dataesr/scientific-paragraphs-categorization huggingface dataset:
Other crowdsourced information can also be used to infer building use. In Fonte et al. (2018), crowdsourced data from Facebook and Foursquare were additionally used to fill in some of the gaps from OSM regarding building use. Using only Foursquare, Spyratos et al. (2017) classified buildings in Amsterdam according to building use types. The best results were obtained for hotels, restaurants, cafes, and retail establishments while such an approach is less suited for identification of industrial use. Finally, as outlined above, a bespoke application could be built to crowdsource building use from photographs or collected by volunteers on the ground using a mobile app. An application specifically developed for crowdsourcing building use (and other land cover/land use attributes) has been developed by IIASA and IGN France called PAYSAGES, which sends volunteers to specific buildings within a city and asks them to provide information on building function along with a photograph of the building (Olteanu-Raimond et al.
Methodologies to collect vegetation parameters at mesoscale Currently, no global data are available to describe urban vegetation in terms of coverage and characteristics. At best, through local administrative databases or open data, it is possible to build land use maps for vegetated surfaces, often with an incomplete coverage, and without the distinction of strata (even if street trees are sometimes listed). Furthermore, the geometric and physiological parameters inherent to vegetation species are unknown. Bare soils are rarely mapped. The properties of urban soils, which are very heterogeneous because they have been significantly reworked, are also not documented in such administrative databases. However, the land cover map obtained by this approach may show an incomplete representation of vegetated surfaces (Figure  9b ) compared to reality (Figure  9a ) and without distinction of vegetation strata.
This results in a significant proportion of urban surfaces that remain undefined, and on which the modeller will have to make assumptions to simulate the urban micro-climate. For street trees, data may be available for some cities via local authorities or through new initiatives based on open data, such as that of Treepedia developed by the MIT Senseable City Lab (2018), which offers a Green Vegetation Index (coverage of urban surfaces by tree crowns) based on Google Street View panoramas  (Li et al. , 2015; Seiferling et al. , 2017). For some European cities, data on urban tree coverage is also available through the Copernicus Urban Atlas portal (EEA, 2018). Moreover, some individual cities have decided to provide various city-descriptive data sets freely, including trees. For example, the city of Berlin, Germany, has developed the online data browser FIS-Broker.
Licensing issues Urban data from authorities, i. e. , public organizations mandated to organize the coproduction and maintenance of data required for public action, are usually available for free, at least for research but not always under free licenses. Some countries, regions, or cities already provide data for free, accessible from data platforms (e. g. France, Berlin (Germany), Hamburg (Germany), Federal State of North-Rhine-Westfalia (Germany), Helsinki (Finland)). Other cities provide the data only under certain conditions and restrictions. In France, the BD-TOPO national database (IGN, 2018) is available for free for research, education and public missions. It is generally used to construct urban land use maps. Administrations of large cities generally possess more precise cadaster data than information covering national or international territories.
Such administrative data can also cover many aspects including morphological, socio-economic, architectural, financial and administrative features. To use such data sets can lead to a large workload because of the city-specific data form and content. Even if GIS-based vector maps can be easily transformed into other data formats, their adaptation for use in a UCM requires effort and programming skills. Furthermore, the exploitation of some data, especially for ORM models that require data at the building scale, can require formal authorization for use due to data protection of people's privacy (e. g. , as required by the European General Data Protection Regulation (GDPR) enforced on 25 May 2018). The area of data licenses is a complex domain. Recently Google has launched a prototype search engine that is dedicated to earth science raster data sets called Google Dataset Search.
This flexibility has highlighted the issue of the trade-off between spatial and semantic (thematic) resolution. In urban models that contain both physical and socioeconomic dimensions, pushing the spatial resolution above or below certain levels is not always advisable, because some socio-economic phenomena do not occur at very fine or at very coarse scales; forcing them to very large or very small scales would misrepresent human dynamics (see e. g. , Votsis, 2017). ORMs using a grid mesh of less than 10 m can take many characteristics of the urban surface into account so reaching this amount of detail is almost infeasible at the city level, e. g. the shape of the trees. Until detailed data sets such as CityGML or LiDAR point clouds become more easily accessible, generalizations and assumptions will need to be applied to these models.
Open data Accessing city-descriptive data from different providers to design an urban climate model experiment that is replicable in Europe typically requires accessing different data repositories, which could have different licensing schemes, and to become familiar with different implementations. As this increases the costs (in terms of time and effort) of using these data sets, a trade-off needs to be made between accuracy and detail. This has been addressed by the OSM project in which a unified framework has been proposed to collaboratively build an open database, largely from integrating existing open data in different countries. The geographic features represented in this database (e. g. , buildings, highways, land use, etc. ) are organized in a relational database model by a set of elements (NODES, WAYS and RELATIONS).
