dataesr/scientific-paragraphs-categorization huggingface dataset:
, the UK Environmental and Emissions Monitoring System database (EEMS), the UK National Atmospheric Emissions Inventory (NAEI), and direct facility-level reporting by Dutch operators. In general, the comparison for individual facilities shows a large discrepancy between the top-down derived emissions and all bottom-up (inventory and reported) estimates, which may be expected because of the nature of single snap-shot measurements per facility in this study and potential temporal variability per facility demonstrated via repeat measurements byFoulds et al. (2022). The largest discrepancy exists with the annual emission data from the globally gridded GFEI inventory for the year 2019, showing that measured aggregated emissions from UK and Dutch sites are higher by a factor of ~ 21 and ~ 279, respectively.
Each stage carries out a thermodynamic assessment, which is a collation and critical review of all available experimental data for a particular thermodynamic system along with a careful estimate for the key thermodynamic properties (i. e. , phase diagrams, enthalpic information, etc. ). The CALPHAD approach has been applied to the in CaO-SiO2 system previously, with the first comprehensive assessment and modelling carried out by Haas et al in 1981  [3]  and it was used as the basis of all modern assessments for the CaO-SiO2 system. While seminal and largely correct, the study by Haas et al has typographical errors in the equations  [4]  which become obvious when comparing against the tabulated data. Commercial databases such as FACTSAGE  [5]  and SGTE also contain extensive information for the calcium silicates; however, recent updates to the CaO  [6]  and SiO2  [7]  end-members have not yet been incorporated and there has been a dramatic change to the assessed melting point of CaO.
The rest of this contribution is organized as follows: First, Sec. 2 and Sec. 3 describes the published data for the calcium silicates used in this study. Sec. 4 details the model used to describe all phases in the system and the fitting procedure for thermodynamic properties of the solid calcium silicates and the associates model, including our novel information criterion approach. The heat content measurements and DFT procedures for Ca2SiO4 and Ca3Si2O7 is detailed in Sec. 5. Finally, the error estimates from the resultant fit to the data points are assessed. This results in a much-needed update to the full cementitious CaO-SiO2 assessment including a complete phase diagram. The model for this fundamental binary system will be used to extend to the CaO-Al2O3-SiO2-FexOy-MgO as part of the development of a thermodynamic database for Portland cement clinkers.
To reduce human bias, the estimation of the uncertainties of each dataset is optimised deterministically. In the first instance the author's stated uncertainties for the experimental measurement are entered. After an initial fit, the deviation between the model predictions and the experimental measurements are used to scale the uncertainty estimates. The error limits suggested by Haas et al  [3]  are employed, where the uncertainties are adjusted with the goal to keep 95% of the data within 2σ (standard deviations) of the final fit and no more than a 1/3 of data outside of 1σ, to prevent over and under weighting of datasets. This method is a deterministic way of estimating uncertainty where there is conflicting data. The approach for determining the uncertainties has been discussed in the literature  [61] ; however, there is no solid consensus on the best approach as of yet.
Thermodynamic model for the phase diagram The melt model used here approximates the melt as a mixture of neutral species, called the`associates''. This model is popular in systems where the melt phase exhibits short range order  [63]  which is the case in metal-oxygen system such as C-S and is readily implemented in popular thermodynamic software. The fictitious associates are formed from the liquid endmembers but may be normalised in different ways; for example, in previous studies of the C-S system, authors have normalised associates to have 2 non-oxygen atoms per mole of species  [14]. In this assessment a simple direct linear mixing of the end members with corrections for the enthalpy and entropy were used. This was chosen as it produced satisfactory results and it is similar to the normalisation found in MTDATA thermodynamic database  [64].
Experimental and computational methods The data that is available for this system is discussed in Section 2. There are several key measurements which should be repeated, or missing data that must be ascertained to complete a thermodynamic assessment targeted at modelling cement clinker formulation. This section details the preparation and synthesis methods used to obtain heat content measurements for C2S and C3S2. To supplement the experimental datasets, DFT calculations were also performed to generate additional theoretical data. This is important especially for high temperature phases like α'-C2S, for which low temperature data does not exist (these low temperature data is important in the fitting of the 3 rd generation function). 5. 1. Synthesis of C 3 S 2 and γ-C 2 S. The starting materials are CaCO3 (Alpha Aesar, 99.
