dataesr/scientific-paragraphs-categorization huggingface dataset:
, 1996; de Bello et al. , 2020; Damgaard, 2019). First, owing to their geographical position being kept "fixed" in the field, permanent plots prevent relocation bias, i. e. the error derived from trying to find the original plot location. This bias is inherent in vegetation resurveys  (Verheyen et al. , 2018). Second, the repeated collection of vegetation data from permanent plots provides broad benefits to our understanding of vegetation change, including the means to track detailed successional trajectories, monitor species interactions over time and assess the stability of the community as a whole. For this reason, permanent plots have been listed among the six most important developments in vegetation science over the past three decades  (Chytrý et al. , 2019). In recent decades, vegetation science has benefited from the development and maintenance of large vegetation databases  (Dengler et al.
, 2011). Historical vegetation relevés performed by early vegetation ecologists, together with recent vegetation-plot data stemming from regional, but also national or continental research and survey projects, have been carefully assembled and digitally archived in the context of centralized initiatives  (Bruelheide et al. , 2019; Chytrý et al. , 2016; Sabatini et al. , 2021; Wiser, 2016). Such global collections of vegetation-plot data are essential to investigate macroecological patterns and provide spatially meaningful answers to global issues, i. e. to effectively perform global-scale biodiversity research. In this context, a comparable effort specifically aimed at assembling and maintaining global databases built on time-series of vegetation data is urgently needed to lay a common ground for future studies focusing on: (a) providing global estimates of changes in plant diversity trends over time; (b) monitoring the conservation status of natural habitats over time; or (c) assessing the stability of ecosystem functions and services.
To the best of our knowledge, the BioTIME initiative  (Dornelas et al. , 2018)  represents the most important global collection of biodiversity time-series to date, including abundance records measured in species assemblages belonging to the marine, freshwater and terrestrial environments. Yet, the powerful spatial representation of BioTIME has limitations that include: (a) an often limited length and/or periodicity of the time-series, which particularly affects vegetation data (29 data sets with at least six data points); and (b) a poor focus on vegetation and terrestrial plant biodiversity (96 data sets, corresponding to about 27% of the whole database). Given that a high number of ecosystem functions and services strongly depend on plants  (Maestre et al. , 2012; van der Plas, 2019) , we deem it crucial for the fields of vegetation science and ecology to be able to rely on a consistent and standardized collection of data sets including high-quality time-series measured at regular intervals and specific to plant communities.
SPERANDII Et Al. Based on these premises, we hereby present the LOng-Term Vegetation Sampling (LOTVS) initiative, a growing global collection of vegetation time-series derived from the regular (mostly, annual) monitoring of plant species in permanent plots. By promoting the use, and supporting the visibility of high-quality temporal data collected using permanent plots, LOTVS ultimately aims to provide the tools to ask relevant ecological questions across a number of taxa, ecosystems and regions. The LOTVS collection provides a platform for aggregating the currently disconnected data sets sampled around the world based on permanent plots. As such, researchers are welcome to contribute to and, based on a scientific proposal (see Section 3. 2), use the available collection of data.
| DE SCRIP TI ON OF LOT VS As of December 2021, LOTVS encompasses 79 data sets (Figure  1 ) for a total of 7,789 vegetation time-series, collected using permanent plots that were monitored for a minimum of six, and a maximum of 99 years (first quartile: 10; mean: 17. 5; median: 16; third quartile: 23 years; see Figure  2 ). The vast majority of LOTVS time-series have a fine-grained temporal resolution: measurements in permanent plots were taken on 10%-100% of the temporal interval (with 100% meaning that plots were sampled at least annually; first quartile: 82. 8%; mean: 87. 4%; median: 100%; third quartile: 100%). A description of the single data sets can be found in  Valencia et al. (2020; supplementary material). This is also supported by online metadata on Zenodo The LOTVS collection contains data from five continents (Figure  1 ; an interactive map can be also accessed here and through the proposal template available at https://doi.
org/10. 5281/zenodo. 5807378), although Europe and North America are the most represented areas to date. Although Europe is the leading continent in terms of data sets (38 of 79), North America hosts the majority of plots (almost 70%, distributed across 30 data sets). Data sets included in LOTVS span a wide climatic gradient, their mean annual temperature ranging between -11. 5°C and 20. 1°C, and their mean annual precipitation between 140 and 2592 mm (source: WordClim 2;  Fick & Hijmans, 2017). As such, they are mostly included in the temperate seasonal forest, temperate grassland/desert and in the woodland/shrubland biomes (sensu  Whittaker, 1975 ; see.
