dataesr/scientific-paragraphs-categorization huggingface dataset:
When plant sapwood area was provided by data contributors, we interconverted between sap flow rate per plant and per unit sapwood area. If leaf area was supplied, we also calculated sap flow per unit leaf area, but note that this transformation does not take into account the seasonal variation in leaf area; we document in the metadata for which datasets this information could be available from data contributors. In QC2 we estimated missing environmental variables which could be derived from related variables in the dataset (Appendix, Table  A6 ). We also estimated the apparent solar time and extraterrestrial global radiation from the provided timestamp and geographic coordinates using the R package "so-laR"  (Perpiñán, 2012). All estimated or interconverted observations were flagged as "CALCULATED" in the "env_flags" or "sap_flags" table (Fig.
Data structure One of the major benefits of the SAPFLUXNET data workflow is the encapsulation of datasets in self-contained R objects of the S4 class with a predefined structure. These objects belong to the custom-designed sfn_data class, which displays different slots to store time series of sap flow and environmental data, their associated data flags, and all the metadata (Fig. S2 ). For further information please see the "sfn_data classes" vignette in the sapfluxnetr package  (Granda et al. , 2019). The code identifying each dataset was created by the combination of a "country" code; a "site" code; and, if applicable, a "stand" code and a "treatment" code. This means that several stands and/or treatments can be present within one site (Table  S3 ). At the end of the QC process, we generated a folder structure with a first-level storing datasets as either sfn_data objects or as a set of comma-separated (csv) text files.
Within each of these formats, a second-level folder groups datasets according to how sap flow is normalized (per plant, sapwood, or leaf area); note that the same dataset, expressing different sap flow quantities, can be present in more than one folder (e. g. "plant" and "sapwood"). Finally, the third level contains the data files for each dataset: either a single sfn_data object storing all data and metadata or all the individual csv files. More details on the data structure and units can be found in the "sapfluxnetr-quick-guide" and "metadata-anddata-units" vignettes, respectively, in the sapfluxnetr package  (Granda et al. , 2019).
Data coverage The SAPFLUXNET version 0. 1. 5 database harbours 202 globally distributed datasets (Figs. 2a and S4, Table  S3 ), from 121 geographical locations, with Europe, the eastern USA, and Australia especially well represented. These datasets were represented in the bioclimatic space using the terrestrial biomes delimited by Whittaker (Fig. 2b ), but note that, as any bioclimatic classification, it has its limitations. Datasets have been compiled from all terrestrial biomes, except for temperate rain forests, although some tropical montane sites have been included. Woodland/shrubland and temperate forest biomes are the most represented in the database, adding up to 80 % of the datasets (Fig. 2b ). However, large forested areas in the tropics and in boreal regions are still not well represented (Fig.
2a  and b ). Looking at the distribution by vegetation type (Fig. 2c ), evergreen needleleaf forest is the most represented vegetation type (65 datasets), followed by deciduous broadleaf forest (47 datasets) and evergreen broadleaf forest (43 datasets). SAPFLUXNET contains sap flow data for 2714 individual plants (1584 angiosperms and 1130 gymnosperms), belonging to 174 species (141 angiosperms and 33 gymnosperms), 95 different genera, and 45 different families (Supplement, Tables  S4-S5 ). All species but one -Elaeis guineensis, a palm -are tree species. Pinus and Quercus are the most represented genera (Fig. 3b ). Amongst the gymnosperms, Pinus sylvestris, Picea abies, and Pinus taeda are the three most represented species, with data provided on 290, 178, and 107 trees, respectively (Fig.
3a ). For the angiosperms, Acer saccharum, Fagus sylvatica, and Populus tremuloides are the most represented species, with 162, 116, and 104 trees, respectively, although most Acer saccharum data come from a single study with a very large sample size (Fig. 3a ). Some species are present in more than 10 datasets: Pinus sylvestris, Picea abies, Fagus sylvatica, Acer rubrum, Liriodendron tulipifera, and Liquidambar styraciflua (Fig. 3a , Table  S4 ). Earth Syst. Sci. Data, 13, 2607 Data, 13, -2649 Data, 13, , 2021  https://doi. org/10. 5194/essd-13-2607-2021.