Heat content measurements. A MHTC96 (Setaram) high temperature calorimeter with a drop measuring head (Type S, 1300°C max) was used for the heat content measurements. The measurements are carried out using an alumina crucible with a platinum inlay (diameter 13 mm and height 46mm). The measurements were performed under flowing Ar atmosphere. The calorimeter is calibrated using pure alumina (Al2O3, 99. 95%) pieces which are dried and calcined at 1400°C for 12h. The pellets have a mass ranging from 15 to 150mg (uncertainty ±0. 02mg). Three series of 6 or 7 alumina samples are dropped for each temperature at 600°C, 700°C, 800°C, 900°C, 1000°C, 1100°C, and 1200°C and the associated heat effect recorded and integrated using the Calypso software package (Setaram). The resulting calibration constant at a given temperature is calculated using heat content values for Al2O3 from the FTOxid database  [5].
s) are plotted as a function of the phase amount introduced into the calorimeter. A linear regression forced passing through the origin is performed to determine the slope. The uncertainty of the measurement corresponds to twice the uncertainty on the slope (95% confidence interval). The measured heat content corresponds to the slope divided by the calibration constant. Again, the introduction temperature was measured by thermocouple (±0. 1K) and corrected to 298. 15K using the low temperature heat capacity data from King  [9]  for Ca3Si2O7 and Ca2SiO4. Additionally, the melting point of Ca3Si2O7 was measured by differential scanning calorimetry in a Labsys TM Evo apparatus designed by SETARAM. Calibration of the DSC equipment was carried out using the melting point of pure NaCl (99. 9% Alfa Aesar) as well as the a-b transition and the melting point of K2SO4 (99.
DFT calculations The ground state properties of all experimentally stable calcium silicates were calculated using density functional theory (DFT)  [69, 70]. The Vienna ab-initio software package (VASP v. 5. 4. 4) was used for the theoretical ground state calculations  [71, 72]. The semi-local SCAN exchange functional  [73]  was used for the many-body exchange-correlation interaction. The GW PAW potentials (Ca_sv_gw, Si_gw and O_gw_new in VASP notation) were used for the calculations as they give the most accurate description of the lattice volume of SiO2 in its a-quartz structure at low temperature. For Ca, the 3s, 3p and 4s orbitals, for Si, the 3s and 3p orbitals and for O the 2s and 2p orbitals were considered as valence states in the calculations. The cut-off energy was set to 800 eV. A gamma centred grid of k-points in the irreducible part of the Brillouin zone is automatically generated following the Monkhorst-Pack scheme  [74].
The k-grids are 2x1x1 for Ca3SiO5, 4x7x9 for γ-Ca2SiO4, 5x4x2 for β-Ca2SiO4, 1x2x4 for α'-Ca2SiO4, 3x3x2 for Ca3Si2O7, 3x3x3 for CaSiO3 wollastonite and 2x3x2 for CaSiO3 pseudo-wollastonite. The lattice parameters as well as the internal atomic coordinates of all investigated compounds are fully relaxed. The linear tetrahedron method with Blöchl corrections  [75]  is used to calculate the electronic density of states (DOS). The relaxations are performed with a convergence criterion of 10 -8 eV/Å for the total energy. Finite temperature properties such as the Helmholtz free energy F and heat capacities at constant volume/pressure CV/CP are approximated using lattice dynamics theory  [76]. The phonon spectrum of selected compounds is determined using the frozen phonon (supercell) method. The vibrational modes are calculated using the phonopy code  [77]  coupled to VASP.
Calorimetric measurements The results of the heat content measurements for Rankinite are summarised in Table  3. No experimental data exists in the literature on the heat capacity or heat content of Rankinite at these high temperatures, thus this data is compared to the estimated data of Haas et al  [3]  and the commercial FTPs database  [5]. The estimated heat content together with the experimental values from 298. 15K to 1471. 65K are presented in Figure  2. There is some agreement with the estimation of Haas but the calculated values using FTPs clearly overestimate the heat content compared with the experimental results. The DSC results for C3S2 were only interpretable on heating due to the peritectic nature of its melting reaction. On cooling, the sample contained γ-Ca2SiO4 and CS which did not completely convert to C3S2.