Each element is described by tags (key=value). For example, a building is described by a WAY element (with at least one "building=yes" key-value 10  ), which refers to a NODE collection (each node represents a coordinate in latitude and longitude). Since its creation in 2004, OSM has gained popularity and grown to over 1 million map contributors and more than 1 million registered users. OSM's success lies in the flexibility of its data model and project operation. The community is voluntary so adherence to semantic tagging is also voluntary. These lack of restrictions, the absence of procedures to capture data and quality control, plus the use of a non-standard data model, have created some difficulties when using OSM data in GIS applications. Extracting OSM data and adapting them to user needs is a current topic of research interest as it requires consideration of data matching, web semantics, data fusion, data quality, etc.
Many other research or operational communities gather and use urban data, such as Building Information Modelling. To incorporate such data, the atmospheric modelling community will need to use data standards from other scientific fields. However, this raises the issue of being able to use information not initially built by or for urban climatologists and modelers. The use of open databases, and more generally the development of open data policies at state and world levels, should be encouraged. 4. Finally, exploration of new techniques such as crowdsourcing and deep-learning should be encouraged for the production of any type of urban parameter. Such methods should ideally be conceived in order to be transferable where data are lacking, eventually going as far as reconstructing plausible city information if necessary.
From data of various origins to Urban Climate Services The trade-off between spatial and thematic resolution is complemented by the trade-off between regular and irregular geometry when representing the urban area. In particular, some types of socioeconomic information are not well-represented in a pixel-based format, but require irregular polygons for representation, e. g. , postcode polygons. Such information could be rasterized and harmonized with other pixel-based information, but this nevertheless creates an artificially high resolution and misrepresents values along the boundaries between polygons. Studies within the FP7 project ToPDAd and the H2020 project EU-MACS have shown that, in the context of climate services and of climate adaptation at regional scales (in this context, metropolitan regions), in which accurate representation of socioeconomic information is crucial, the uncertainty introduced by harmonizing climate and socioeconomic data was problematic.
Unfortunately, such data are not yet available everywhere since many high-resolution data sets are required. This advocates for removing obstacles to the reuse of available data about public spaces, especially data provided with a well-documented quality by legally mandated organizations, by encouraging user communities to contribute information relevant to climate change studies such as the OSM crowdsourcing community. Such data should also be as homogeneous in their content and quality as possible. This would greatly ease the production of generic urban data for future regional and even global very-high resolution urbanized climate models as well as tailored urban climate services. Appendix 1: Overview of several global land cover data sets with an urban description Table  A1  provides a list of recently produced data sets that have a focus on land cover classification of urban areas.
Jackson et al. (2010) define 33 regions in the world with different climates, socio-economic characteristics and architectural practices for four classes of urbanization, which are characterized by their morphology (low/medium/high density and tall building district), thermal and radiative properties. • The World Urban Database and Access Portal Tools (WUDAPT; Ching et al. , 2018) aims to construct a global database on urban form and function. Landsat satellite data and local expertise are used to create spatial maps of Local Climate Zones (LCZ; Stewart and Oke, 2012).
The Open ELS project, which is led by several mapping agencies across Europe and brought together through the EuroGeographics organization, has even proposed a new specific open license. In Europe, after a public consultation on the Directive for Public Sector Information, a proposal for revision has been adopted by the Commission and is being discussed by the Parliament and Council. This revision would lead to greater availability of geographic data under an open license for microscale analysis. The licensing problems associated with the use of public national data also underline the interest in using open databases such as OSM. 7. 2 Cataloguing issuesIt is currently still hard to search and find suitable data sets, even if the situation is improving. Existing catalogues and portals are dedicated either to a territory like the French géoportail6or data.
Acknowledgements Coauthors Burmeister, Esch ,  Heldens ,  Kanani-Sühring ,  Maronga, Pavlik  and Zeidler express their gratitude to the  German Federal Ministry of Education and Research (BMBF)  for funding grant  01LP1601  within the framework of Research for Sustainable Development (FONA; www. fona. de). Coauthor See would like to acknowledge the support of the  FP7-funded ERC project CrowdLand  (Grant n°  617754 ). Coauthors Masson ,  Bocher, de Munck, Lemonsu, Lévy, Schoetter, Tornay, Bonhomme thank the French National Agency of Research  for their support through the project applied  Modelling and urbAn Planning laws: Urban Climate and Energy (MApUCE)  with reference  ANR-13-VBDU-0004. Coauthors Masson ,  Bocher, de Munck , Lemonsu, Schoetter, Votsis and Bucher express their gratitude to ERA4CS, an  ERA-NET  initiated by  JPI  Climate with co-funding from the  European Union  (Grant n°  690462 ) for the  URCLIM  project (www.