04 to 400 m 2 ; ~80% of the plots range from 0. 04 to 1. 25 m 2 , with 1 m 2 being the most frequent (49%) plot size in the collection. Information on plot size is missing for 90 plots, corresponding to 0. 8% of the whole LOTVS collection. The method used to quantify species abundance also varies among the 79 data sets (Figure  3b ). The most frequent approach uses visual estimation of species cover (75%), followed by recording the frequency of individual species across a given number of subplots (17%), and third by the collection of above-ground biomass for each species in the plot (9%). In most cases, biomass clipping is intended to mimic mowing. All plots included in LOTVS were permanently marked in the field; geographic coordinates are available for either specific plots or for unique localities of each data set.
Besides estimating plant species abundance, some of the data sets within LOTVS also include information about bare ground cover or the abundance of other taxa (e. g. bryophytes, lichens). Taxonomic standardization is fundamental when compiling vegetation databases. This is a necessary step: (a) when addressing the issue of nomenclature redundancy, caused by the multitude of synonyms characterizing botanical literature  (Kalwij, 2012) ; and (b) to the ultimate goal of performing comparative analyses across data sets. It also allows a later integration of data with ancillary information linked to taxonomic entities, e. g. species functional traits. Original data sets included in LOTVS show considerable variation in the chosen taxonomical references reflecting F I G U R E 2 Distribution of the duration (in years) of the time-series included in LOTVS.
The number of time-series within each class is reported above the bars different regional and national traditions as well as the time when the work was undertaken. As such, a taxonomic standardization was deemed necessary. To do this, we standardized the nomenclature of plant species following The Plant List, currently the most widely used global reference list  (Kalwij, 2012). This was done in R (R Core Team, 2019) using the package Taxonstand  (Cayuela et al. , 2019) , which allows the automatic standardization of plant names by running an internal query to The Plant List (http://www. thepl antli st. org) and returning standardized species names, eventually resolving synonyms and homogenizing intraspecific taxonomic entities to the level of species. Nevertheless, a non-standardized version of the LOTVS collection, including (for each data set) the original nomenclature of the species, is also available for potential users (see Section 3.
| DATA USAG E As an unprecedented collection of vegetation time-series, LOTVS has huge potential to support timely and innovative research in the fields of vegetation science, plant ecology and temporal ecology. It should be noted, however, that installing and maintaining permanent plots is a very time-and resource-consuming task, and thus a powerful collection of vegetation plots such as LOTVS can only arise from collaborative efforts that stem from an impressive amount of work carried out by many data contributors, whose effort must be acknowledged. To this end, anyone can contribute to LOTVS with original data, as long as the data fully comply with LOTVS requirements (see Section 3. 2). At the same time, data included in LOTVS can be requested and, based on the accessibility level (see Section 3.
| Contributing data Detailed information on how to contribute to LOTVS, as well as specific data requirements can be found on the dedicated website (https://lotvs. csic. es/contr ibute/). LOTVS welcomes data sets including vegetation time-series collected from permanent plots with a fixed (i. e. permanently marked) geographical position in the field, possibly replicated in space, maintained for a minimum of 6 years and sampled at annual intervals. In principle, the time-series should be continuous, i. e. no gaps should be present. However, exceptions are allowed, provided that observations for some years are only missing for a reduced number of plots. In cases of missing years for all of the permanent plots, the new data set can only be incorporated to LOTVS if: (a) only a very limited number of years is missing; and (b) their distribution within the time-series is irregular, i.
