dataesr/scientific-paragraphs-categorization huggingface dataset:
Averaging these densities over all cars, we obtained a function giving the probability density of presence of a car at each point in the department. After integration of this probability density function over a given SAR within a department, we could estimate the proportion of time spent by the cars of the department in the SAR. We could finally estimate the number of kilometers traveled by the cars of a department in a SAR by multiplying the total number of kilometers T d with this proportion. To identify the best measure of effort, we have compared the predictive efficiency of our model when the sampling effort of the dead was measured either with V ð1Þ j or with V ð2Þ j. We used a cross-validation approach: we have considered separately the even years  (2002, 2004)  and the odd years (2003, 2005) of our study period; for each measure, we fitted a model using the data collected during the even years (using for each one the optimal value of ν, selected using the approach described in the previous section) and we measured the prediction error on the odd years, and conversely.
Results We fitted the penalized log-linear model developed in the previous section on the complete dataset, including the data concerning the common genet and the wildcat. Indeed, even if these two species were not of interest to us, including these data in the fit added precision to the estimation of the effort e j2 (these detections actually proved the presence of the officers at those places). We first compared the two possible measures of sampling effort using cross-validation. The negative log-likelihood of the dataset estimated with cross-validation was much higher for the measure V ð2Þ j (Q 2 = 5468) than for the measure V ð1Þ j (Q 1 = 5240), indicating that the latter was characterized by the best predictive efficiency (see S1 Appendix, section 6, for further details). This result may be explained by the fact that the main role of the national wildlife protection officers is to ensure that the enforcement of the laws pertaining to the hunting and trapping of wild animals is as uniform as possible over a department, which suggests that their frequency of use of the SARs during the study period was proportional to their area.
Further studies are needed to account for this type of error in our model. One possible approach would be to model the probability of identification error using a dataset collected by a random sample of officers of the ONCFS with a double observer approach  [27]. We could then fit our approach in a Bayesian framework, accounting for the uncertainty caused by identification errors using an informative prior distribution derived from this model of identification errors. In this study, we supposed that the sampling effort of dead animals was proportional to the number of kilometers travelled by the officers. However, this is a very rough approximation of the actual effort, and it would be interesting to model this effort more precisely in a further study. Indeed, this sampling effort can be seen as the probability that a given animal a in a given region was detected as a dead animal by an officer.
Note that we provide both the data and the functions required for this fit in the companion R package scsl (see S1 Package), so that the interested reader can try this "spatio-temporal" approach on our data. Note that if a monitoring of the relative density over time is carried out with this approach, it is important to prevent a decrease in the officers' motivation with time (which would lead to an unmeasured decrease of the sampling effort, i. e. not strictly related to the number of kilometers). In our study, the data collected through the SCSL program are very simple data and quick to collect. They are useful to the officers themselves at the local scale (e. g. to build regional maps of distribution), which helps a lot to keep them motivated. We have also tried to motivate people through annual reports and regular communications, which helps them stay interested and updated in the project.
Finally, our modeling approach in itself has interesting implications. Indeed, this approach makes it possible to combine two datasets collected on the same population: (i) one dataset focusing on the dead animals collected with a supposed known sampling effort, and (ii) one dataset collected on the living animals with an unknown sampling effort. This ability of our modeling approach to combine information collected with different methods on the same population has interesting implications for the analysis of data collected in citizen sciences programs  [18]. Indeed, our modeling framework could indeed be used to combine (i) a dataset collected using a known sampling design generally conceived by a scientific team and (ii) an opportunistic dataset obtained by crowdsourcing, collected by volunteers, generally without any knowledge of the sampling effort.
Our approach would allow us to estimate the sampling effort for the opportunistic dataset, and correct the biases generated by the uncontrolled and unknown effort in such data. Preliminary results suggest that this approach would allow us to obtain unbiased and precise estimates of the relative densities of the studied species (Giraud et al. in prep. ). This confirms the remark of MacKenzie et al. [36] : "in some situations, it may be appropriate to share or borrow information about population parameters for rare species from multiple data sources. The general concept is that by combining the data, where appropriate, more precise estimates of the parameters may be obtained. " Further work is now required to identify the conditions under which our approach could be used with data collected under citizen science programs.
Acknowledgments We warmly thank the members of the  CiSStats group  for stimulating and fruitful discussions on opportunistic data and related statistical issues. We are also grateful to  Nigel Gilles Yoccoz  and to an anonymous referee for their very useful comments, which greatly helped to improve this paper. This work was partially funded by the  Mastodons program  from  CNRS , by the  CiS-Stats program  from  INRA  and by the  Chaire de Modélisation Mathématiques et Biodiversité from VEOLIA-Ecole Polytechnique-MNHN. We also thank  Murielle Guinot-Ghestem  ( ONCFS ) for her comments on previous versions of the manuscript, and American Journal Experts for checking the English language.