Methodological aspects For more than 90 % of the plants, sap flow at the whole-plant level is available (either directly provided by contributors or calculated in the QC process); this is important for upscaling SAPFLUXNET data to the stand level (cf. Sect. 4. 2). Because the leaf area of the measured plants is often not available as metadata, sap flow per unit leaf area was estimated for only 18. 6 % of the individuals (Fig. 4 ). The heat dissipation method is the most frequent method in the database (HD, 66. 4 % of the plants), followed by the trunk sector heat balance (TSHB, 16. 4 %) and the compensation heat pulse method (CHP, 8. 4 %) (Fig. 4 ). This distribution is broadly similar to the use of each method documented in the literature, although the TSHB method is overrepresented here, compared to the current use of this method by the sap flow community  (Flo et al.
, 2019; Peters et al. , 2018 ; Bar height for a given colour is proportional to the number of plants in the corresponding dataset, which is also shown in parentheses next to the dataset code. Steppe et al. , 2010). Radial integration of single-point sap flow measurements is more frequent than azimuthal integration (Table  2 ), except for the CHD method. For a large number of plants measured with the HD method and all plants measured with the HPTM method, there was not any radial integration procedure reported. In contrast, the CHP, HR, SHB, and TSHB methods are those which more fre-quently addressed radial variation in one way or another (Table 2). Azimuthal integration procedures are also more frequent when the TSHB method is used (Table  2 ). Table  1. Number of sap flow times series in SAPFLUXNET depending on whether they were calibrated (species-specific) or noncalibrated, or whether this information was not provided, for the different sap flow methods: cyclic (or transient) heat dissipation (CHD), compensation heat pulse (CHP), heat dissipation (HD), heat field deformation (HFD), heat pulse T-max (HPTM), heat ratio (HR), stem heat balance (SHB), and trunk sector heat balance (TSHB).
Many large angiosperm trees from tropical (CRI_TAM_TOW, IDN_PON_STE, GUF_GUY_ST2; see Table  S3  for dataset codes) and temperate forests (Fagus grandifolia, USA_SMIC_SCB) also show large sapwood areas (> 5000 cm 2 ), but the plant with the deepest sapwood is a gymnosperm, an Abies pinsapo in Spain with 30. 7 cm of sapwood depth.
Stand characteristics Stand-level metadata include several variables associated with management, vegetation structure, and soil properties. Half of the datasets originate from naturally regenerated, unmanaged stands, and 13. 9 % come from naturally regenerated but managed stands. Plantations add up to 32. 2 %, and orchards only represent 4 % of the datasets. Reporting of structural variables is mixed, with stand height, age, density, and basal area showing relatively low missingness (6. 4 %, 11. 4 %, 12. 9 %, and 13. 4 %, respectively); in contrast, soil depth and leaf area index (LAI) are missing from 26. 7 % and 33. 7 % of the datasets. SAPFLUXNET datasets originate from stands with diverse structural characteristics. Median stand age is 54 years, and there are several datasets coming from > 100-yearhttps://doi.
The distribution of LAI shows a median of 3. 5 m 2 m -2 , with the largest values observed in temperate (CZE_BIK, USA_DUK_HAR, HUN_SIK) and tropical (GUF_GUY_GUY, COL_MAC_SAF_RAD) forests. The stands with the lowest LAI correspond to the sparse woodlands from Mediterranean and semi-arid locations and also those from forests near altitudinal or latitudinal treelines (FIN_PET, AUT_TSC). SAPFLUXNET datasets show a median soil depth of 100 cm, with only a dozen datasets originated from sites with soils deeper than 10 m (Fig. 5b ). The number of plants per dataset is highly variable, with most of the datasets (86 %) containing data for at least 4 trees and 46 % of the datasets having data for at least 10 trees (Fig. 6a ; see also Fig. 9 ).