e. LOTVS is not intended to accept permanent plots that, according to their original scope, are only sampled every n years. Following these requirements, we are also not looking to include data collected in the context of so-called resurveying studies, where historic vegetation plots are revisited after a longer period and re-recorded. In fact, these are the subject of other databases (e. g. the ReSurveyEurope initiative, http:// eurov eg. org/eva-datab ase-re-surve y-europe). Also, although data collected using different sampling approaches are welcomed (e. g. visual estimation of species cover, biomass, frequency, number of individuals), the sampling approach should be consistent over time. LOTVS does not plan, at present, to incorporate permanent plots that only record species occurrence (i.
e. presence/ absence data). To be included in LOTVS, permanent plots should be preferably representative of natural or semi-natural vegetation. The former can be defined as vegetation that developed in the absence of human influence and/or has long been left undisturbed by humans; as to the latter, its existence and maintenance depend on human practices (e. g. grazing or mowing) carried out for either production, conservation or a mix of the two purposes. As such, time-series data recorded from artificial seed mixtures such as those sown in biodiversity experiments are not currently accepted.
We note that about 65% of LOTVS data sets are publicly available, either because they belong to long-term ecological research (LTER) programs, or because they were archived and published by their data owners. Also, several of the LOTVS data sets are publicly available in their own right, via contact with their owners. Depending on the accessibility level specified, data owners and contributors hold (or not, in case of freely usable data) the right to request authorship on eventual publications based on the proposal submitted by the applicants when they request the data. In all cases, to request data included in LOTVS, a short and sound scientific proposal describing the aims BOX 1 Working definitions of the main habitat types and sampling methods mentioned. Habitat definitions were adapted from the Convention of Biological Diversity website (https://www.
I G U R E 1 Map showing the geographical location of the 79 data sets included in LOTVS. A more detailed view is given for areas featuring a high density of data sets: (a) Europe, (b) North America. ESRI World Satellite Imagery was used as the base map. For a subset of sites, representative vegetation types are shown. Photos were taken at: (left, starting from the top): Soebatsfontein (South Africa), Bayreuth (Germany), Roquefort-sur-Soulzon (France), McLaughlin Natural Reserve (California), Santa Rita Experimental Range (Arizona); (right, starting from the top): Laikipia (Kenya), Göttinger Wald (Germany), Krkonoše Mountains (Czech Republic), Ohrazení (Czech Republic), Hays (Kansas) //doi. org/10. 5281/zenodo. 5807378; see Section 3. 3). At present, LOTVS includes vegetation time-series specifically focused on herbaceous species and shrubs mostly belonging to grassland habitats, followed by mixed vegetation types (i.
of the project and the type of data required should be prepared and submitted to the LOTVS supervising committee. This process is intended to: (a) minimize conceptual overlap of proposals addressing highly similar research questions; and (b) make sure that all data owners are informed about the possible use of their data and are free to decline it if they wish so. To help potential users become familiar with the data and facilitate data requests, we provide both a metadata sheet describing the LOTVS data sets and a proposal template (both available on Zenodo: https://doi. org/10. 5281/zenodo. 5807378). This will be updated every time new data sets are added to the LOTVS collection (or current data sets are updated). This metadata sheet provides a brief description of each data set and contains information on several features (e.
Day: Te Apārangi Royal Society of New Zealand (Rutherford Postdoctoral Fellowship). Jiří Danihelka: Czech Science Foundation (project no. 19-28491X) and Czech Academy of Sciences (project no. RVO 67985939). Francesco de Bello: Spanish Plan Nacional de I+D+i (project PGC2018-099027-B-I00). Eric Garnier: La Fage INRA experimental station. Tomáš Herben: GAČR grant 20-02901S. Anke Jentsch: German Federal Ministry of Education and Research (grant 031B0516C -SUSALPS) and Oberfrankenstiftung (grant OFS FP00237). Norbert Juergens: German Federal Ministry of Education and Research (grant 01LG1201N -SASSCAL ABC). Frédérique Louault and Katja Klumpp: AnaEE-France (ANR-11-INBS-0001). Robin J. Pakeman: Strategic Research Programme of the Scottish Government's Rural and Environment Science and Analytical Services Division.