In order to boost the identification of low-molecularweight drugs on protein-protein interactions (PPI), it is essential to properly collect and annotate experimental data about successful examples. This provides the scientific community with the necessary information to derive trends about privileged physicochemical properties and chemotypes that maximize the likelihood of promoting a given chemical probe to the most advanced stages of development. To this end we have developed iPPI-DB (freely accessible at http://www. ippidb. cdithem. fr), a database that contains the structure, some physicochemical characteristics, the pharmacological data and the profile of the PPI targets of several hundreds modulators of protein-protein interactions. iPPI-DB is accessible through a web application and can be queried according to two general approaches: using physicochemical/pharmacological criteria; or by chemical similarity to a user-defined structure input.
In both cases the results are displayed as a sortable and exportable datasheet with links to external databases such as Uniprot, PubMed. Furthermore each compound in the table has a link to an individual ID card that contains its physicochemical and pharmacological profile derived from iPPI-DB data. This includes information about its binding data, ligand and lipophilic efficiencies, location in the PPI chemical space, and importantly similarity with known drugs, and links to external databases like PubChem, and ChEMBL.
As opposed to most traditional targets, PPIs have not evolved to bind small molecules  (4). Indeed, the molecular topography of most known PPIs, often described as shallow, large and hydrophobic, makes them harder to tackle with small compounds and these features have often been translated in the design of larger and more hydrophobic modulators. In fact, such interfaces are now known to preferentially bind compounds that display some specific physicochemical characteristics and chemotypes  (5) (6) (7) ). Yet, analyzing further successful LMW PPI modulators should be essential to rationalize what makes those molecules so special and capable to bind to such intricate surfaces and thus assist the design of future generations of PPI inhibitors. Two databases already propose to access the structural and pharmacological data of existing successful examples of PPI modulators.
First, the TIMBAL (8) database proposes compounds that are automatically imported from ChEMBL (9) (https: //www. ebi. ac. uk/chembl/) following a manual selection of the PPI target type, it contains the data of about 8900 compounds on several PPI targets. Most of the data come from a large pool of integrins for which the target is not always clearly identified. Second, the 2P2I-db (  10 ) is a manually curated database from the PDB (11) (Protein Data Bank) that collects the crystallographic data of cocrystallized orthosteric PPI inhibitors. In the last version, it contains 242 compounds. In order to help the scientific community to gain new knowledge about LMW modulators of this new target class, we propose a database, named iPPI-DB, together with a user-friendly web interface (http://www.
Presentation of iPPI-DB iPPI-DB is a relational database containing the structure, the physicochemical characteristics, the pharmacological data (biochemical and/or cellular binding data) of compounds modulating known PPI targets as well as the profile of the corresponding targets. As those data are manually extracted from the literature and curated by experts, we set up a comprehensive protocol to decide whether or not a given compound should enter the database and thus such as to ensure assure the data quality. First, we consider only world patents and peer-reviewed articles from scientific journals with expertise in medicinal chemistry. Also considered, the PPI targets must have been discussed in several scientific publications with for instance links between biological studies previously published, therefore with some demonstrating the pertinence of the interactions and their contribution in a given disease state and more specifically in terms of functional mechanism.
At this time, a total of 2461 binding data have been collected on 1650 compounds across 13 families of highly homologous PPI targets. This represents a significantly higher number of accessible data than in the first version, which only provided access to a representative subset of 352 compounds. As described below, the interface now allows the user to perform a chemical similarity search on iPPI-DB, to restrict the search only to drug candidates, and to export all results, which was not available in the first version.
iPPI-DB webapps In order for the PPI scientific community to fully make use of the database, we have designed a new user-friendly web application that offers tools and predictive models as well as two approaches to query iPPI-DB. The first possibility to search the database is to use physicochemical and pharmacological criteria on a given target. The second possibility is to import an input molecule as the query compound and search by chemical similarity the entire collection regardless of their target type.
Querying iPPI-DB using physicochemical or pharmacological criteria In the first approach physicochemical and pharmacological criteria can be defined to search active compounds within iPPI-DB on a given PPI target. The user can refine the search by tuning several properties such as potency thresholds, physicochemical descriptors (e. g. molecular weight, hydrophobicity, Fsp3). As an alternative, the user can apply one of the three commonly used physicochemical rules namely Lipinski's rule of five (  16 ), Veber's (  17 ) and Pfizer's 3/75  (18)  in order to select only compounds that are predicted as orally bioavailable in humans, rats or as potentially less toxic, respectively. Finally, the user can choose to extract only drug candidates on the PPI targets that have some compounds presently tested in preclinical or clinical phases (annotated using the data MDDR march 2012).
