dataesr/scientific-paragraphs-categorization huggingface dataset:
Step 1: Availability and Coverage of Citations in NosoBase® There were a variety of citations included in the bibliographies of the three systematic reviews, including articles on methodology and economic impact (review 1); surveillance, good practice, and psychology (review 2); and systematic review methodologies, meta-analysis, statistics, and models (review 3). These and other nonhand hygiene related references were included in the category "other. " Hand hygiene references were defined as articles directly related to research in hand hygiene practices. See Table  1  and Figure  1  for the comparison between the three systematic reviews and the database NosoBase®. From  The contact author for each systematic review confirmed that the literature search was carried out either by information professionals/librarians, or by researchers previously trained by them.
Accessibility In terms of accessibility, there is free online access to the NosoBase® database and web resources, with phone and email access to librarians for bibliographic aid and advice. However, the new database system (transitioned in March 2017) required current users to adapt to the new interface. There is also an urgent need for a search engine to access the website resources due to data growth. The opportunities for improvement involve facilitating access through user guides and facilitating website navigation and searching through an efficient search engine.
Production The strengths of NosoBase® lie in its rich collection of French and multilingual documents (e. g. , English, Spanish, and German); diverse IPC related documentation (legislation, guidelines, and toolkits); articles in multiple formats (paper and electronic); and a new generation Functional Requirements for Bibliographic Records (FRBR) database system. It also has a librarian curated specialized IPC thesaurus. However, there is a lack of standardized internal procedures, and it was significantly time-consuming to adapt and format the new database system. The thesaurus is not updated regularly, and it needs to evolve along MeSH terms. There is therefore opportunity for growth through streamlining and standardizing work procedures, through exploiting the functions in the new database system, and through revising the IPC thesaurus to evolve with MeSH terms.
Conclusion Specialized or subject-specific bibliographic databases came into being due to the growth and proliferation of publications. However, they currently face increasing costs of maintenance, competition from free online access to multidisciplinary databases such as PubMed, and the development of online search engines such as Google Scholar. Evidence based practices in librarianship also led to the development of methodologies to evaluate the relevance of documentation centres. This study explored the relevance of the national IPC bibliographic database NosoBase® and its documentation centre services. The results indicate its relevance, reflected by a good coverage and availability of citations from three systematic reviews based on the theme of hand hygiene, a wide scope of content based on hand hygiene related keywords, and an important listing of French language based publications and grey literature.
2: Scope and Relevance of Content in NosoBase® soignante, Hygiènes, Inter-bloc, Noso-info, Pratiques psychologiques, Risques et qualité en milieu de soins, Soins aides-soignantes, Soins, and national Systematic review infection control bulletins. A majority of these journals are not indexed in commercial or other Availability in NosoBase® All citations well-known databases. HH citations All citations HH citations Number (n) n % n % n % Review 1 The documents listed included four legislative 120 106 88. 3 79 65. 8 75 70. 8 Review 2 texts from the Public Health Council (HCSP), the 88 57 64. 8 55 62. 5 46 80. 7 Review 3 French National Organization for 89 Standardization (AFNOR), and the French 47 52. 8 48 53. 9 38 80. 9 Republic Official Journal (Journal officiel de la République française).
B. Results of the Quad Band Antenna Without AMC The antenna was designed using the electromagnetic simulation software CST. A prototype was fabricated and Corresponding author: Amira Bousselmi experimentally tested. The reflection coefficient of the fabricated quad-band antenna measured by a Rohde Schwarz ZVA 67 vector network analyzer is compared with the simulation results and shown in Figure  3. A good agreement is observed between the results. The proposed design should operate at four frequency bands, 1. 278GHz, 2. 8GHz, 5. 8GHz and 10GHz, whereas the measured ones are 1. 3GHz, 2. 82GHz, 5. 5GHz and 9. 5GHz, the small discrepancy obtained at the higher resonance frequency is attributed to the fabrication tolerances provided by the SMA connector which are not considered in the simulation.
