dataesr/scientific-paragraphs-categorization huggingface dataset:
A. Rouillard ,  V. Génot ,  M. Janvier ,  Elie Soubrier ,  F. Auchère ,  E. Buchlin  and  E. Pariat  acknowledge support from the  French space agency (Centre National des Etudes Spatiales ; CNES; https://cnes. fr/fr)that funds activity in plasma physics data center ( Centre de Données de la Physique des Plasmas ; CDPP; http://cdpp. eu/)and the  Multi Experiment Data and Operation Center  (MEDOC; https://idoc. ias. u-psud. fr/MEDOC),and the space weather team in Toulouse (Solar-Terrestrial Observations and Modelling Service; STORMS; https://stormsweb. irap. omp. eu/). This includes funding for  GAIA-DEM , the data mining tools AMDA (http://amda. cdpp. eu/),CLWEB (clweb. cesr. fr/)and the propagation tool (http://propagationtool. cdpp. eu). R. Pinto ,  M. Lavarra ,  Y. Wu  and  A. Kouloumvakos  acknowledge financial support from the  ANR  project  SLOW_   SOURCE  No  ANR-17-CE31-0006 -01 ,  ANR  project  COROSHOCK  No  ANR -18 -ERC1 -0006 -01  and  FP7   HELCATS  project https://www.
helcats-fp7. eu/under the  FP7 EU  contract number  606692. A. Warmuth  acknowledges the support by  DLR  under grant No. 50QL0001. The STEREO SECCHI data are produced by a consortium of  RAL (UK) ,  NRL (USA) ,  LMSAL (USA) ,  GSFC (USA) ,  MPS (Germany) ,  CSL (Belgium) ,  IOTA (France)  and  IAS (France). The ACE data were obtained from the  ACE science center. The WIND data were obtained from the  Space Physics Data Facility. Javier Rodriguez-Pacheco  acknowledges  Spanish  Project:  FEDER/MCIU-AEI/Project ESP2017-88436-R.
Erosion of biodiversity generated by anthropogenic activities has been studied for decades and in many areas at the species level, using taxa monitoring. In contrast, genetic erosion within species has rarely been tracked, and is often studied by inferring past population dynamics from contemporaneous estimators. An alternative to such inferences is the direct examination of past genes, by analyzing museum collection specimens. While providing direct access to genetic variation over time, historical DNA is usually not optimally preserved, and it is necessary to apply genotyping methods based on hybridization-capture to unravel past genetic variation. In this manuscript, we apply such a method, i. e. , HyRAD, to large time series of two butterfly species in Finland, and present a new bioinformatic pipeline, namely PopHyRAD, that standardizes and optimizes the analysis of HyRAD data at the within-species level.
The same holds true for Lycaena helle, an arcticalpine Palearctic species inhabiting fresh, damp meadows, which shows a patchy distribution throughout Europe and has declined throughout most of its range, especially in Finland, where it remains present in only two sites, Kuivaniemi Simo and Kuusamo  (Heino, Poykko, & Itames, 1998). In this study, we aim to examine how the genetic structures and diversities of E. embla and L. helle have been impacted by recent environmental changes and human activities, relying on HyRAD data generated from 118 and 165 museum specimens, respectively, collected across the last century as well as from extant populations. To achieve this, we developed a pipeline, namely PopHyRAD, that (1) aligns each sequence read against the probe catalog (including reference genomes when available, or probe sequences provided by users), (2) identifies and controls for deamination patterns (typical of historical DNA), (3) eliminates putative paralogs, PCR duplicates, low quality genotypes and indels, and; (4) keeps only bi-allelic loci for downstream analyses.
popHyRAD bioinformatic pipeline The sequence pairs generated from the ddRAD probe libraries, were first cleaned and overlapping reads were collapsed using AdapterRemovalv2  (Schubert, Lindgreen, & Orlando, 2016). Then a first dataset reduction was performed following the first step of the dDocent pipeline  (Puritz, Hollenbeck, & Gold, 2014) , keeping only alleles covered by at least four reads and, thus, removing the vast majority of sequencing errors. We merged intra-individual loci using cd-hit-est from the CD-HIT tool  (Fu, Niu, Zhu, Wu, & Li, 2012 ) using a minimum identity threshold of 90%. This step was repeated across samples to produce a combined catalog of all loci identified for a given species. Loci shared by at least half of the probe samples (i. e. three out of six) were kept to remove uninformative specific loci.