• Jackson et al. (2010) define 33 regions in the world with different climates, socio-economic characteristics and architectural practices for four classes of urbanization, which are characterized by their morphology (low/medium/high density and tall building district), thermal and radiative properties. • The World Urban Database and Access Portal Tools (WUDAPT; Ching et al. , 2018) aims to construct a global database on urban form and function. Landsat satellite data and local expertise are used to create spatial maps of Local Climate Zones (LCZ; Stewart and Oke, 2012).
(Loga et al. , 2016)10)s National Research Council Institute for Research in Construction(Jackson et al. , 2010), and in France, with a detailed analysis of existing residential real estate (RAGE, 2012). More recently, initiatives on a larger scale have been undertaken, e. g. , the "Tabula" project(Loga et al. , 2016), which has developed a catalog of national building typologies representing the residential building stock in 20 European countries. These studies only deal with residential building use; however, the proposed methodology can be applied to all building uses, and to a larger geographical scale. The construction of the comprehensive architectural database consists of describing the construction materials used for the building archetypes. A literature review on construction practices in France byTornay et al.
The corresponding analysis is based on processing of multi-temporal mass data collections of the Landsat archive using Google Earth Engine. • Masson et al. (2003) and Faroux et al. (2013) built a 1 km resolution global database suitable for vegetation and urban surface models for atmospheric modelling, with the associated database. The urban description is based on CORINE Land Cover over Europe • number of urban spatial extension spatial resolution classes Global Human 9 Global 100m Settlement Layer Global Urban 1 Global 12m (public 75m) Footprint Ecoclimap-SG 10 (LCZ) Global 300m Jackson et al. (2010) 4 Global 1km WUDAPT 10 (LCZ) 100s of cities 100m, maps not yet around the world, available from a China, Europe central repository CORINE 9 Europe 25 ha Urban Atlas 17 700 large cities in urban block Europe.
High-throughput DNA sequencing techniques enable time-and cost-effective sequencing of large portions of the genome. Instead of sequencing and annotating whole genomes, many phylogenetic studies focus sequencing effort on large sets of pre-selected loci, which further reduces costs and bioinformatic challenges while increasing coverage. One common approach that enriches loci before sequencing is often referred to as target sequence capture. This technique has been shown to be applicable to phylogenetic studies of greatly varying evolutionary depth. Moreover, it has proven to produce powerful, large multi-locus DNA sequence datasets suitable for phylogenetic analyses. However, target capture requires careful considerations, which may greatly affect the success of experiments. Here we provide a simple flowchart for designing phylogenomic target capture experiments.
INTRODUCTION High throughput DNA sequencing technologies, coupled with advances in high-performance computing, have revolutionized molecular biology. These advances have particularly contributed to the field of evolutionary biology, leading it into the era of big data. This shift in data availability has improved our understanding of the Tree of Life, including extant  (Hug et al. , 2016)  and extinct organisms (e. g. ,  Green et al. , 2010). While full genome sequences provide large and informative DNA datasets and are increasingly affordable to produce, they pose substantial bioinformatic challenges due to their size (data storage and computational infrastructure bottlenecks) and difficulties associated with genomic complexity. Further, assembling full genomes is often unnecessary for phylogenomic studies if the main goal is to retrieve an appropriate number of phylogenetically informative characters from several independent and single-copy genetic markers  (Jones and Good, 2016).
, 2012). In contrast, the target capture method  (Albert et al. , 2007; Gnirke et al. , 2009)  offers a different genome-subsampling alternative. It consists of designing custom RNA bait sequences, which hybridize (bind) with the complementary DNA region of the processed sample. In a subsequent step, the DNA fragments that hybridized with bait sequences are captured, often amplified via PCR, and then sequenced. The design and selection of bait sets for a phylogenomic study is an important decision that needs to be considered with the organism group and research question in mind. Target capture focuses sequencing effort and coverage (also referred to as sequencing depth) on preselected regions of the genome. This allows for the targeted selection of large orthologous multi locus datasets, which is one of the reasons why target capture has been deemed the most suitable genomereduction method for phylogenetic studies  (Jones and Good, 2016) , leading to its ever-growing popularity (Figure  1 ).
(B) Normalized cumulative publications using target sequence capture in relation to other phylogenomic studies over time sorted by year of publication. We restricted our searches for studies published from 2006, the year of release of the first commercial high-throughput sequencer. We searched for Original Articles published in English in the category 'Evolutionary Biology'. We used eight combinations of keywords in independent searches that included the terms: 'hybrid' OR 'target*' OR 'exon' OR 'anchored' AND 'enrichment' OR 'capture' AND 'phylogenom*'. We merged the datasets and we removed duplicated records by comparing unique DOIs (blue bars in panel A). These searches were contrasted with all other phylogenomic studies as specified by the keywords 'sequencing' AND 'phylogenom*' (yellow bars in panel A).