ARTICLE Adaptive responses of animals to climate change are most likely insufficient Viktoriia Radchuk et al. #  Biological responses to climate change have been widely documented across taxa and regions, but it remains unclear whether species are maintaining a good match between phenotype and environment, i. e. whether observed trait changes are adaptive. Here we reviewed 10,090 abstracts and extracted data from 71 studies reported in 58 relevant publications, to assess quantitatively whether phenotypic trait changes associated with climate change are adaptive in animals. A meta-analysis focussing on birds, the taxon best represented in our dataset, suggests that global warming has not systematically affected morphological traits, but has advanced phenological traits. We demonstrate that these advances are adaptive for some species, but imperfect as evidenced by the observed consistent selection for earlier timing.
7, 8 ) and hence most studies can only speculate on whether adaptive responses have occurred. Here, we extracted data from many published studies to assess these three conditions in free-living animals and thus determine whether the observed phenotypic changes are adaptive. Multiple studies report data satisfying the first two conditions. In particular, increases in temperatures across multiple locations during recent decades are well documented (i. e. global warming  15  ). Similarly, the effects of climate change on several traits are well characterized. For example, the timing of biological events, such as reproduction or migration (hereafter 'phenological traits'), has generally advanced across multiple taxa and locations  13, [16] [17] [18]. 'Morphological traits', such as body size or mass, have also responded to climate change, but show no general systematic pattern  8, 14, 19, 20.
A substantial challenge to test the third condition is that the data must be collected over multiple generations in single populations. Existing datasets assembling data on either trait variation  [21] [22] [23]  or selection  [24] [25] [26]  across taxa, although valuable, are not well suited for testing whether phenotypic trait changes are adaptive, because these two types of datasets rarely overlap in terms of species, traits, study location and study period. Siepielski et al. 27  assembled a global dataset combining climatic factors and selection in species and showed that precipitation, rather than temperature, explained most of the variation in selection. However, neither their analysis nor a follow-up study  28  assessed whether phenotypic responses to climate (PRC) were adaptive because the assembled dataset does not contain data on trait changes.
A search on Web of Knowledge (Methods) returned 10,090 publications, of which 58 were retained. These publications reported data on 4835 studies (representing 1413 non-aquatic species in 23 countries) that contained information on phenotypic responses to climate change. Out of these 4835 studies, a subset of 71 studies (representing 17 species in 13 countries) contained all the information required to assess whether responses were adaptive (including selection differentials, Methods). We stored information on the 4835 studies in the 'PRC' dataset, and information on the subset of the 71 studies in the 'PRC with Selection data' (PRCS) dataset. We used the PRC dataset to assess how representative the PRCS subset was with regard to (1) the observed change in climatic factors over time, and (2) the change in phenotypic traits in response to climatic factors.
We define a 'study' as a dataset satisfying our selection criteria for a unique combination of a species, location, climatic factor, phenotypic trait and fitness component. We had more studies than publications because some publications reported data for several species, several climatic factors and/or several phenotypic traits. Structure of PRC and PRCS datasets. The studies in both datasets were predominantly conducted in the Northern Hemisphere (Supplementary Fig. 1 ). The PRC dataset was heavily biased towards arthropods (88% of studies), with other taxa constituting only a small proportion of the studied species (Supplementary Fig. 2 ). In contrast, the PRCS dataset was heavily biased towards birds (95%). We found no studies for insects and amphibians that reported annual selection data and satisfied all other inclusion criteria.
Among the climatic variables used, temperature dominated both datasets (>70%, Supplementary Fig. 2 ); therefore, we focused on the effects of temperature changes in the main text and provide results for precipitation in Supplementary Fig. 3  and Supplementary Note 1. The majority of studies focused on phenological (rather than morphological) traits, with this bias being less pronounced for the PRCS dataset. The median duration of a study was 29 years in the PRCS dataset and 24 years in the PRC dataset (Supplementary Fig. 4 ). Adaptive responses to global warming. We generally expected that warming temperatures would be associated with an advance in phenological events, because most studies on phenology in our PRC dataset represented early season (spring) events in the Northern Hemisphere, and such events were previously shown to mainly advance with warming temperatures  30, 31.
