dataesr/scientific-paragraphs-categorization huggingface dataset:
Temperature Monitoring A Dense Network of Temperature Sensors In order to characterize temperature variability over this area of 19,233 ha, including 12,200 ha of vineyards, a network of 90 temperature sensors was deployed at the end of 2011. This represents a density of one sensor for 214 ha. At this local scale, it is important to take into account the topography (exposure, slope, elevation), the latitude and longitude, and also local parameters, such as rivers and urban areas, which can potentially influence spatial distribution of temperature (Figure  1 ). Temperature sensors and data loggers were distributed to cover as much as possible the variability of these local parameters. The data loggers used in this project from 2012 to 2015 are Tinytag Talk2-TK-4023 (Gemini Data Loggers, United Kingdom).
From 2016 to 2018, automatic data recovery was achieved by using the LoRa technology, with new data loggers developed by OrbiWise company (Geneva, Switzerland). The thermistor probes used throughout the project from 2012 to 2018 were PB-5005-0M6 (Gemini Data Loggers, United Kingdom). The data loggers were set up on vine posts in the vineyard plots in order to measure temperature as close as possible to the vines. The probes were installed inside solar radiation shields (Type RS3), at a height of 1. 2 m close to the vegetation. To reduce measurement errors due to vegetation located just around the solar radiation shields, vine shoots were regularly removed during the vegetative period  (Madelin et al. , 2014). The data loggers recorded both minimum (Tn) and maximum (Tx) hourly temperatures.
For data treatment, daily temperatures are used in this study. The daily minimum temperature corresponds to the extreme minimum temperature between the day before at 6 pm and the day at 7 pm, and the maximum temperature corresponds to the extreme maximum temperature between the day at 6 am and the day after at 7 am. Average daily temperature was computed as (T n + T x )/2. Theoretical accuracy of the data logger is 0. 4 • C and to reduce measurement uncertainties, two thermistor probes were installed in every solar shield starting in 2017. This allows detecting deviation and erroneous temperatures due to sensor problems. From 2012 to 2017, the quality of the minimum and maximum temperatures was graphically analyzed by plotting the daily data of all the sensors. Outliers and deviations were visually detected and eliminated of the data base.
Weather Station Meteorological data from the weather station of Météo-France, located in the Saint-Émilion area (Figure  1 ) were used to regionally characterize the studied years (2012-2018) (Figure  2 ). To compare the temperatures registered by this weather station and those recorded by the Tinytag data loggers located in vine parcels close to the canopy, a Tinytag data logger was set up in a vineyard plot at 4 meters distance to this weather station at the end of 2015.
Bioclimatic Indices The Winkler degree day summation  (Winkler, 1974)  was used in this study, as it is well adapted to study the influence of temperature on vine development. This index is based on the sum of mean temperatures above 10 • C, from 1 April to 31 October. Because temperatures were measured inside the canopy in this project, and not in a weather station as in  Winkler (1974) , this index is referred to a canopy Winkler index (CWI)  (de Rességuier et al. , 2018). CWI is based on the same formula as the Winkler index, the difference is due to the location where temperatures were recorded. The GFV model  (Parker et al. , 2011 (Parker et al. , , 2013) )  was created to simulate the occurrence of mid-flowering and mid-véraison from temperature data for a wide range of grape varieties.
Vine Development Monitoring To determine the impact of spatial temperature variability on vine development, the major phenological stages (mid-budbreak, mid-flowering, and mid-véraison) were measured for Merlot from 2013 through 2018 on blocks of 20 vines each at 60 locations near temperature sensors. Only 17 such blocks were surveyed during the 2012 vintage when the project was under development. The specific day when 50 percent of vine organs reached stage "C" for budbreak, stage "I" for flowering and stage "M" for véraison was recorded  (Baggiolini, 1952; Destrac-Irvine et al. , 2019). The 2017 vintage was excluded from the data analysis, because of frost damage which affected 80% of the reference plots. Grape maturity dynamics were monitored on 18 blocks chosen to be representative of the thermal variability identified inside the study site.
Environmental Co-variables Spatial temperature variability at the local scale is influenced by topography-related parameters  (Carrega, 1994; Beltrando, 2000; Scherrer and Körner, 2011; Quénol and Bonnardot, 2014). A digital elevation model (DEM) provided by National Geographical Institution (IGN, France), with a 25 m horizontal resolution and 1 m vertical resolution, was used to produce raster layers of elevation, slope, and exposure by using the Spatial Analyst Tools from ArcGis software (ESRI, 92195 Meudon, France). Exposure was separated into two components (north/south and east/west)  (Bonnefoy, 2013). Given the size of the study area (17 km * 18 km), the latitude and the longitude were also calculated from ArcGis software and taken into account as variables. Pixel values of all these environmental variables were extracted at the location of each temperature sensor.