Funding information  NERC  and  BBSRC , Grant/Award Number:  NE/N018125/1 ;  2017 program  for attracting and retaining talent of  Comunidad de Madrid , Grant/ Award Number:  2017-T2/AMB-5406 ;  European Research Council , Grant/ Award Number:  ERC-SyG-2013-610028 ;  German Federal Ministry of Education and Research (BMBF) , Grant/Award Number:  01LG1201N  ( SASSCAL ABC );  New Zealand Ministry for Business, Innovation and Employment; Community of Madrid and Rey Juan Carlos University; AnaEE-France , Grant/Award Number:  ANR-11-INBS-0001 ;  Fundación Ramon Areces ;  GAČR, Grant/Award Number :  20-02901S  and  19-28491X ;  Agencia Estatal de Investigación -Spanish Plan Nacional de I+D+i , Grant/Award Number:  PGC2018-099027-B-I00 ;  Spanish Government , Grant/Award Number:  PID2019-110521GB-I00 ; European.
DATA AVA I L A B I L I T Y S TAT E M E N T The data used to produce the graphs in this manuscript are available as metadata stored in Zenodo (https://doi. org/10. 5281/zenodo. 5807378). The data sets in the LOTVS collection are available, according to their accessibility level, upon the submission of a scientific proposal to the LOTVS' Supervising Committee (see Section 3. 2).
F I G U R E 1 Map showing the geographical location of the 79 data sets included in LOTVS. A more detailed view is given for areas featuring a high density of data sets: (a) Europe, (b) North America. ESRI World Satellite Imagery was used as the base map. For a subset of sites, representative vegetation types are shown. Photos were taken at: (left, starting from the top): Soebatsfontein (South Africa), Bayreuth (Germany), Roquefort-sur-Soulzon (France), McLaughlin Natural Reserve (California), Santa Rita Experimental Range (Arizona); (right, starting from the top): Laikipia (Kenya), Göttinger Wald (Germany), Krkonoše Mountains (Czech Republic), Ohrazení (Czech Republic), Hays (Kansas) //doi. org/10. 5281/zenodo. 5807378; see Section 3. 3). At present, LOTVS includes vegetation time-series specifically focused on herbaceous species and shrubs mostly belonging to grassland habitats, followed by mixed vegetation types (i.
J. Day: Te Apārangi Royal Society of New Zealand (Rutherford Postdoctoral Fellowship). Jiří Danihelka: Czech Science Foundation (project no. 19-28491X) and Czech Academy of Sciences (project no. RVO 67985939). Francesco de Bello: Spanish Plan Nacional de I+D+i (project PGC2018-099027-B-I00). Eric Garnier: La Fage INRA experimental station. Tomáš Herben: GAČR grant 20-02901S. Anke Jentsch: German Federal Ministry of Education and Research (grant 031B0516C -SUSALPS) and Oberfrankenstiftung (grant OFS FP00237). Norbert Juergens: German Federal Ministry of Education and Research (grant 01LG1201N -SASSCAL ABC). Frédérique Louault and Katja Klumpp: AnaEE-France (ANR-11-INBS-0001). Robin J. Pakeman: Strategic Research Programme of the Scottish Government's Rural and Environment Science and Analytical Services Division.
Primer design Based on a nucleotide alignment using MEGA software version 7 (34) of all available AMPV full length sequences on the database, a zone in the L gene (position 1980-2140) that allowed for the design of one single forward and one single reverse primer that were common to all subgroups and for the design of subgroups specific probes was selected (Table  1 ). Primers and probes were evaluated in silico using OligoAnalyzer Tool (https://eu. idtdna. com/pages/tools/ oligoanalyzer) for secondary structures and self-and heterodimers. Specificity was investigated using Primer-BLAST tool. Probes were double-quenched with 3 ′ Iowa Black FQ quencher and an internal ZEN quencher to decrease the background. Probes for AMPV-A, B and D used 5 ′ 6-carboxyfluorescein (FAM) and probes for AMPV-C and an endogenous reference gene, the house keeping gene GAPDH (Table  1 ) used 5 ′ hexachloro-fluorescein phosphoramidite (HEX).