The absence of contamination in the catalog was evaluated using Centrifuge  (Kim, Song, Breitwieser, & Salzberg, 2016). Reads from each historical sample were cleaned using Trimmomatic  (Bolger, Lohse, & Usadel, 2014)  and individually mapped on the loci catalog generated above using bwa aln  (Li & Durbin, 2009). PCR duplicates were removed using MarkDuplicates from the Picard toolkit (http://broadinstitute. github. io/picard). Nucleotide mis-incorporation patterns were investigated using MapDamage2. 0  (Jónsson, Ginolhac, Schubert, Johnson, & Orlando, 2013) , and base quality scores were rescaled according to their probability to represent a post-mortem DNA deamination event in order to reduce the impact of DNA decay on downstream analyses. Finally, individual genotypes were called using freeBayes  (Garrison & Marth, 2012) , and considered for further analyses when (1) showing qualities higher than 100, (2) shared by at least 80% of the samples, and (3) biallelic (Figure  1 ).
Population genetics analyses Allelic frequencies, Observed gene diversity (Hs) and Inbreeding level (FIS) were estimated using the R package hierfstat  (Goudet, 2005). The frequency of fixed alleles, which is a proxy of the frequency of allele extinction, was estimated using a custom script. For these estimations only localities with more than 8 samples were kept. These estimations were performed after the samples were binned within temporal groups (or time slices). More precisely, we merged samples from each geographic location within three time slices  : before 1950, between 1950 and 2000 and present samples (i. e. 2015 ; see details in Table  1 ). Additionally, we merged all samples from all locations within the same temporal bins to identify patterns at the whole scale of Finland. Population genetic structure was investigated using a subset of SNPs, where only one SNP per locus was considered so as to minimize linkage disequilibrium (i.
Isolation-by-distance was tested by examining the correlation between genetic differentiation, FST/1-FST, using FST estimations between population pairs performed with hierfstat  (Goudet, 2005) , and the log-transformed distance, as suggested by  Rousset (1997). Due to the ongoing debate in using Mantel tests for IBD examination  (Diniz-Filho et al. , 2013) , we used a simple Spearman's correlation. To corroborate the role of geographic distance as well as time in the differentiation between localities by using another approach than a simple Spearman's correlation, we also performed distance-based redundancy analysis (dbRDA) integrating geographic variables and year of collection, in an individual-based approach, following recommendations from Laura Benestan (unpublished data; https://github.
com/laurabenestan/db-RDA-and-db-MEM). First, we created spatial variables using Moran Eigenvector's Maps (MEMs) implemented in adespatial R package  (Dray, Blanchet, Borcard, Guenard, & Jombart, 2016). Second, a Principal Coordinates Analysis (PCoA) was performed on the Euclidean genetic distances based on genotypes of each sample. Finally a global dbRDA was applied by integrating the year of collection as an additional variable to spatial components, and an ANOVA with 1000 permutations was performed to assess significance of each variable within the model using vegan R package  (Oksanen, Kindt, Legendre, & O'Hara, 2016).
HyRAD efficiency The HyRAD wetlab and popHyRAD bioinformatic pipeline developed for this study were particularly efficient in retrieving DNA sequences of historical samples collected up to more than 100 years ago and stored in museum conditions. The analysis of ddRAD data used for designing the probes allowed to obtain sufficient data at 3,826 loci for E. embla and 3,443 for L. helle, which allowed us to undertake population genomic analyses. In addition, the identification of deamination patterns at the extremities of the reads from the collection samples and not in the fresh samples confirmed the endogenous nature of DNA present in these historical samples (Supplementary Figure  1 ).