Temporal characteristics The oldest datasets in SAPFLUXNET go back to 1995 (GBR_DEV_CON, GBR_DEV_DRO), while the most recent data reach up to 2018 (datasets from the ESP_MAJ cluster of sites). Several multi-year datasets are present in SAPFLUXNET (Fig. 6 ), with 50 % of the datasets spanning a period of at least 3 years and some datasets being extraordinarily long (16 years in FRA_PUE). Frequently, the datasets only cover the "growing season" periods, or even shorter periods for some sites which were eventually included because they improved the ecological and geographic coverage of the database (e. g. ARG_MAZ, ARG_TRE as representative of deciduous Nothofagus forest in southern Patagonia). In contrast, a few datasets show continuous records over multiple years (Fig. 6b ). Amongst the longest datasets, most of them come from European or North American sites (Fig.
6 ), except some datasets from Israel (ISR_YAT_YAT, 7 years), Russia (RUS_FYO, 7 years), South Korea (KOR_TAE cluster of sites, 6 years), or New Zealand (NZL_HUA_HUA, 5 years). SAPFLUXNET provides an unprecedented database to study the detailed temporal dynamics of plant transpiration across species and sites globally. Sub-daily records of sap flow (e. g. at least at hourly time steps) are available for extended periods (Fig. 6b ), allowing both seasonal and diel patterns in water-use regulation by trees to be addressed, as well as how these temporal patterns change across species or years across terrestrial biomes, reflecting different phenologies and Earth Syst. Sci. Data, 13, 2607 Data, 13, -2649 Data, 13, , 2021  https://doi. org/10. 5194/essd-13-2607-2021 water-use strategies. For instance, in Mediterranean forests, evergreen species such as Quercus ilex, Arbutus unedo, and Pinus halepensis show moderate sap flow the whole year round, while the deciduous Quercus pubescens shows higher sap flow density during a shorter period and its water use is heavily reduced during a dry year (2012) (Fig.
Availability of environmental data All SAPFLUXNET datasets contain ancillary time series of the main hydrometeorological drivers of transpiration, accompanied by information on where these variables had been measured (Fig. 8a ). Air temperature is available for all datasets. Although vapour pressure deficit (VPD) was originally absent in 38 % of the datasets (Fig. 8a  and b ), we could estimate it for those sites providing air temperature and relative humidity data (QC Level 2; see Sect. 2. 3), and finally only 2 out of the 202 datasets have missing VPD information. For radiation variables, shortwave radiation was most often provided, compared to photosynthetically active and net radiation, which were less provided; only 8 out of 202 datasets do not have any accompanying radiation data. Most of these environmental variables were measured on site, with precipitation being the variable most frequently retrieved from nearby meteorological stations (48 % of the datasets) (Fig.
8a ). Soil https://doi. org/10. 5194/essd-13-2607-2021 Earth Syst. Sci. Data, 13, 2607-2649, 2021 water content measured at shallow depth, typically between 0 and 30 cm below the soil surface, is provided for 56 % of the datasets, while soil moisture from deep soil layers is available for only 27 % of the datasets.
Applications in plant ecophysiology and functional ecology There are multiple potential applications of the SAPFLUXNET database to assess whole-plant wateruse rates and their environmental sensitivity, both across species (e. g. Oren et al. , 1999b)  and at the intraspecific level  (Poyatos et al. , 2007). SAPFLUXNET will allow disentangling the roles of evaporative demand and soil water content in controlling transpiration at the plant level, complementing recent studies looking at how water supply and demand affect evapotranspiration at the ecosystem level  (Anderegg et al. , 2018; Novick et al. , 2016). The availability of global sap flow data at sub-daily time resolution and spanning entire growing seasons will allow focusing on how maximum water use and its environmental sensitivity vary with plant-level attributes such as stem diameter  (Dierick and Hölscher, 2009; Meinzer et al.