We defined a trait change to be adaptive in response to climate if the climatedriven change in phenotype occurred in the same direction as linear selection. For example, with an increase in temperature over the years, breeding time occurs progressively earlier, with earlier breeding conferring higher fitness (Fig. 1 ). In contrast, if the trait changed in the direction opposite to selection (e. g. later breeding, despite earlier breeding being favoured), then the response was considered maladaptive  11. The detected adaptive responses might be due to microevolution, phenotypic plasticity or both. As we used selection differentials that were measured at the phenotypic level, we could not differentiate among these sources. We conducted separate analyses for PRCS and PRC datasets and, within each of them, for temperature and precipitation.
e. WMSD and trait change over time differ in their sign) with a binomial test (Methods). We also performed a meta-analysis of the product between WMSD and the sign of the climate-driven trait change over time using a mixed-effects model (Methods). In line with the recent global temperature increase  33  , temperature increased across studies by 0. 040 ± 0. 007 °C (mean ± SE) per year according to the PRCS dataset (likelihood ratio test [LRT] between the model with and without change in temperature over years: χ 2 = 20. 4, df = 1, p < 0. 001), and by 0. 043 ± 0. 005 °C per year according to the PRC dataset (χ 2 = 41. 0, df = 1, p < 0. 001) (Fig. 2 ). These rates are slightly higher than those observed in recent meta-analyses that, similarly to our study, are biased toward data from northern latitudes (range 0.
03-0. 05 °C per year  17, 31  ). A possible explanation for this discrepancy is that warming rates are higher in recent time series such as ours (Supplementary Fig. 5 , median first year in the PRCS dataset = 1980, and median study duration = 29 years)  31, 34. Consistent with previous work  13, 16, 17  , phenology advanced with increasing temperatures at a rate of -0. 260 ± 0. 069 standard deviations in the focal trait per degree Celsius (SD per °C) according to the PRCS dataset (LRT between the model with and without change in phenology: χ 2 = 11. 2, df = 1, p < 0. 001) and at a rate of -0. 248 ± 0. 037 SD per °C according to the PRC dataset (χ 2 = 22. 9, df = 1, p < 0. 001). In the PRC dataset, the phenological response to temperature varied among taxa (Fig. 3 , LRT between the model with and without taxon as a predictor: χ 2 = 133.
5, df = 5, p < 0. 001), with the strongest phenological advancement found in amphibians, followed by insects and birds (Supplementary Data 1). This finding is in line with previous research showing that amphibians advanced their phenology faster than other taxa  13, 16. In contrast to Cohen et al. 17  , we did not find significant variation in phenological responses among different types of traits (categorized as arrival, breeding and development), either in the PRCS dataset (Supplementary Data 2, LRT between the model with and without the trait type as a predictor: χ 2 = 0. 5, df = 2, p = 0. 775) or in the PRC dataset (LRT: χ 2 = 0. 4, df = 2, p = 0. 809). Our findings of advancing phenology with warming temperatures were qualitatively unaffected by including abundance, and, although abundance did affect phenological responses, the effects of temperature on phenology were generally larger than those of abundance (Supplementary Fig.
6 ). Morphological traits were not associated with temperature in the PRCS (rate of change: 0. 060 ± 0. 078 SD per °C; LRT: χ 2 = 0. 6, df = 1, p = 0. 443) and only marginally associated with temperature in the PRC dataset (rate of change: -0. 053 ± 0. 029 SD per °C; LRT: χ 2 = 3. 3, df = 1, p = 0. 068). Neither endothermy nor type of morphological measure (skeletal vs. body mass) moderated the relationship between morphological traits and temperature (Supplementary Data 2). Our analyses indicated, however, that taxa may moderate the effect of temperature on morphology in the PRC dataset (LRT: χ 2 = 4. 5, df = 1, p = 0. 11, Supplementary Data 2), with negative associations on average observed in mammals, and no strong association found in birds (Fig. 3 ). Across studies, we found a negative WMSD (=-0.