Statistical Analyses The effect of environmental parameters (elevation, slope, north/south exposure, east/west exposure, latitude, longitude) on minimum temperatures on 13 March 2012 and 7 April 2012 were investigated by multiple linear regressions. Average daily mean, minimum, and maximum temperatures over the growing season, CWI, and phenological observations were represented annually by using boxplot graph set up with the package ggplot2 from R software. Daily thermal amplitude on minimum and maximum temperature (years taken together) were represented monthly by using boxplot graph. In the boxplot representation, outliers are represented as dots. They correspond to observations whose values are higher than the value of the third quartile plus 1. 5 times the interquartile interval, or less than the value of the first quartile minus 1.
Spatio-Temporal Temperature Analysis Based on a large unprecedented dataset obtained by a high density network of temperature observations, important spatial amplitude of seasonal temperatures over this area was observed. An average of 320 degree-days of amplitude over the 7 years studied was found for CWI. This study also underlines large spatial amplitude on minimum temperatures during the vegetative season and great inter-annual variation of maximum temperatures, which impacts the vintage effect. Hence, spatial temperature variability was more driven by minimum temperatures, while temporal (i. e. , year-to-year) temperature variability was more driven by maximum temperatures. Previous studies investigating temperatures at the vineyard scale have also shown great spatial temperature variability  (Quénol et al.
These results need to be confirmed over the duration of the project with the use of appropriate statistical tools. Another approach to assess the impact of weather conditions on spatial temperature variability is to classify daily temperature maps based on statistical criteria. In a second step, synoptic atmospheric conditions (wind direction and atmospheric pressure) of each cluster can be determined. This approach was tested on the 2014 data of this study site  (Le Roux et al. , 2017b). A classification in nine nodes of the spatial distribution of temperature was obtained. To determine the average daily atmospheric conditions of each node, the outputs of the regional model (Weather Research and Forecasting) were used. Spatial variability of temperatures was analyzed according to the atmospheric situation and allowed a better understanding of the results regarding the distribution of the temperatures over the study area.
Relationships Between Vine Development and Temperature It is well established that temperature is a major driver of plant phenology  (Chuine et al. , 2013). Most studies assessing the relationship between temperatures and phenology are based on point data (i. e. , data obtained in specific locations). Spatial modeling of phenological stages has been implemented, but only at large scale  (Fraga et al. , 2016; Sgubin et al. , 2018). Our aim was to produce local scale maps of the occurrence of phenological stages. To do so, our spatial temperature modeling was coupled to phenology modeling. A high number of phenology observations were used as a validation dataset. The GFV model  (Parker et al. , 2011)  used to assess mid-flowering and mid-véraison dates was developed with data collected by regular weather stations.
In this study, temperature data were recorded by Tinytag thermistor probes installed inside the vegetation. Hence, it was necessary to compare obtained data with temperatures recorded by a regular weather station. Temperatures registered by both systems are different for minimum and maximum temperatures. This result underlines influences of the local environment (vine parcel, canopy), but also an impact of intrinsic characteristics of measurement equipment (type of sensor, solar shield) on recorded temperatures. In order to use the published GFV model parameters  (Parker et al. , 2013)  on the data collected with the Tinytag data loggers, temperatures collected in this study were corrected by means of a linear regression. Several hundred real phenology observations allowed validation of the GFV model at this scale (RMSE = 2.
In our dataset, amplitude (i. e. , the difference recorded by the coldest and warmest sensors) is 2. 6 • C for Tn, 2. 1 • C for Tx and 320 degree-days for CWI (average 2012-2018). On the maps obtained by spatial modeling, this amplitude is very similar for Tn with 2. 5 • C but is reduced to 0. 8 • C for Tx and 251 degreedays for CWI (average 2012-2018). Similar reduction is shown for phenological stages (from 9 to 6 days for mid-flowering dates and 13 to 9 days for mid-véraison dates). The spatial modeling of the minimum temperature over the vegetative season is very accurate and the temperature ranges and the amplitudes are very close to the recorded temperatures. On the other hand, there is a sharp reduction of modeled amplitudes of maximum temperature. Visualization of the measured maximum temperature distribution over the different growing seasons (Figure  5 ) shows that the amplitudes are often extended by extreme points, which is not the case for the minimum temperature distribution.
ACKNOWLEDGMENTS We are grateful to the interns involved in this project:  Marc Legault ,  Pauline Souquet ,  Maëlle Eveno , and  Mélissa Bordage. We also acknowledge the technical assistance of the  Groupement de Défense contre les Organismes Nuisibles (GDON) du Libournais and of Jean-Pascal Tandonnet ,  Guillaume Pacreau , and  Nabil Zirari  for collecting phenological data.