Mapping and SNPs calling comparison We first aimed at testing whether different read alignment procedures could impact on the amount of HyRAD data retrieved. Bwa aln was able to align only a limited number of reads against the catalog of HyRAD probes, representing a mean of 11. 2% (sd 6. 7) across all samples This proportion was improved to 37. 1% (sd 8. 1) when using bwa mem (Supplementary Table  2 ). Bowtie2 showed intermediate efficiency with a mean value of 18. 3% (sd 7. 9) across all samples. These differences, however, decreased after quality filtering, with bwa aln showing 6. 9% (sd 2. 0) of mapped reads , bwa mem 12. 1% (sd 4. 8) and bowtie2 11. 0% (sd 2. 8). We also observed broad differences between methodologies aimed at calling genotypes. We counted the total number of positions covered by at least 80% of the samples and showing biallelic SNP polymorphisms.
Patterns of population declines To investigate genetic patterns associated with population size reduction, we focused our analyses on three descriptive statistics, (i) the observed gene diversity (Hs), (ii) the frequency of fixed alleles, and (iii) the inbreeding coefficient (FIS). Results are depicted in Figure  3. For E. embla, sufficient temporal data could only be retrieved for three localities, i. e. Kuusamo, Muonio and Pelkosenniemi. These indicated reduction of the observed gene diversity (Hs) and the frequency of fixed alleles through time. This overall reduction is, paradoxically, associated with a sharp increase of the inbreeding coefficient at only one locality, i. e. Kuusamo. For L. helle, only one locality, i. e. Kuusamo Liikasenvaara, was sampled at different time points. It showed a dynamic diversity trend where the observed gene diversity (Hs) and the frequency of fixed alleles were relatively stable between the 1920s and the 1990s, but declined after the 1990s.
In this study, we collected a large sample set of two butterfly species spread across Finland, and spanning the last ~120 years. This sampling provided us with an unique opportunity to quantify the variation of the genetic diversity in both species at a time when their distribution drastically declined  (Rassi et al. , 2010). We have benefited from the HyRAD genome-complexity-reduction method to obtain genetic data from these valuable samples. HyRAD has been increasingly used in different labs, not only to identify genetic variation in historical material  (Crates et al. 2019; Keighley et al. 2019; Linck et al. , 2017; Linck et al. 2019; Schmid et al. , 2017) , but also in ancient DNA  (Schmid et al. , 2017). Indeed, these methods based on hybridization capture allow to retrieve very small quantities of degraded DNA, which often remain unquantifiable before capture (Supplementary Table  1 ).
Acknowledgements We are grateful to  Christophe Dufresnes ,  Tomasz Suchan ,  Camille Pitteloud  and  Kimmo Saarinen  for their invaluable help in the field, to  Nils Arrigo  for bioinformatic support, to  Stéphanie Manel  for advice in distance-based redundancy analysis, and to  Jérôme Goudet  for support during early development of the PopHyRAD pipeline and for commenting on this manuscript. We thank the.
http://doi. org/10. 5281/zenodo. 3668644 for Erebia embla and http://doi. org/10. 5281/zenodo. 3668660 for Lycaena helle. Scripts for the whole analytic process have been uploaded to Github (https://github. com/JeremyLGauthier/Scripts_Gauthier_et. al_2019_MER). The PopHyRAD pipeline is constantly under development and improvement. Current version can be found at https://github. com/JeremyLGauthier/PHyRAD).
Step Three: Understand Stakeholder Objectives Vouga estuary Natura 2000 site governance involves a multiplicity of institutions, organisations and stakeholders, and involves articulation of programs and plans of local, regional and national levels  (Teles et al. 2014; Fidélis and Carvalho 2015; Lillebø et al. (eds) 2015; Sousa et al. 2016; Sousa 2017; Fidélis et al. 2019). The Portuguese Environment Agency, through its Regional Hydrographic Administration for Portugal Centro Region (APA/ARH Centro) was engaged at a very early stage of the project, helping identify key management questions. Other stakeholders engaged at the kick-off stage of the work and contributed to the datasets that support scenario development include the Institute for Nature Conservation and Forests (ICNF, I. P. ), Regional Directorate for Agriculture and Fisheries (DRAP Centro), Hydrographic Institute (IH), and Directorate-General for Marine Resources (DGRM).