Overall, phenological traits in both the PRCS dataset (black) and the PRC dataset (dark blue) were negatively affected by temperature. Morphological traits were not associated with temperature in the PRCS (grey) and showed a tendency to a negative association with temperature in the PRC dataset (cyan). In the PRC dataset there was significant variation among taxa in the effect of temperature on phenological (blue) traits, and a tendency to such variation for morphological traits (cyan). See Fig. 2  for legend details. The majority of the species pictures were taken from Pixabay (https:// pixabay. com/images/). The exceptions are a picture of red-billed gull (credit: co-author J. A. M. ) and four pictures taken from Macaulay library (https:// www. macaulaylibrary. org/). Illustration credits for pictures taken from Macaulay library: great reed warbler-Peter Kennerley/Macaulay Library at the Cornell Lab of Ornithology (ML30060261), European pied flycatcher-Suzanne Labbé/Macaulay Library at the Cornell Lab of Ornithology (ML30638911), song sparrow-Steven Mlodinow/Macaulay Library at the Cornell Lab of Ornithology (ML47325951) and Eurasian scops owl-Jon Lowes/Macaulay Library at the Cornell Lab of Ornithology (ML103371221).
45. Furthermore, the lack of a temporal trend in the strength of selection means that, although populations are not perfectly adapted in their phenology, they are not getting more adapted or less maladapted over time as temperatures continue to rise. This result suggests that they are phenotypically tracking a shifting optimum, lagging behind at a constant rate, as predicted by Bürger and Lynch  35. Our comparisons of the actual vs. critical lags suggest that there is low but non-negligible probability that the degree of maladaptation is large enough for the majority of our study populations to be at risk. The actual risk of population extinction may in fact be larger because our estimations do not account for several sources of stochasticity  35. Moreover, our dataset predominantly includes common and abundant species (e.
Further, our analyses are correlative and we cannot rule out the possibility that the presumed effects of temperature are in fact due to other or additional environmental variables that correlate with climate, or that selection estimates are biased by environmental correlations between trait and fitness 51 , or do not accurately reflect total selection as a result of being based on incomplete fitness measures. Similar to the recent global assessments of the climate effects on phenology  17, 34  and selection  27  , our datasets are heavily biased towards studies from the Northern Hemisphere. Additionally, the majority of the phenological traits in our datasets focus on early season (spring) events. Previous research has shown that early season phenological responses, especially at northern latitudes,   are advancing with warming temperatures  16, 31, 52  , and therefore an advancement of phenological events was our main working assumption.
However, evidence for this response is equivocal  14, 19, 20, 55, 56. Our results suggest that inconsistency in findings to date may be explained by different studies focusing on different taxa (e. g. birds  14, 20  , mammals  57  ). Indeed, we found that the association between morphological traits and temperature tends to differ among taxa, with only mammals showing a clear negative association. This finding contrasts with a lack of relationship between temperature and morphology reported for mammals by Meiri et al. 57  , potentially because their study periods were longer than ours, and they used a different morphological measure (condyle-basal length). Although our PRC dataset is not exhaustive, our findings of variation among taxa in both phenological and morphological responses highlight the importance of collecting observations on a wide range of taxa.
Difference between actual and critical lag The assembled PRCS dataset suggests several avenues for further research. For example, currently underappreciated effects of climatic variation on traits and, in turn, on fitness and population viability may be pronounced  58  and our datasets could be used to investigate them. Further, extending this dataset to incorporate vital rates and, ideally, population growth rate would allow for the mapping of environmental changes onto demography via phenotypic traits  44, 59, 60  , and ultimately a better assessment of how trait responses impact population persistence. Our results are an important first demonstration that, at least in a range of bird species, adaptive phenological responses may partially alleviate negative fitness effects of changing climate.