Acknowledgements -We thank  Alessandro Carlini ,  Andrea Chiocchio ,  Francesco Ciabattoni ,  Lorenzo Latini ,  Armando Macali ,  Cinzia Mastrogiovanni ,  Ambra Pazzani  and  Mauro Zampiglia  for assistance with the experimental procedures. Funding -The research was supported by a grant from the  Italian Ministry of Education ,  Univ. and  Research  ( PRIN  project  2017KLZ3MA ) to  D. Canestrelli.
Biological invasions are responsible for tremendous impacts globally, including huge economic losses and management expenditures. Efficiently mitigating this major driver of global change requires the improvement of public awareness and policy regarding its substantial impacts on our socio-ecosystems. One option to contribute to this overall objective is to inform people on the economic costs linked to these impacts; however, until now, a reliable synthesis of invasion costs has never been produced at a global scale. Here, we introduce InvaCost as the most up-to-date, comprehensive, harmonised and robust compilation and description of economic cost estimates associated with biological invasions worldwide. We have developed a systematic, standardised methodology to collect information from peer-reviewed articles and grey literature, while ensuring data validity and method repeatability for further transparent inputs.
In this paper, we introduce InvaCost as the most up-to-date, comprehensive, harmonised and robust global-scale data compilation and description of economic cost estimates associated with invasive species. InvaCost has been constructed to provide a contemporary and freely available repository of monetary impacts that can be relevant for both research and evidence-based policy making issues. To achieve this goal, we have developed a defensible, systematic, transparent and repeatable collaborative work strategy (Fig. 1 ). A large pool of both scientific peer-reviewed articles and grey literature (i. e. the diverse and heterogeneous body of material available outside, and not subject to, traditional academic peer-review processes  29  ) was collected and scrutinized (Table  1 ). We extracted therein explicit estimations of costs and expenditures associated with invasive species, and then coupled them with a range of descriptors presented in this paper (Online-only Table  1 ).
Here, we provide a full description of the process of InvaCost development, as well as specific details of all materials analysed. This unique, globally representative database (n = 2419 cost estimates currently described) is freely accessible online  30  and will be regularly updated with contributions from both authors and future users. readable by the review team (i. e. written in English or French) for ensuring reliable assessment, (ii) containing at least one cost estimate (iii) exclusively associated with (at least one) invasive species. This assessment was based on the progressive analysis of titles, abstracts and keywords. Materials whose abstracts were not accessible were conservatively considered as relevant for further full-text analysis. ** Cited references were materials not gathered with our literature search process, but mentioned as original references in the relevant materials (initially collected) we analysed when seeking for cost estimates.
com/), Google Scholar database (https://scholar. google. com/) and the Google search engine (https://www. google. com/). We carefully composed appropriate search strings that were consensually retained as the most efficient among a set of potential candidates. A decision was taken following preliminary tests based on a handful of relevant articles provided by consulted subject experts on some taxonomic groups (amphibians, reptiles, fishes and ants). Final selection of search strings comprised those considered to have the largest potential to identify key references. Each search string was set to include a combination of two search terms, related to 'invasive' and 'economics'. For both terms, we used a range of synonyms or related words. For example, for 'invasive' we used invasi*, invader or exotic; for 'economics' , we used econom*, cost or monetary.
To ensure transparency and validity, each document was checked by two reviewers and in case of a disagreement between assessors, a third reviewer was involved. However, it was often difficult to judge from the topic whether the content of an article was relevant and so consequently many more articles were conservatively kept when final agreement was lacking among assessors. Finally, relevant materials were scrutinized for data on economic costs (Table  1 ; Fig. 1 , step 3). During this step, additional relevant materials were found as cited by the analysed materials. Obtained cost data were collated in a database and the costs were converted to a common and up to date currency (2017 US$), and then depicted by different descriptors. Categories extracted from relevant materials allow search of the database and data pre-selection to facilitating analysis of costs based on taxonomic groups, geographical areas, impacted sectors, types of costs, or other categories.
The reliability of cost estimates and all associated information recorded in the final InvaCost database was systematically checked at least twice, and every ambiguous element was discussed to reach a consensus. We also checked all entries in the database to ensure that there were no obvious duplicate reports (i. e. multiple documents reporting the same cost estimate) or mistakes. Hereafter, we specifically describe each of the steps made to generate InvaCost. Literature search. Web of Science. We used the Web of Science (hereafter called WoS) to conduct a search for potentially relevant materials on 7 December 2017 (Fig. the titles of article's bibliographic references and not necessarily in the main text of the article itself. To limit the search to relevant fields of research, we used the function 'refine' to exclude subject areas not related to economics and/or invasion biology.