The chemical search provides the user with all the binding data of the 20 closest iPPI-DB compounds to the input structure according to the chosen type of fingerprint and their Tanimoto index. It also recalls the structure of the query compound along with its compliance with respect to the three chemistry rules mentioned above (Figure  1 ). Example of a query on iPPI-DB using the mode 'Chemical Similarity'. As the query compound, the user can either copy and paste a SMILES string into the interface, or directly sketch the compound within a Marvin JS editor. Here, we show an example with the structure with a recently identified piperidinone-based inhibitor of the MDM2/p53 interaction  (25). Then, the compound can properly be imported, and the desired type of fingerprints for the chemical similarity search has to be chosen between ECFP4 and FCFP4.
The search provides the user with all the binding data (here 47) of the 20 most similar iPPI-DB compounds in a table that can be sorted according to various criteria: molecular descriptors, activity, efficiencies or bibliographic IDs. The interface also provides a summary of the properties of the input compound using a radar chart based on nine physicochemical properties (molecular weight-MW, hydrophobicity-AlogP, the number of H-bond donors-HBD and acceptors-HBA, Topological Polar Surface Area-TPSA, the number of rotatable bonds RB, the number of aromatic rings-Ar, the proportion of sp3 carbon atoms-Fsp3 and the number of chiral centers-R/S) and its compliance to the three chemistry rules, Lipinski's RO5, Veber's and Pfizer's 3/75.
Visualizing the results within a sortable and exportable datasheet The results of any query are displayed as a sortable and exportable datasheet that contains all the binding data found in iPPI-DB concerning the compounds fitting the query criteria. Each line matches a compound's binding data and recalls the iPPI-DB compound ID. Also displayed are a 2D representation of the compound, a radar chart summarizing the values of nine computed physicochemical descriptors and the activity, the PPI target bound by the compound (with a clickable link to the Uniprot  (19)  web server (http://www. uniprot. org)), the name of the assay (with a mouse over to have its full name) and whether it is a cellular assay, the type of activity that was derived from the assay (pIC 50 , pK i , etc. ), the potency of the compound to bind its target, the nine physicochemical descriptors, the ligand and the lipophilic efficiencies  (20) , and the iPPI-DB ID of the bibliographic source from which the data were retrieved.
Accessing all data of a given iPPI-DB compound through its ID card If the user is interested in a particular modulator, a click on its iPPI-DB ID within the results' datasheet will open an individual compound ID card where all data about the compound are summarized through four different tabs: compound summary, physicochemistry, pharmacology and drug similarity (Figure  2 ). In the compound summary tab, the chemical structure of the compound is recalled along with its canonical SMILES, its IUPAC (http://www. iupac. org) name, brand name if any, development phase if the compound is present in MDDR march 2012 version), external links to the compound within other databases such as PubChem  (21)  and ChEMBL if any, and a link to the Pubmed article or the Wipo patent with the name of the compound as defined in the bibliographic source to facilitate its identification within the document.
Finally, a principal component analysis individual map is provided to locate the position (in red) in the iPPI chemical space of the selected compound with respect to all iPPI-DB compounds (in gray) and all the other iPPI-DB compounds on the same target (in blue). In the pharmacology tab, all binding data available on the selected compound are given. A biplot also represents the lipophilic-and ligand-efficiencies of the selected compound (in red) with respect to the one of all iPPI-DB compounds (in gray) and to the one of all the other modulators available on the same target (in blue). Finally in the drug similarity tab, the chemical structure of the selected compounds is shown next to the five most similar drugs that are found in the MDDR database (march 2012 version). Along with their structures, the following data are provided about the MDDR drugs: the Tanimoto index (using FCFP4 fingerprint), the development phase and the activity class (e.
CONCLUSION Given the importance taken by protein-protein interactions in the last decade in the field of drug discovery, new tools and data collections are necessary to address the challenge raised by this intricate class of therapeutic targets. Indeed, one avenue to assist the identification of new chemical probes on PPI is to collect successful examples of such modulators and learn from these molecules. With the iPPI-DB initiative, we hope that the presence in one database of manually annotated data with freely accessible and intuitive tools to query them will boost drug discovery and chemical biology projects for this target class. Along the same line, big data analysis of such databases and of databases containing regular compounds should help to rationalize why some compounds are capable of modulating PPI interfaces  (22).