However, the melting temperature obtained is 1737±5K, which is identical to the value reported by Levin  [52]. O'Brien  [41]  measured the β, α'-Ca2SiO4 heat contents by an inversed drop calorimetric technique. The temperature of transition γ→α' is situated near 1120K. Therefore, in this present work the measured heat contents values include the γ-Ca2SiO4 to α'-Ca2SiO4 in the temperature range 972. 86-1471. 65K. The obtained results compared to calculated values using the FTPs database are plotted in Figure  3  Figure  3  γ, α'-Ca2SiO4 Heat contents measurement. Lines were calculated using data from FactSage FTPs data  [5]  and Haas  [3] : γ-Ca2SiO4 300K to 1072. 0K, α'-Ca2SiO4 1171. 39K to 1471. 05K. Coughlin  [44]  values for α'-Ca2SiO4 have been corrected with enthalpy of transition γ→ β from Forest  [35]  to have the same reference state.
The Checklist of the Vascular Plants of the Republic of Guinea (CVPRG) is a specimen-based, expertvalidated knowledge product, which provides a concise synthesis and overview of current knowledge on 3901 vascular plant species documented from Guinea (Conakry), West Africa, including their accepted names and synonyms, as well as their distribution and status within Guinea (indigenous or introduced, endemic or not). The CVPRG is generated automatically from the Guinea Collections Database and the Guinea Names Backbone Database, both developed and maintained at the Royal Botanic Gardens, Kew, in collaboration with the staff of the National Herbarium of Guinea. A total of 3505 indigenous vascular plant species are reported of which 3328 are flowering plants (angiosperms); this represents a 26% increase in known indigenous angiosperms since the last floristic overview.
These voucher specimens provide auditable evidence of the occurrence of each taxon in Guinea. The CVPRG is a specimen-based, expert-validated knowledge product, generated automatically from the Guinea Collections Database and the Guinea Names Backbone Database, both developed and maintained at the Royal Botanic Gardens, Kew (hereafter Kew), in collaboration with the National Herbarium of Guinea. Earlier iterations of CVPRG underwent expert review by 42 regional or taxon specialists who co-authored this data descriptor. The CVPRG is published in two formats: a Darwin Core Archive available from GBIF  10  and a printable pdf available from Zenodo  11. The Guinea Collections Database is based on an extract from Kew's Wet Tropics Africa database comprising records from environmental surveys for mining projects 2005-2015, records from botanical exploration and Red Listing activity in relation to Tropical Important Plant Areas  12  and specimen records cited by  9  of taxa not represented in other sources.
These records were complemented by: (i) data from GBIF for specimens deposited in herbaria at Kew, Missouri Botanical Garden and Muséum National d'Histoire Naturelle, Paris (K, MO, P; codes follow Index Herbariorum (Thiers, B. M. Index Herbariorum. http://sweetgum. nybg. org/science/ih/)); (ii) data from P georeferenced via the Guinea Biodiversity Information for Development (BID) project  [13] [14] [15]  ; data from MO, and from the herbaria of Meise Botanic Garden (BR) and Naturalis Biodiversity Centre (WAG) informed by the Rainbio database  16. The Guinea Names Backbone Database is based on a download from the African Plant Database (APD (version 4. 0. 0) Conservatoire et Jardin botaniques de la Ville de Genève and South African National Biodiversity Institute, Pretoria, http://africanplantdatabase.
ch (accessed 29 July 2021)) for tropical Africa. This was complemented by records from Plants of the World Online (POWO. Facilitated by the Royal Botanic Gardens, Kew. http://www. plantsoftheworldonline. org/2019-2022) for all taxa reported in  9. Guinea is unusual among tropical African countries in having a recently published Flora  9  , but all data therein are over 30 years old, mostly over 50 years old. The first decades of the 21 st century saw rapid growth in botanical fieldwork in Guinea, yielding numerous herbarium specimens documenting the Guinean flora. However, this period saw major tree-cover loss (25% in 2000-2018) in the Forestière region, home to Guinea's greatest known plant diversity  17. Against a backdrop of habitat loss due to subsistence and cash-crop farming, exacerbated by growing threats from mining, CVPRG is a key resource for those researching, communicating about and seeking to safeguard Guinea's rich plant diversity and the social, ecological and economic benefits accruing from these biological resources.