, 2014). SAPFLUXNET includes metadata to identify methods (e. g. HRM;  Burgess et al. , 2001)  and data processing approaches (zero-flow determination method in "pl_sens_cor_zero", Table  A5 ) that can help identify suitable datasets to quantify night-time fluxes. Sap flow data have been widely employed to assess changes in tree water use after biotic (e. g. Hultine et al. , 2010)  or abiotic  (Oren et al. , 1999a)  disturbances. Likewise, sap flow data have been used to report changes in species and stand water use following experimental treatments in-volving resource availability modifications (e. g. Ewers et al. , 1999)  or density changes (i. e. thinning;  Simonin et al. , 2007). The SAPFLUXNET database includes datasets with experimental manipulations, applied either at the stand or at the individual level, qualitatively documented in the metadata (Table  3 ).
The main treatments present are related to thinning, water availability changes (irrigation, throughfall exclusion), and wildfire impact (Table  3 ), potentially facilitating new data syntheses and meta-analyses using these datasets (e. g. Grossiord et al. , 2018). The combination of SAPFLUXNET with other ecophysiological databases can be informative as to the relative sensitivity of different physiological processes in response to drought, for example those related to growth and carbon assimilation  (Steppe et al. , 2015). Within-day fluctuations of stem diameter can be jointly analysed with co-located sap flow measurements to study the dynamics of stored water use under drought and its contribution to transpiration (e. g. Brinkmann et al. , 2016)  and to infer parameters on tree hydraulic functioning using mechanistic models of tree hydrodynamics  (Salomón et al.
, 2017; Steppe et al. , 2006; Zweifel et al. , 2007). These analyses could be carried out for a large number of species by combining SAPFLUXNET with data from the DENDROGLOBAL database (http: //78. 90. 202. 92/streess/databases/dendroglobal, last access: 8 June 2021); there are at least 18 SAPFLUXNET datasets with dendrometer data in DENDROGLOBAL. This database and the International Tree-Ring Data Bank (S. Zhao et al. , 2019)  could also be used with SAPFLUXNET to investigate, at the species level, the link between radial growth and water use, including their environmental sensitivity  (Morán-López et al. , 2014) , and how these two processes comparatively respond to drought  (Sánchez-Costa et al. , 2015). Moreover, given the tight link between water use and carbon assimilation, combining SAPFLUXNET with water-use efficiency from plant δ 13 C data could potentially be used to estimate whole-plant carbon assimilation  (Hu et al.
The SAPFLUXNET database will provide a longdemanded data source to be used in ecohydrological research  (Asbjornsen et al. , 2011). Upscaling individual measurements to the stand level  ( Čermák et al. , 2004; Granier et al. , 1996; Köstner et al. , 1998)  is necessary to quantitatively compare sap-flow-based transpiration with evapotranspiration and transpiration estimates at the ecosystem scale and beyond. Even though SAPFLUXNET was designed to accommodate sap flow data at the plant level, scaling to the ecosystem level is possible for many datasets. For a basic upscaling exercise using SAPFLUXNET data  (Poyatos et al. , 2020b) , whole-plant sap flow can be normalized by individual basal area (as DBH is usually available in the metadata; cf. Sect. 3. 3), averaged for a given species, and then scaled to stand-level transpiration using total stand basal area and the fraction of basal area occupied by each measured species (see stand metadata, Table  A3 ).
For many datasets, sap flow data are available for the species comprising most of the stand basal area (often even 100 %, Fig. 9 ), but species-based upscaling may be unfeasible in many tropical sites (Fig. 9b ), where size-based scaling could be applied instead (e. g. da  Costa et al. , 2018). Further refinements of the upscaling procedure could be achieved by using trunk diameter distributions of the sap flow plots  (Berry et al. , 2018). This information, however, is not readily available in SAPFLUXNET, and other data sources (e. g. forest inventories, LIDAR data) or additional simplifying assumptions (i. e. applying the size distribution of measured individuals in the dataset) would be needed. Stand-level transpiration estimates from a large number of SAPFLUXNET sites can contribute to improve our understanding of the role of forest transpiration in the context of stand water balance and its components at the ecosystem (e.
