyczek et al. , 2018). Similarly, although we criticized above the vagueness of data quality assessments in Natura 2000 and Article 17 databases, they are a step in the right direction, and fact sheets of European Red Lists of Habitats (https://forum. eionet. europa. eu/european-red-list-habitats/library/index_html) illustrate progress in this respect.
Involving experts in ecology and computer sciences in the design of databases is needed to eliminate Procrustean beds. It is, however, insufficient to address empty boxes. Indeed, the ZNIEFF database, for example, was produced through an extensive consultation involving hundreds of experts  (Elissalde-Videment et al. , 2007). A cultural shift is also needed among conservation practitioners, consultants and others, to embrace the idea that documenting and acknowledging knowledge gaps and uncertainties is just Such a shift is currently hindered by the fact that the majority of existing databases cannot be fully understood given the lack of direct links between the assessments and the data sources and raw information they are based on. While such a comprehensive database structure would probably be challenging to implement, an alternative would be to provide ancillary information that would reflect quality and characteristics of data sources, such as reliability, value ranges, confidence intervals, uncertainty, etc.
While there is a certain unavoidable level of trade-off between the ecological quality and interoperability of data, efforts should be made to ensure that proper integration, harmonization and interoperability does not compromise ecological meaning of the stored information, and that metadata management and standardization also reflect ecological meaning  (Kissling et al. , 2018). There is a growing number of promising initiatives focused on these issues, such as Darwin Core and Humboldt Core, the two standards for publishing and integrating biodiversity data and inventories  (Wieczorek et al. , 2012; Guralnick et al. , 2018) , and Ecological Metadata Language, a widely used metadata standard  (Michener, 2006). Another important improvement would be to use differentiated data sharing policies, as initiated by Botanical Conservatories in France for other purposes.