Data acquisition and processing. Creating a comprehensive, contemporary specimen-based checklist of a region requires two parallel workflows: • Gathering all available data on field collections within the region and validation of identifications. • Creating a "names backbone" of accepted taxon names and synonyms. Here we describe data acquisition and processing for each of these workflows, before reporting how they were brought together for iterative checklist creation and expert validation steps. Fig. 2  Growth in numbers of plant species known from Guinea and in the numbers of herbarium specimens documenting the Guinea flora. Increase over time in: (i) plant species recorded from the Republic of Guinea, plotted (dashed trace) by the date when species was first described (which may predate their first record from Guinea by many years) and (ii) herbarium specimens collected in Guinea and incorporated in the Guinea Plant Collections database, plotted (solid trace) by their year of collection.
Collections data acquisition. Plant specimen data was collected from a variety of existing digital resources including herbarium collection databases at the Royal Botanic Gardens, Kew (K), the Missouri Botanical Garden (MO), and the MNHN, Paris (P), the herbarium at Adam Mickiewicz University (POZG), the herbarium of the Naturalis Biodiversity Centre (WAG), from GBIF  [35] [36] [37] [38]  , and from datasets developed for earlier projects including Guinea BID  [13] [14] [15]  , Guinea TIPAs and Rainbio  14. Data were also extracted from Lisowski's Flore de la Guinée  9. All our collections data sources are detailed in Table  1. This data was reduced to a common record format and consolidated in a Microsoft Access database, the Guinea Collection Database comprising 32330 records. We aimed to ensure that collections coverage in this database was sufficient to have at least one expert-verified collection-based record cited of each vascular plant taxon found in Guinea, including all taxa recorded in the Flore de la Guinée  9.
A secondary objective was to obtain an idea of the geographic range of each taxon within Guinea (28164 collections are georeferenced at varying levels of accuracy). We have not attempted to record in our database every plant collection ever made in Guinea nor to georeference all specimens, an endeavour beyond the scope of the project. Collections data processing. All taxon names obtained from specimens are standardised to accepted names based on our names backbone database (see below). When specimens are collected, duplicates are generally sent to several herbaria. There may be several specimen records for any given collection event and therefore individual plant. A process of deduplication based on matching collection number, taxon name, and date was performed; a preferred duplicate was selected with the most complete metadata.
Names data acquisition. A custom names backbone database was built, the Guinea Names Backbone Database. The starting point was a download in 2019 from the African Plant Database (APD (version 4. 0. 0), Conservatoire et Jardin botaniques de la Ville de Genève and South African National Biodiversity Institute, Pretoria, http:// africanplantdatabase. ch (accessed 29 July 2021)) for the tropical Africa area. From Plants of the World Online (POWO, facilitated by the Royal Botanic Gardens, Kew. http://www. plantsoftheworldonline. org/2019-2022) records for all taxa in  9  and all taxa shown as present in Guinea were downloaded and the information matched by taxon name and added to the Guinea Names Backbone Database. Data records have been added for taxa new to science as they have been published.
Resources consulted during compilation included publications describing species or genera new to science  [39] [40] [41] [42]  ; other taxonomic literature such as taxonomic monographs, revisions, synopses or checklists  ; phylogenetic studies  [67] [68] [69] [70] [71] [72] [73] [74] [75]  ; nomenclatural updates  [76] [77] [78] [79] [80] [81]  ; collectors' biographies or itineraries  82, 83  ; and the following online data resources resulting from earlier and/or ongoing compilation endeavours: the BioPortal of Naturalis Biodiversity Centre (https://bioportal. naturalis. nl): the Botanical Collections resource hosted by Meise Botanic Garden (http://www. botanicalcollections. be/#/en/home); the AMUNATCOLL Database of Natural History Collections of the Faculty of Biology Adam Mickiewicz University, Poznan (https://amunatcoll.