Step Four: Understand the Social-Ecological System The assessment of Vouga estuary's current state included the identification of habitats, specific public and private primary human activities, and respective pressures in the entire Natura 2000 territory. To address the ecological perspective of the system, data sources from scientific publications, projects (e. g. , LAGOONS EU FP7; ADAPT-MED EU FP7 ERA-NET; LTER-RAVE FCT, AQUACROSS EU H2020), national agencies (e. g. , above mentioned), online platforms (e. g. , Copernicus datasets) and from national/regional official reports were integrated. In order to harmonise habitats classification, all data sets (mainly following Annex I of EU Habitats Directive;  Sousa et al. 2016)   ). The potential of a given habitat to supply ecosystem services was attained using a lookup table on the contribution of each EUNIS habitat compiled based on expert judgment  (Teixeira et al.
2019). The identified ecosystem services were aggregated into eleven ecosystem services in order to enable spatial multi-criteria analysis by stakeholders (Table  4 ). This table includes the correspondence code from CICES v4. 3 to 5. 1, considering the identified services for the considered territory, as well as selected aggregation of services used in the scope of the participatory moments in order to optimize communication and active participation of stakeholders. Analysis of ecosystem services valuations (Martínez-López et al. 2019a) revealed two major stakeholder opinion groups, stating within group more similar preferences regarding ecosystem services, and whose composition was heterogeneous and not related to specific stakeholder groups identified. Weights in the spatial multi-criteria analysis in the selected Natura 2000 area took into account the mean of the ecosystem services scores given by individuals in the same opinion group.
EBM incorporates adaptive management The proposed measures, namely the habitats restoration measures, follow principles of resilience thinking and adaptive management, by considering ecology, management of natural capital and systems analysis. Source:  Lillebø et al. (2019)  Additionally, the relevance of these coastal wetlands as nursery areas, which support important economic activities in the region, is acknowledged by local populations  (Dolbeth et al. 2016; Newton et al. 2018; Lillebø et al. 2019). For the implementation, relevant EU funding instruments might be considered, namely R&I H2020 and the following Horizon Europe programmes, LIFE environmental programme, as well as Regional Development and/or Territorial Cooperation funds  (Marino et al. 2014; UE 2016). Both measures to restore tidal wetlands have the same policy target (i.
collaborative research project AQUACROSS (Grant Agreement no. 642317). Thanks, are also due to the Portuguese Foundation for Science and Technology (FCT) for the financial support to CESAM (UID/AMB/50017/2019; UIDP/50017/2020+UIDB/50017/2020). Ana Genua-Olmedo was funded by the project PORBIOTA-Portuguese E-Infrastructure for Information and Research on Biodiversity (POCI-01-0145-FEDER-022127), financed by FCT through PIDAC national funds and co-funded by the FEDER. Special thanks to Mariana Morgado for her work in the cartographic data collection, and to stakeholders participating at the workshops for their valuable contribution.
According to its strategic long-term vision, Europe wants to be a climate-neutral economy by 2050. Buildings play a crucial role in this vision, and they represent a sector with low-cost opportunities for high-level CO 2 reduction. The challenge the renovation of the existing building stock, which must be increased to 3%/year, more than double compared to the current 1. 2%/year. In this context, the ALliance for Deep RENovation (ALDREN) project has the goal of encouraging property owners to undertake renovation of existing buildings using a clear, robust, and comparable method. This paper aims to present the ALDREN approach and the ALDREN Building Renovation Passport (BRP), giving an overview of the connections and data links to other existing databases and certification schemes. To understand the data value potential of buildings, one requires reliable and trustworthy information.
The Building Renovation Passport, introduced by the recent Energy Performance Building Directive (EPBD) recast 844/2018/EU, aims to provide this information. This paper presents the experience of the ALDREN BRP for non-residential buildings as well as the development procedure for its data model and the potential that this tool could have for the construction market. The ALDREN BRP has been structured into two main parts-BuildLog and RenoMap-with a common language that facilitates communication on the one hand and, on the other, the setting of renovation targets based on lifetime, operation, and user needs.
e. , the asset value of the building), in line with the development of a Building Renovation Passport (BRP) for non-residential buildings (especially offices and hotels) as a complementary tool to the Energy Performance Certificate (EPC). The ALDREN BRP will include a tangible long-term vision for national renovation strategies with a link to finance instruments and minimum renovation targets, offering a secure pathway that avoids the lock-in effect towards CO 2 reduction. Long-term planning with intermediate targets would be very helpful for owners and stakeholders, increasing the confidence required to invest in renovation. The BRP will also be used for data collection regarding building characteristics, energy efficiency, and comfort levels, overcoming the lack of information that typically affects our buildings.