We exported all records (n = 16,875) into an Excel worksheet  30  (Table  1 ) to identify the relevant materials by a two-step procedure. First, we excluded the references identified only based on 'Keywords Plus' , which were shown to be poor specific descriptors of the content of articles  31. We also excluded references identified based on the presence of only a single search term in the topic, as we assumed that words related to both search terms ('invasive' and 'economics') should be mentioned at least once in the title, abstract and/or keywords of a relevant material. To identify these irrelevant materials within the references collected, we developed a script (see Code Availability) in the R programming language (R v. 3. 4. 3)  32. Subsequently, 10,592 references were kept for the next screening step based on the described criteria.
In the second step, the topic of every reference selected was checked manually to ensure potential relevance of its contents. This allowed the elimination of documents incorrectly identified as relevant, such as studies without a true monetary assessment, or those focusing on economic estimates not directly attributable to invasive species only. Finally, 1,333 documents were judged as relevant materials (Table  1 ) and moved to the final data collation step. Google scholar. The Google Scholar database is a large source of grey as well as peer-reviewed literature. Nevertheless, we had to modify our approach in order to address inherent limitations of this database as a search tool (see Haddaway et al. 33  for a comprehensive analysis). Typically, Google Scholar allows limited Boolean operators (no nesting using parentheses permitted) and search strings are limited to 256 characters.
We specified currencies for prioritising materials with monetary data in the top of the resulting list. These currencies were chosen as they were the most often used to express economic costs in the literature collected from the WoS. Nevertheless, any reference evoking economic costs in other currencies was expected to be also captured by some specific combinations of 'economic' terms in our search string that we would expect to be mentioned at least once in the full-text of relevant papers. In addition, we included the concomitant presence of 'invasive' and 'species' terms to restrict the outcomes to papers within the scope of our synthesis. Subsequently, we collected all viewable results (100 pages, n = 992 references of the 668,000 generated), thus going beyond the traditional and arbitrary sample size of first 50-100 results, which is frequently selected in many systematic reviews.
We especially targeted grey literature because searches by the other two platforms mainly led to peer-reviewed publications. We assumed that documents published online by various governmental and non-governmental organisations (NGO), research centres and academic institutes are more likely to contain relevant data than other types of documents such as blogs and catalogues  29. Therefore, we restricted our search to the documents located on governmental, academic and NGO webpages to ensure that explicit, traceable and expertise-based information was retrieved. We conducted independent searches for each type of webpage by specifying the type of web extension in the advanced search facility (. gov for governmental,. edu for academic, and. org for organisational webpages). 361 search hits were collected (document name, publishing year and URL of the main website homepage, if available) and stored in the database with the same host of dedicated information (Table  1 ).
If the item analysed was a website homepage, we conducted on-line searches of potentially relevant materials within the website database(s), by filters if available, or by using the search bar with combinations of keywords. Websites that did not contain a database or search bar were searched manually. We then eliminated all duplicates resulting from references being listed on multiple websites, or due to typographical mistakes and/or incomplete records when reporting a reference within different repositories. A total of 119 potentially relevant materials was finally obtained (Table  1 ). Targeted collection. Finally, we sourced other potentially relevant materials that did not originate from the above-described processes (Fig. 1 , step 1b). On one side, we dedicated specific efforts on gathering cost estimates for particular taxa or areas for which data previously obtained seemed scarce.
A typical message first summarized the objectives of our research project and second, requested recipients to provide relevant material and/or suggest further contacts in this regard. On the other side, we also compiled additional materials when establishing the methodology for the project (e. g. when testing different search string combinations at initial stages of the work), from the bibliographic alerts set up by the review team. All 1417 documents obtained from this process were entered in the database, with information on the person providing the document (Table  1 ;  30  ). Subsequently, 150 documents identified as not previously retrieved were considered relevant for further, full-text screening (Table  1 ). Extraction of cost estimates. The Online-only Table  1  comprises all the information of InvaCost that we mention further in this article, using simple quotation marks for 'Columns' of the database and italic letters for the different categories within each column.
org/indicator/FP. CPI. TOTL?end=2017&start=1960) of 2017 by the CPI of the year of the cost estimation. As an alternative, we also converted costs to 2017 US$ value based on PPP instead of the classical market exchange rates in the initial conversion step. PPP values were primarily collected from data provided by the World Bank (https://data. worldbank. org/indicator/PA. NUS. PPP?end=2017&start=1990), or by the OECD (https://data. oecd. org/conversion/purchasing-power-parities-ppp. htm) when information was not retrievable through the World Bank database. For this purpose, we had to deal with published costs that were expressed in currency that was different from the country where the costs were estimated (e. g. published cost in African countries expressed in US or Canadian $). Thus, prior to using PPP as a conversion index, we had to perform a retro-conversion by multiplying the original cost estimate by the official market exchange rate (local currency unit per currency unit used).