Names data expert validation. The expert opinions of 42 checklist authors, each specialising in a different taxonomic group or in the flora of the region as a whole (Supplementary Table  1 ), were used to clean the Guinea Names Backbone Database. Where sources disagreed as to the accepted name and synonymy, a choice has been made by the specialist concerned. For groups where no specialist was available (e. g. Moraceae, Vitaceae), APD was followed. Collections data expert validation. Collection records from the Guinea Collections Database were sent to the expert reviewers along with draft checklist family reports. Experts were not asked to confirm every collection determination, but rather to review taxa with few collections (generally three or fewer) or outside of recorded distributions shown on POWO.
The vast majority of taxon name changes since 2010 are due to nomenclatural changes. Guinea species which lacked taxonomist-verified records from any other country were assigned as endemics (Supplementary Table  2 ) after checking herbarium records, APD, POWO, GBIF, and taxonomic literature  9, 20, 26, 30, 31, [91] [92] [93] [94] [95] [96] [97] [98] [99] [100] [101] [102] [103] [104]. Generation of the checklist. The checklist is generated automatically from the Guinea Collections Database and the Guinea Names Backbone Database. This prevents the databases and text copy from getting out of sync. The collections are grouped and summarized using Access queries (SQL) and exported to an XML file. This file was then used to generate versions of the checklist in Word document form for publication as a printable pdf  11  and as Darwin Core data  10  which was uploaded to GBIF using their Integrated Publishing Toolkit (IPT, https:// www.
gbif. org/ipt), applying GBIF guidelines for checklist publication (https://ipt. gbif. org/manual/en/ipt/latest/ checklist-data). Each expert screened and annotated draft checklist accounts of the taxa in which they specialise (Supplementary Table  1 ) in late 2019, in mid-2021 and finally, in mid-2022. Final updates to the checklist were made in August 2022. All changes were incorporated in the Guinea Collections Database and Guinea Names Backbone Database before the creation of the printable version of the checklist  11  and the corresponding Darwin Core dataset  10. Structure of the printable checklist. Data records from the Guinea Collections Database and Guinea Names Backbone Database are grouped and summarised to create family lists comprising one or more species treatments. Within the checklist, the angiosperm families are ordered following Angiosperm Phylogeny Group IV  105  , while the Pteridophytes follow WorldFerns (Hassler, Michael (2004 -2022): World Ferns.
Data for infraspecific taxa (subspecies or varieties) appear below the corresponding species entry. Details of publishing authors follow each name at species or infraspecific rank, except for autonyms. The most recent specimen in the Guinea Collections Database of each species or infraspecific taxon is generally presented as a voucher. The voucher information usually comprises the collector(s') name(s) and collecting number, followed by the date of collection and, where available, an ID unique to that specimen (usually a barcode). This allows users to rapidly find and view any corresponding specimen details and images made available by the herbarium where the specimen is deposited (indicated by an alphabetical prefix to the ID number) or via an aggregator such as GBIF. No digital record of a herbarium specimen has been located for 642 species.
Data Records The download of the data set(s) is available via the GBIF. org repository under a CC-BY 4. 0 Licence in a Darwin Core Archive File. The CVPRG has been published as a Checklist dataset  10  on GBIF. The data may be examined and downloaded from GBIF: https://doi. org/10. 15468/f5gb45. Darwin Core Archive (DwC-A) is a standardized format for sharing biodiversity data as a set of one or more data tables. The Taxon core data table contains 34,048 records, each with the data fields listed in Table  2. These comprise 4,195 accepted names and 29,821 synonyms. The IPNI plant ID (https://www. ipni. org) is used as the stable identifier for taxa. Two extension data tables are also part of the DwC Archive. An extension record supplies extra information about a core record. The Occurrence extension contains one record per taxon, that of the voucher specimen chosen to evidence the presence of the taxon within Guinea.
It comprises 4024 voucher records in total, each with the data fields listed in Table  3. The Species Distribution extension contains one or more records per taxon: one for the country and one for each region within Guinea from which the Guinea Collections database contains at least one specimen of the taxon, making 10419 records in total, each with the data fields listed in Table  4. Together, the data in the three data tables described above (Tables  2 3 4 ) include all the data presented in the CVPRG with the following exceptions: (i) the indication of species considered endemic to Guinea; (ii) the numbers of specimens of each species known from Guinea as a whole and from each of the four regions of Guinea. These omissions are due to the limitations of the Darwin Core format.