Once genome or transcriptome references are produced or downloaded, the next step is selecting the target loci for bait design. Good starting points for identifying loci with the right amount of genetic variation are the bait design tools MarkerMiner 1. 0  (Chamala et al. , 2015) , BaitFisher  (Mayer et al. , 2016)  and MrBait  (Chafin et al. , 2018) , as well as the simulation package CapSim  (Cao et al. , 2018). BaitFisher (with its filtering program BaitFilter) and MrBait allow for the design of baits targeting a broad taxonomic spectrum and different enrichment strategies. MrBaits uses multiple sequence alignments (MSAs) or genomes as inputs, while BaitFisher uses only MSAs. A more specific tool is MarkerMiner, which specializes in designing baits for single-copy nuclear genes in angiosperms using transcriptomes.
BIOINFORMATICS Data Storage and Backup High-throughput sequencing produces large volumes of data, in the size range of at least tens to hundreds of Gigabytes (GB), which need to be stored efficiently. It is therefore important to plan for sufficient storage capacity for processing and backing up genomic data. In addition to the raw sequencing data, target capture projects typically generate a high volume of data that exceed the size of the original data 3-to 5-fold during the processing steps. This is due to several bioinformatic processing steps (outlined below), which produce intermediate files of considerable size for each sample. Assuming an average raw sequencing file size of 1-2 GB per sample, we recommend reserving a storage space of up to 10 GB per sample. Most importantly, the raw sequencing files should be properly backed up and preferably immediately stored on an online database such as the NCBI sequence read archive  (Leinonen et al.
Other pipelines are more focused on guiding users through the complete process from cleaning raw sequencing reads to producing data structures that can be readily used for phylogenetic inference (e. g. Multiple Sequence Alignments or SNP datasets), such as PHYLUCE  (Faircloth, 2016)  or SECAPR  (Andermann et al. , 2018)  (Table  2 ). The choice of which pipeline to choose is usually based on the type of data at hand and on the intended use of these data. For example PHYLUCE is particularly streamlined for sequence data of UCEs and enables easy and fast processing of raw reads into MSAs. SECAPR on the other hand is designed for more general use as it combines the user-friendly design of PHYLUCE with additional steps of sequence engineering, making it more suitable for target capture data of any type.
If the aim is to retrieve very long sequences including intron sequences flanking the targeted exons, HYBPIPER is the appropriate tool, yet it requires more bioinformatic knowledge to prepare the input data and to process the resulting sequences into data structures for phylogenetic analyses. Similarly, aTRAM enables generation of long sequences, using an iterative assembly approach, which we explain in more detail below. All pipelines named above are usually used for short read data (Illumina). While our experience with processing long read data (i. e. Nanopore or PacBio) for target capture is limited, it appears that for these datatypes the use of standardized pipelines is not as common as for short read data. The lack of an established pipeline for long-read target capture is perhaps due to greater differences between datasets produced with these methods, in terms of coverage, fragment length, and intended purpose of the experiments.
Nanopore sequencing is commonly used for assembling small genomes (e. g. ,  Loman et al. , 2015; Bethune et al. , 2019)  and while some workflows have been published as software packages, such as Nanopolish (based on  Loman et al. , 2015) , the majority of studies using this sequencing technology apply a combination of bioinformatic toolbox commands to create project-specific workflows. Similarly, studies that have used PacBio sequencing in combination with target capture enrichment commonly create their individual customized workflows (e. g. ,  Wang et al. , 2015; Lagarde et al. , 2017).
Cleaning, Trimming, and Quality Checking The first step after receiving and backing up raw read files is the removal of low-quality reads, of adapter contamination, and of PCR duplicates. These are usually done in conjunction, using software such as Cutadapt  (Martin, 2011)  or Trimmomatic  (Bolger et al. , 2014). Low quality reads: Illumina reads are stored in FASTQ file format, which in addition to the sequence information contains a quality (PHRED) score for each position in the read, representing the certainty of the nucleotide call for the respective position. This information enables cleaning software to remove reads with overall low quality and to trim parts of reads below a given quality threshold. Adapter contamination: Adapter contamination particularly occurs if very short fragments were sequenced (shorter than the read length).
Reference-based assembly: There are several mapping software packages that allow mapping (aligning) reads against a reference library. Commonly used read mapping software packages are the Burrows Wheeler Aligner BWA,  (Li and Durbin, 2010) , Bowtie  (Langmead et al. , 2009) , and Minimap  (Li, 2016). The resulting reference-assembly product is usually stored in BAM format. The reference-assembly approach collects the complete read variation for each locus and enables the user to extract SNPs, to phase reads belonging to different alleles or to simply build consensus sequences from the read variation. When mapping reads against a reference library (collection of reference sequences), the user must choose similarity thresholds, based on how similar the sequence reads are expected to match the reference sequence.