dataesr/scientific-paragraphs-categorization huggingface dataset:
10 Citations are the most commonly used measure for visibility and impact of a journal. Over the years, many different indexes of citations have been created, which indicates that this is a very controversial topic. Although these indexes have limitations that have been reported in the literature  11  , the idea of counting citations remains valuable. However, the source of citation data for counting may be one of the more important limitations for these indexes, resulting in significant differences between them. 12 In our analysis, we identified massive differences between the databases that we used as sources for citation data. 4] [15]  The number of citations received increased during the study period, but the journal's indexation in PubMed Central in 2014 and subsequent inclusion in PubMed produced a significant increase in citations.
The incomplete coverage of some databases, such as Web of Science, in pharmacy practice becomes more evident when analyzing the journals that are cited in Pharmacy Practice more frequently. As expected, our analysis demonstrated that a journal more frequently cites journals in the same area of knowledge; the five most cited journals in 2015 were pharmacy journals. This means that the immediate consequence of missing references for Pharmacy Practice is a reduction in the Journal Impact Factor not only for other pharmacy journals but also for Pharmacy Practice itself. In a recent editorial, using data from the Web of Science, it became apparent that Pharmacy Practice should have appeared in the Journal Citation Reports with an Impact Factor of 0. 754. 16 If self-citations were counted, this value should rise to 0.
CONCLUSIONS This analysis of the first decade of articles published in Pharmacy Practice serves as a valuable benchmark for enhancing the quality of the journal going forward. During this decade, Pharmacy Practice was admitted to major databases, resulting in increased growth in terms of both visibility and impact. The editorial process duration increased with the implementation of a more rigorous reviewer selection process. International collaboration among authors is low. Some of these patterns and trends deserve further analysis to identify potential tendencies in the field of pharmacy practice that may result in weaknesses for all journals in the field.
In the framework of biological invasions, this tool makes it possible to not only model the ecological niche of pests or weeds in their native range and extrapolate to potential areas of risk  [8, 9] , but also to identify the geographical origin of an overlooked invasive species based on its introduced range  [10]. Species occurrence data are available in various forms. For plant species, herbarium collections and botanical literature have remained the main sources of plant distribution data for centuries. More recently, the development of online databases, which gather contributions not only from scientists but also from informed citizens  [11] , has allowed several countries to provide accurate gauges of species occurrence within their borders. On a broader scale, the Global Biodiversity Information Facility project (GBIF, www.
gbif. org) collects freely available distribution data worldwide. However, the accuracy of these occurrence data is heterogeneous. To begin with, the data generally suffer of a lack of completeness, particularly when it is sourced from developing countries  [12]. In addition, most online databases are not subject to thorough verification. As an example, Yesson et al. rejected 16% of the occurrence data for legume species included in the GBIF database  [13]. The erroneous data comprised such a high proportion of the total that, had it not been rejected, it would have been all but impossible to obtain a reliable niche estimate via SDM modelling. Besides, unresolved taxonomy and synonymous nomenclature add another layer of bias to species distribution data  [14]. For example, the analyse of 4500 specimens of ginger species across 40 international herbaria revealed that at least 58% of them were misidentified or not updated regarding recent taxonomy  [15].
To date, only two studies using this free source of data in biodiversity sciences are referenced in the Web of Knowledge database ('Google Street View' search, http://webofknowledge. com), both of which focus on animal species. In the first of them, the authors successfully identify the nesting habitats of two cliff-nesting vultures through 'virtual' prospection  [16]. More recently, cross-validation between field and virtual occurrence data of the pine processionary moth, Thaumetopoea pityocampa, has demonstrated the robustness of data collected using street view imagery (SVI)  [17]. Overall, these seminal works suggest that further applications of street view data should be possible from such a widely distributed source of information. Indeed, most of these pictures capture information on the surrounding vegetation, and the high quality of this imagery should allow the identification of many plant species and habitats.
SVI prospection method The proposed SVI prospection method was tested in mainland Spain, a region that fulfils the following criteria: (i) it hosts A. donax as a common species in a substantial part of the territory; (ii) it has well-maintained and reliable occurrence data from the Spanish botanical national database Anthos (www. anthos. es, Fundación Biodiversidad, Spain). A total of 505 geo-located records of A. donax occurrences dating back as far as 1839 were extracted from the Anthos database. The sampling strategy for the SVI prospection was based on a simple random design. The study area was divided into a grid of 5484 6-arc-min (i. e. 0. 1x0. 1 degree) cells using Quantum GIS 2. 4 (QGIS, www. qgis. org). Cells with >25% of their surface area in the sea or otherwise outside of Spain's borders were excluded from the analysis.