Validation of the algorithm in an animal model We tested our algorithm on data from an established animal heart model of repolarization heterogeneity which produced a region of shortened APD proximal to the stimulus site and prolonged APD distal to it (Figure  4 ). The preparation is summarised in the Supplemental Material  [9]. Activation times and repolarisation times for each beat were measured at every mapping electrode site and the RVI mapping algorithm (above) applied under conditions when re-entry did and did not occur. In the case where re-entry did not occur (panel A) activation spread downwards from the stimulus site (Ω) and blocked as it entered the region of prolonged refractoriness (white bar). The wavefront travelled round the region of block (as shown in Figure  3A  by curved white arrows) and arrived at the distal side while that region was still refractory, thus was unable to travel back through the line of block: there was subsequently no re-entry (Scenario as depicted in Figure  1B ).
Electrophysiological recordings Activation and repolarization times were computed using a semi-automated system which first identifies and discounts any cases where the T-wave is indistinct or corrupt, and then calculates the timing of activation and repolarization events using the Wyatt criteria  [10] , with further validity checks on relative magnitudes of key deflections in the electrograms. The algorithm, including error checking, is particularly designed for robust measures in the presence of noise; and has previously been described in detail (software was written using MATLAB R2010b, Mathworks, Inc Natick, MA, USA)  [15].
Superimposition of the RVI with scar, and circuit exit point is shown in Figure  8  Discussion We have developed an algorithm able to identify localized regions of high susceptibility to conduction block and re-entry using a novel parameter referred to as the re-entry vulnerability index (RVI). In an animal model of repolarization heterogeneity (mimicking the peri-infarct region) in which re-entry had been induced and mapped, the algorithm successfully identified the region of block and subsequent re-entry. We then applied this technique to a patient undergoing radiofrequency VT ablation in whom activation and repolarization times were measured from 200 electrode sites in the LV during pacing. From these data points the algorithm generated a global map of RVI which could be co-registered with the anatomy.
Acknowledgments Grants and Financial Support: The research was supported by the  Department of Health  via the  NIHR comprehensive Biomedical Research Centre  award to Guy's  & St Thomas' NHS Foundation Trust  in partnership with  King's College London and King's College Hospital NHS Foundation Trust , and by  The Centre of Excellence in Medical Engineering  funded by the  Wellcome Trust  and  EPSRC  ( WT088641/Z/09/Z ). BH and PT are supported by an  MRC grant  ( G0901819 ) and RC is supported by a grant from  Marató de   TV3. Industry related disclosures: NC is funded by an  Educational Fellowship Grant  and JG has an unrelated grant both from  St. Jude Medical.
Here SCIAMACHY ozone number density data are extracted from the ozone profile product of the operational algorithm (level 2 version 5. 02). The data retrieved in this version are most useful for altitudes between about 15 and 40 km because there is a reduced sensitivity to ozone above 40 km and below 20 km, leading to substantially increased retrieval errors at those altitudes (European Space  Agency, 2011 Agency, , 2013)). The data are accompanied by quality flags indicating the validity and quality of the retrieved product (European Space Agency, 2013). Initial validation results for version 5. 01 (for ozone profiles equivalent to version 5. 02) showed a positive bias in the tropics, especially below 20 km, a good agreement (within 5 %) in the mid-latitudes and a variable bias was observed for the polar regions, with larger deviations above 35 km  (European Space Agency, 2011; 2013).
Ground-based NDACC lidar data In this study we have used ozone profiles obtained by ground-based lidars that are part of the Network for the Detection of Atmospheric Composition Change (NDACC; http://www. ndacc. org; Kurylo and Solomon, 1990). To be-come associated with NDACC, it is obligatory to have a good description of the data quality through intercomparison of at least the retrieval software, followed by intercomparison with other instruments. The latter can be done with other instruments such as sondes, or with the NDACC travelling standard, the NASA GSFC (Goddard Space Flight Center) lidar. Ozone profiles are retrieved using differential absorption in sets of two wavelengths in the ultraviolet domain, where ozone is in each set only strongly absorbed at one of the two wavelengths. The difference of the slope of the logarithm of the retrieved lidar signals as a function of altitude is used to determine the ozone number density profile.
Data selection, collocation and preprocessing We selected all SCIAMACHY and lidar ozone number density data from the period 2002-2012 having a reported error of 30 % or less and having valid processing flags. Collocations of SCIAMACHY and ground-based lidar ozone profiles were sought within 20 h and 800 km and the number of collocations with each site is listed in Table  1. The profiles are interpolated to a common altitude grid with a 1 km resolution using a nearly linear spline, followed by the calculation of the relative differences with respect to the lidar as follows: SCIAMACHY-lidar lidar ×100 %. The resulting data set consists of over 25 000 difference profiles between the collocated pairs, together with metadata (i. e. EVs) providing information on the observation characteristics. These differences are assumed to be to the largest extent attributable to the satellite retrieval as the lidar data are of high and known quality (Sect.