Only studies that reported SE estimates along with annual linear selection differentials were retained. For the majority of studies, we extracted selection differentials directly from the published studies, and for 12 studies, we calculated them ourselves using the respective individual-level data shared by the authors. To identify the studies satisfying the above-mentioned criteria, we searched the Web of Knowledge (search conducted on 23  ). These detailed taxon names were combined with the same keywords for climate change, adaptation and trait as before. Finally, we joined the unique records from each of these two searches in a single database. The literature search returned 10,090 publications, 56 of which were retained after skimming the abstracts. Of these 56 publications, 23 contained the data necessary to assess the three conditions required to infer adaptive responses and were used for assembling the final dataset (PRCS dataset).
In cases where several publications reported on the same study system (same species in the same location, measuring the same traits and selection via exactly same fitness components), we retained the publication that reported data for the longest time period. We assembled the PRCS dataset by directly extracting the data from the identified 23 publications wherever possible, or by contacting the authors to ask for the original data. Data were extracted either from tables directly or from plots by digitizing them with the help of WebPlotDigitiser or the metagear package in R  61. In the process of contacting the authors, one research group offered to share relevant unpublished data on two more species, adding two more studies to the dataset, totalling 25 publications. The PRCS dataset consisted of 71 studies containing data on annual values of climatic factors, annual phenotypic trait values and annual linear selection differentials for 17 species in 13 countries (Supplementary Data 3).
The remaining 33 publications from the originally selected 56 (58 with the shared unpublished data considered as two publications) did not report data on selection, but presented data on the annual values of climatic factors and mean population phenotypic traits, totalling 4764 studies that covered 1401 species. We retained these studies and combined them together with the 71 studies in the PRCS dataset to assemble the 'PRC' dataset (Supplementary Data 3). With the PRC dataset, we did not aim for comprehensive coverage of the literature published on the topic. Instead, we used this larger PRC dataset to verify whether the smaller PRCS dataset was representative in terms of climate change over time and trait change in response to a climatic factor. A flowchart showing the numbers of studies included at each stage of the systematic literature review is given in Supplementary Fig.
10. Assessing whether the responses are adaptive. Separate analyses were conducted for the PRCS and PRC datasets and, within each of them, for temperature and precipitation. All analyses were conducted using linear models as no deviation from linearity was detected by visual inspection of the relations between (1) year and climate, (2) trait and climate and (3) selection and year for each study (Supplementary Figs. 11 12 13 14 15 ). First, we assessed for each study condition 1 necessary to infer adaptive responses (i. e. the extent to which the climatic variable changed directionally over years). To this end, we fitted for each study a mixed-effects model with the climatic variable as the response and the year as a fixed covariate, taking into account temporal autocorrelation (as random effect): Clim t ¼ α þ β Clim Year t þ ε t þ ε; ð1Þ where Clim is the quantitative climate variable, Year the quantitative year covariate, t the time, ε t is a Gaussian random variable with mean zero and following an AR1 model over years, and ε is an independent Gaussian random variable with mean zero and variance representing the residual variance of the study.
1e ). The variance used in weighting is the prediction variance. The SE of the WMSD is deduced from these weights and from the covariance matrix of the predictions (see source code of the function extract_effects () in our R package 'adRes' for details). In the second step, to assess whether the response is adaptive, we considered WMSD in combination with the slopes obtained for the previous two conditions, as follows: we defined a trait change to be adaptive in response to climate if the climate-driven change in phenotype occurred in the same direction as linear selection. In contrast, if the climate-driven change in phenotype occurred in the direction opposite to selection, then the response was considered maladaptive. We measured the climate-driven change in phenotype as the product of the slopes obtained for conditions 1 and 2.