We randomly selected 20% of the cells (N = 1097) using the QGIS-function 'Random selection' for sampling by the SVI prospection method. Of the 505 field occurrences from the Anthos database we reduced to only 216 cells with field presence. By chance, ten of these happened to also be among the original set of 1097 previously selected cells. The remaining 206 cells with Anthos records were also prospected in order to evaluate the omission rate of SVI data. A repetitive procedure was conducted in order to standardize the SVI prospection for each cell. The procedure involves the following three steps (S1 Video). (i) Explore each cell eastward and southward, using aerial photographs scaled between 1:1x10 5  and 1:1x10 4 (c. 1-10 km a. s. l. ), to look for suitable areas for A. donax (e. g. human-disturbed rivers, farmlands and suburban zones) that include street view transects (e.
Assessment of SVI data and method reliability To evaluate the omission risks of SVI prospection, we started by evaluating the difference between data collected on SVI and data collected in field as a percentage of mismatch in the cells that showed field occurrence. Next, we tested the hypothesis that mismatches between SVI and field data are linked to the density of roads covered by SVI within each prospected cell: fewer roads may be found within non-congruent cells than within congruent ones. The Google SVI network is currently not available in GIS vector format. Consequently, we postulated that the exhaustive road network is closely correlated to the SVI network among continental Spain. We exported the continental Spain road network from OpenStreetMap Data (http://download. geofabrik. de/) to estimate the total length of road (in km) per cell.
Comparison of SVI and field data in SDM Distribution (probability of presence) of A. donax was assessed using a Generalised Linear Model (GLM), with a binomial distribution of error and a logit link  [19]. This method gathers several explanatory variables in a linear function in order to model a binary response ranging between 0 and 1. We compared the outcomes of SDMs built with either field data or SVI data. Three different datasets were used: (i) Anthos: 216 field presences from the Anthos database and 216 pseudo-absences data randomly selected among the 5268 remaining cells (with 1000 replicates), (ii) SVI sub : a subset of 216 presences and 216 absences randomly selected from the entire SVI dataset (with 1000 replicates), and (iii) SVI full : the complete SVI dataset. The first dataset can be thought of as the means to a fundamental assessment of SVI data, as it represents the basic model of species distribution that can be performed using data from a botanical database.
Because of its similar sample size, the second dataset allows a comparison of model efficiency between field data and SVI data. The third dataset illustrates the type of species distribution modelling that can be performed using SVI data, taking into account the effects of both data quality (as model 2) and quantity. We used a set of three synthetic climatic data as explanatory variables in the GLM. Climatic data were extracted from the Bioclim dataset, provided by WorldClim v. 1. 4 [20] , which includes nineteen climate variables in a GIS-based raster format. A principal components analysis (PCA) using values from the nineteen climate variables for each cell was performed to produce an uncorrelated set of three synthetic variables, hereafter referred to as PC1, PC2, and PC3 (the values of the three first principal components).
Results The 216 occurrences of A. donax derived from the field data were mainly located along the Mediterranean coast, from the coastline to about 100 km inland, with a deeper continental occurrence along the Ebro alluvial plain (NE Spain; Fig  2A ). Occurrences were also recorded in two Atlantic regions (Cantabria and Galicia), as well as other regions scattered sporadically over the greater inland area. An important part of the data was obviously located close to towns, especially between Valencia and Murcia. The SVI prospection collected 345 occurrences of A. donax among the 1097 sampled cells (31%; Fig  2B ). The species was not recorded, and therefore considered absent, from the 752 remaining prospected cells (Fig  2C ). On a coarse scale, species potential distributions obtained from these two sources of data were roughly similar.
On a finer scale, SVI provided relatively more data further inland, as well as a much denser potential distribution alongside the Ebro valley and in Andalusia. The SVI prospection detected A. donax in 81. 1% of the 216 cells that included field presences (Fig  2D ). The mismatches between SVI and field occurrences are mainly localized to inland cells, i. e. in regions where few occurrences have been recorded by both methods. The probability of matching between the two sources of data was positively influenced by the total length of roads (on a log scale) available in the cell (β = 1. 10 ± 0. 25; Fig  3 ). However, the proportion of deviance explained by this model remains small (11%), suggesting that other factors might be influencing the mismatches. Furthermore, median road length (on log scale) across all 5484 cells was 0.
13, a value for which the predicted matching probability was as high as 72%. Interestingly, the median road length (on log scale) for the cells with field data (Anthos database) was much greater (0. 83) than the one across all of Spain (median for SVI data: 0. 08; Fig  3 ). The proportion of deviance explained by models using SVI data was three times greater than that of models with field presence data and randomly selected pseudo-absences (ca. 33% vs. 11%; table  1 ). The three models retained the three synthetic bioclimatic variables as significant (effect size > 0). However, effect sizes (in absolute values) associated with bioclimatic variables were two to five times greater in SVI-derived models than in field-based ones. This discrepancy did not arise from a difference in the number of absence data used in the model.