Data analysis Droplet clusters were evaluated in 2D plots as shown in Figure  1  using QuantaSoft software version 1. 7. 4 Bio-Rad, France). A cut-off amplitude threshold, set at ∼6,000 for CH1 and 3,200 for CH2, distinguished positive droplets (one or more than one copy of the target) from negative droplets (devoid of target molecules). Ten or more positive droplets was considered as a positive result. The mean amplitude values for positive clusters in CH1 and CH2 for test samples were compared with those of AMPV reference strains to assign the AMPV subgroup. An absolute quantification of the clusters was performed.
Methodology used in ToR 2 To answer ToR 2, an extensive literature search (ELS) was outsourced by EFSA (OC/EFSA/ALPHA/ 2020/02 -LOT 2). The aim of this ELS was to answer the epidemiological question: 'what is the average, shortest and longest period of time (measured as the number of days from the earliest point of infection with CCPP to the time of declaration of a suspicion by the competent authority after the clinical investigation by an official veterinarian) for an outbreak of CCPP to be reported'. To answer this question, an ELS on case reports, papers describing outbreaks or epidemics of CCPP and any other relevant grey literature or data was carried out. For inclusion in the ELS, the earliest point of infection had to have been estimated through an epidemiological investigation. Papers and other sources of data were excluded, when the earliest point of infection was determined purely by subtracting a known incubation period from the date of the suspicion of the outbreak.
Extensive Literature Search A search was carried out on 21/5/2021, identifying 48 unique references published after 1/1/2000. As no references were available for outbreak data from the EU/EEA, the search was extended to data from the rest of the world and to simulation data. Among the 48 references, three were selected to be included in the qualitative review. The full selection process is displayed in Figure  6. One of the three references reported dates instead of periods; therefore, the dates were used to calculate the different periods of interest (as described in Section 2. 1 -PICOS table). No data was retrieved for the main outcome of interest, i. e. the period between the earliest point of infection and the suspicion report. The extracted data for other periods that are included in the outbreak reporting process (i.
, 2005). In addition, we reconstructed the period between the earliest point of infection and the suspicion report by adding together the incubation period  (OIE, 2009)  and the period between the first suspicion and suspicion report found in the context of the captive sand gazelle outbreak which took place in 2013 in the United Arab Emirates  (Lignereux et al. , 2018) : 2. 1. Average period = mean incubation period (22 days), plus period between first suspicion and suspicion report (2 days) = 24 days. 2. 2. Shortest period = min incubation period = 3 days. 2. 3. Longest period =max incubation period (41 days), plus period between first suspicion and suspicion report (2 days) = 43 days. However, since no data was available in the literature with respect to the period between the earliest point of infection and the suspicion report, it is not possible to provide an objective conclusion on the current monitoring period for CCPP (45 days) based only on the evidence found by ELS.
In a CCPP-affected establishment, the following considerations should be taken into account when designing derogations from killing animals: i) the lack of specificity of clinical examination; ii) animals without clinical signs may be incubating CCPP which cannot be detected by laboratory tests (the incubation period is usually 6-10 days, but varies from 2 days to 4 weeks); iii) some animals may become 'carriers' following their exposure and may remain a source of the Mccp; iv) the length of infectious period is not known; v)data on sensitivity and specificity of diagnostic tests are sparse, complicating interpretation of test results and estimates of predictive values; All the animals intended for derogation from killing should be subjected to thorough individual clinical examination and samples for laboratory examination with serological tests (c-ELISA, LAT) should be collected from all the animals irrespective of the presence of clinical signs.
g. if the index case was in a slaughterhouse and the epidemiological enquiry is taking time). No transmission kernels either specific for CCPP or for diseases that have similar transmission routes to CCPP were found in the literature, nor were data suitable to estimate kernels identified. Accordingly, the zone sizes for CCPP were assessed using expert's knowledge. The causative agent can be transmitted by air, and therefore, animals in adjacent establishments can be infected. Based on the WG expert opinion, the absence of a protection zone is not considered effective while the defined minimum radii of 3 km of the A protection zone including the establishments adjacent to the infected establishment or establishments with pastures or yards adjacent to the infected one (e. g. 1 km zone depending on the local situation and the farms distribution) since this would prevent disease spread (90-100% certainty).