6 is then sub-structured in three respective subtasks: Task 2. 6. 1 Overview of the available knowledge and information; Task 2. 6. 2 Rendering and structuring of all the collected data and knowledge; Task 2. 6. 3 Building Passport (BP) form definition. This paper presents the activities related to the analysis performed on the available knowledge of non-residential buildings, and a critical review of the results that lead to the definition of the data model structure for the ALDREN BRP. The overview of existing databases and similar ongoing initiatives regarding BRP is a very important task to compare the ALDREN BRP with comparable data, recognized standards, and compatibility with existing databases at national and European levels. This work aims to present the ALDREN approach and the ALDREN BRP, focusing on the connections and data links with other existing databases, certification schemes, and the potential that this service could have for the market.
The paper is structured as follows: Section 1 introduces first the ALDREN project in a nutshell and then focuses on the ALDREN concept and the presentation in more detail of Task 2. 6 goals, the core of the paper; Section 2 sets the scene for the main topic data in the construction sector, providing an introduction to definitions, terminology, and criteria for the database analysis performed in detail in Section 3. The overview of the available knowledge then focuses on the currently ongoing European BRP initiatives and is presented in Section 4. Section 5, starting from the lessons learned from previous sections, presents the Data Model (DM) structure of the ALDREN BRP. Finally, conclusions and recommendations for next steps are made in Section 7.
Data terminology and Criteria for the Evaluation of Existing Buildings Database in Europe As the actual value of data continues to grow, the limits of its prospective value are pushed every day. In 2017, the Economist  [2]  published a widely referenced article claiming that oil had been replaced as the world's most valuable resource by data-specifically, data captured from users by tech companies. Similarly, in the construction industry, data refers to the huge quantities of information that have been stored in the past and that continue to be acquired today. Data can come from everywhere, for example, people, sensors, machines, and any other data-generating devices, and they can be replicated indefinitely and moved around the world speedily, cheaply, and through the net. Building construction data already exist in all the plans and records of anything that has ever been built.
This is equally true for the evaluation of energy programs and policies. Evaluators typically will try to leverage as much available data, databases, and studies as possible to support their evaluations. This section presents some key database concepts and their corresponding definitions, considering the basic structure of the Data, Information, Knowledge, and Wisdom (DIKW) pyramid. Like other hierarchy models, the DIKW pyramid has rigidly set building blocks-data comes first, information is next, then knowledge follows, and finally wisdom is on the top. Each step up the pyramid answers questions about the initial data and adds value to them. The selection and the description of the terms have been studied and individuated from the literature for this topic  [3, 4]  and summarized in Table  1.
This selection was performed with two objectives: (i) Providing a jumping off point for those interested in learning more about databases and their design and management; (ii) Focusing on the concept that affected the definition of the ALDREN BRP data model. Nowadays there are multiple databases related to building characteristics and stocks. The building's data are collected by different institutions (i. e. , statistics offices, energy agencies, consultancy companies, research organization, others) mainly on the member states (MS) level; its quality, availability, and completeness vary significantly between the different countries  [1]. An overview of a selection of existing databases has been conducted with the aim to individuate their main characteristics and to understand which of them can be used, linked, and connected to support the ALDREN methodology for the BRP definition and the assessment of energy interventions in existing non-residential buildings.
Code Criteria Description A Organization of the data Data collected can be stored and organized in different ways. Typical choices for data organization are the following. • Relational database: in this database the raw data is organized into sets of tables, and into relations between them: • NoSQL database: NoSQL is an umbrella term, which encompasses several different technologies that are not relational in nature. This lack of relational structure results in unstructured or semi-structured data in storage. • No specific organization: data are stored without a specific machine-readable organization criterion.
B Organization of the database Databases for data collection can be stored in different ways. Focusing on the Energy Performance Certificate (EPC) register, the most common database organization can be as listed following: • National database: a national building database exists and all the EPCs are collected in this unique database (DB); • Regional database: a regional building database exists and all the EPCs are collected in this unique DB; • Multiple databases: multiple databases exist (both at a national and regional level) for each specific EPC; • No collection in database: EPCs are not collected in a database.