Indeed, randomly selecting 216 presence and 216 absence data, so as to match the sample size of field-based data, provided results very similar to those obtained by considering the entire SVI dataset (Table  1 ). For all of the predictive variables that were considered, we found no overlap between the confidence intervals of the predicted slopes from SVI (SVI full or SVI sub ) and from field-data (Table  1 ), further suggesting that the model based on field data had greatly underestimated the effect of bioclimatic variables. Projection of models based on field data and SVI data produced a similar potential distribution across Spain (Fig  4 ), with highly suitable areas along the coasts, and moderately suitable areas along the Ebro valley and north and south of the Sierra Morena Mountains, up to Madrid.
However, the potential distribution model based on SVI data was more conservative. For example, the model based on field data predicted the arid and cold central Spain (in Castilla y Leon) and the Sierra Morena mountains to be poorly suitable, whereas the full SVI model predicted those areas to be unsuitable. The latter result appears more consistent with the known ecological requirements of the species. This was confirmed by the frequency distributions of probabilities of presence across Spain predicted by both models (Fig 5 ): no cell was predicted to be unsuitable with the model based on field data, which found a median probability of presence across Spain of 0. 4. By contrast, the model based on SVI data predicted a higher proportion of unsuitable cells and a lower median probability of presence (0.
Discussion The more striking outcomes of our alternative prospection method using street view imagery (SVI) were the impressive amount of presence data collected in a relatively short time, and the fact that it provided absence data (which are not available in field databases). This has made it possible for us to model different ecological niches (with drastic changes in effect size for the three considered predictive variables) and spatial distributions for the species in question. We collected, using SVI, 345 presences, i. e. more than 1. 5 times as many as the 216 field presence records in the Anthos national Spanish database. In addition, our SVI data were collected by a lone worker in about 50 hours (parsimoniously spread over one working month) without incurring any travel expenses. By contrast, the traditional field dataset has been amassing since 1839 and has involved the work of hundreds of botanists.
Our random selection of prospected cells limited data aggregation within over-informed regions, e. g. around major cities, research centers and university campus, or within well-prospected regions. For example, 22% of field presences are located in the Province of Alicante, where SVI prospection localized only 8% of SVI presences (Fig 2A ); this prospection bias is specific to the Anthos database, and mainly due to the detailed prospection of the Province of Alicante through the PhD thesis of Luis Serra Laliga, generating a regional database of more than 100 000 species records  [22]. Table  1. Proportion of deviance explained and effect size (95% confidence intervals into brackets) for three predictive variables (3 first axes of a PCA run on bioclimatic variables, see Methods) by models exploring the distribution of Arundo donax.
The 216 absence data used in (i) and (ii) were randomly generated 1000 times and the results shown refer to median values. Moreover, the SVI data are both more homogeneously distributed and more accurately located than field data: each presence is tied to an accurate GPS position (vs. several field data corresponding to centroids of regional locality). Consequently, anyone can verify the SVI presence using our sampling information (S1 Table ). These data can then be used for diachronical studies using SVI methods of field monitoring. The exploration of the 216 cells gathering Anthos field presences with the SVI prospection method confirmed species occurrence in 81. 1% of cells. This implies a mismatch proportion of 18. 9%, which though weak is not null, suggesting a significant impact due to false absences in SDMs  [23].
Data used to 1. Considering the dates of observation in the Anthos database, which span the years 1839 to 2014, the extinctions of formerly observed populations could explain some mismatches. It is noteworthy that 44% of the mismatching cells correspond to occurrences recorded before the 1990s in the Anthos database (28% for the matching cells) and that no mismatch was noticed for field data recorded after 2010 (two records for the matching cells). 2. Mismatches could be due to a lack of SVI in cell, leading to an indication of apparent species absence in a poorly prospected cell. This hypothesis is supported by the fact that 59% of the mismatching cells (N = 24) exhibit a total road length (on a log scale) that is below the 0. 45 threshold that corresponds to an 80% probability of matching.
3. Finally, these mismatches could be explained by their eco-geographical position in the edge of the bioclimatic niche of A. donax. Indeed, field presences could over-inform species occurrence in its limits of distribution and ecological niche: one can expect that a naturalist is more likely to mention a species when this species is rare in this area. This is actually in line with the fact that most mismatches were inland, where both SVI and field presences are rare (Fig  2D ). If field prospection overestimates rare occurrences, SVI plant prospection may underestimate them. Consequently, this alternative method must be led on common species to minimize false absences. A current limit of the SVI prospection is of course its restriction to the vicinity of the road network. Interestingly however, we showed that field data were more highly biased than SVI data toward cells with a denser road network, a pattern likely to be common to most field databases and that undermine their value in the SDM context too.
A crucial added value of the SVI method is that it provides absence data that are supported by a standardised prospection method and a random sampling strategy (at the level of the study area). Biogeographical studies often misinterpret the absence of occurrence data in a region as a species absence, an effect often referred to as 'pseudo-absence'  [25]. This inference supposes that field presences exhaustively describe species distribution, a postulate that is rarely met. The impact of pseudo-absence on a model's discriminating ability can be mitigated by using alternative pseudo-absence selection strategies (e. g. by using a minimal and maximal distance to presence points for selecting pseudo-absence candidates, or by defining environmental conditions in which pseudo-absences can be selected which differ from the environmental conditions of a defined proportion of presence data).
However, these methods may lead to over-optimistic model evaluations and they remain in all cases arbitrarily selected. Spatial homogeneity in coverage (achieved by a randomized sampling effort) is indeed a key to modelling species distribution  [26]. Classic methods involving the arbitrary selection of pseudo-absence can therefore define pseudo-absence in areas where the species was not actually looked for. By contrast, the SVI method defines absence data by a prospection similar to the one used for recording presence data. In our case, the results clearly show that absence data collected using the SVI method changed the model outputs relative to presence-pseudo absence data, thus improving discrimination between presence and absence, and decreasing the probability of occurrence of the giant cane in cold and arid areas.
We observed that even for a similar sample size, SVI-based GLMs showed an explained deviance three times greater than field-based models, with substantially higher effect sizes associated with bioclimatic variables. This large difference between the two types of data may putatively be attributed to false absences included in pseudo-absence data related to the SVI data. False negatives are indeed particularly deleterious to model calibration and data fitting  [23]. It is worthwhile to note that the proportion of deviance explained by SVIbased models using the whole dataset or a subset of it (with less than half of the total sample) were highly similar. This result suggests that, in the case of A. donax, the SVI prospection in 10% of cells covering Spain would provide a similar goodness-of-fit for model calibration while increasing the time and cost effectiveness of the SVI proposed method.
For example, when it comes to plant species, we recommend that the SVI-prospection method be applied only to highly recognizable common taxa such as trees, shrubs or tussock herbs, which maintain a characteristic shape throughout the year. The latter consideration is crucial: special attention must be paid to seasonal differences in species' morphological aspects (e. g. colour phenological differences of A. donax leaves; Fig 1A  and 1B ) or to the periodicity of physical damage. Eventually, the ecological restriction of street view to roadsides will be partly removed when its coverage is extended into the wild. Indeed, this has already begun with street view imagery now beginning to depict natural areas such as national parks (e. g. Grand Canyon, USA; Galápagos Islands, Ecuador). This new data collection will allow naturalists to virtually visit many natural landscapes, making SVI prospection a boon to future biodiversity studies.
Acknowledgments The authors wish to thank  Wolfgang Cramer  ( IMBE ) for offering scientific support and helpful comments, the  'MacroBio' Team (IMBE)  for publication costs, and  Leopoldo Medina  ( Anthos, Fundación Biodiversidad, Spain ) for providing highly valuable field occurrence data from Spain. The authors are also grateful to  Rafael Dias Loyola ,  Bazartseren Boldgiv ,  Daniel de Paiva Silva  and three anonymous reviewers for their relevant comments which have greatly improved the methodological aspects of the present study.
The LTQ-Orbitrap Velos instrument was set in data dependent mode to automatically switch between full scan MS and MS/MS acquisition with a dynamic exclusion of 20 s turned on. Survey scans (350-2,000 m/z) were acquired in the Orbitrap system with a resolution of 60,000 at m/z 110. The ten most intense ions with charge states of 2+ or 3+ were sequentially isolated and fragmented in the HCD collision cell using a normalized collision energy of 40. The fragment ions were analyzed with a resolution of 7,500. The general mass spectrometric conditions were as follows: 2. 30 kV spray voltage, 100 μA source current, no sheath and auxiliary gas flow, heated capillary temperature of 225°C, predictive automatic gain control (AGC) enabled, and an S-lens RF level of 64%. Mass spectrometer scan functions and nLC solvent gradients were controlled by the Xcalibur data system (Thermo, San Jose, CA, USA).
Peptide spectrum Matching Sequences from Homo sapiens were downloaded from the UniProt consortium. A target-decoy database was generated using PatternLab 4. 0  (16)  to include a reversed version of each sequence found in the database plus those from 127 common mass spectrometry contaminants. The ProLuCID search engine (v. 1. 3. 1) (17) was used for comparing experimental spectra against those theoretically generated from a sequence database. The search was limited to fully and semi-tryptic peptide candidates. The search parameters imposed carbamidomethylation of cysteine as a fixed modification and the iTRAQ-4 modification in the N-terminal, K, and Y residues as variable. The search engine accepted peptide candidates within a 40-ppm tolerance from the measured precursor m/z and used the XCorr as the primary search engine score.