g. Tor-ngern et al. , 2018)  and catchment levels  (Oishi et al. , 2010; Wilson et al. , 2001). Importantly, SAPFLUXNET can contribute to a better understanding of the global controls on vegetation water use  (Good et al. , 2017) , including the biological and climatic controls on evapotranspiration partitioning into transpiration and evaporation components  (Schlesinger and Jasechko, 2014; Stoy et al. , 2019). There is some overlap between the FLUXNET network and SAPFLUXNET (47 datasets from FLUXNET sites). Hence, transpiration from SAPFLUXNET can also be used as a "ground-truth" reference for transpiration estimates from remote sensing approaches  (Talsma et al. , 2018)  and from eddy covariance data  (Nelson et al. , 2020). Extrapolating sap-flowderived stand transpiration to large spatial scales can be challenging due to landscape-scale variation in forest structure  (Ford et al.
, 2007)  or topography  (Hassler et al. , 2018)  and due to the low spatial representativeness of sap flow measurements  (Mackay et al. , 2010). A promising research avenue to help elucidate the role of vegetation in driving hydrological changes across environmental gradients  (Vose et al. , 2016)  would be to combine species-specific stand transpiration data from SAPFLUXNET with stand structural and compositional data from forest inventories (e. g. sapwood area index;  Benyon et al. , 2015). Understanding the patterns and mechanisms underlying species interactions with respect to water use within a community is necessary to predict tree species vulnerability to drought  (Grossiord, 2020). Multispecies datasets from SAPFLUXNET (Table  S3 ) can be used to assess competition for water resources amongst species, for example by identifying changes in seasonal water use across co-existing species and hence characterizing the spatiotemporal segregation of their hydrological niches  (Silvertown et al.
, 2015). By providing a detailed seasonal quantification of tree water use, SAPFLUXNET could also complement isotope-based studies and contribute to interpreting the large diversity in root water uptake patterns observed worldwide  (Barbeta and Figure 9. Potential for upscaling species-specific plant sap flow to stand-level sap flow using SAPFLUXNET datasets. Datasets are shown using an aggregated biome classification; "dry and tropical" include "subtropical desert", "temperate grassland desert", "tropical forest savanna", and "tropical rain forest". Each panel shows the percentage of total stand basal area that is covered by sap flow measurements for each species in the dataset. Datasets are also coloured by the number of species present. Numbers on top of each bar depict the total number of plants for a given dataset.
Empty bars show datasets for which sap flow data expressed at the plant level were not available. Peñuelas, 2017; Evaristo and McDonnell, 2017)  and to explaining the different seasonal origin of root-absorbed water across species and environmental gradients  (Allen et al. , 2019). Plant water fluxes and hydrodynamics are amongst the most uncertain components of ecosystem and terrestrial biosphere models  (Fatichi et al. , 2016; Fisher et al. , 2018). These models are now incorporating hydraulic traits and processes in their transpiration regulation algorithms  (Mencuccini et al. , 2019) , but multi-site assessments of these algorithms are usually performed against evapotranspiration from eddy flux data  (Knauer et al. , 2015; Matheny et al. , 2014). Model validation against sap flow data has been carried out typically at only one  (Kennedy et al.
, 2016; Speckman et al. , 2020; Ward et al. , 2017). Open-source software also allows a seamless integration of different data processing approaches and the implementation of species-specific calibrations, which can contribute to obtaining more robust estimations of sap flow and facilitate replicability  (Peters et al. , 2021). Sap flow measured with thermometric methods provides a precise estimate of the temporal dynamics of water flow through plants  (Flo et al. , 2019). However, their performance in measuring absolute flows is mixed. While some wellrepresented methods in SAPFLUXNET, such as CHP, yield accurate estimates (at least for moderate-to-high flows), the HD method, the most represented method by far, can significantly underestimate sap flow. Our suggested bias correction for uncalibrated HD data (cf.
Sect. 3. 7) can be applied, but, given the unexplained high variability (i. e. by species and wood traits) in the performance of sap flow calibrations  (Flo et al. , 2019) , these corrections should be applied with caution. SAPFLUXNET has been designed to store whole-plant sap flow data, and therefore sap flow measured at multiple points within an individual is not available in the database. Even though this spatial variation could be useful to describe detailed aspects of plant water transport  (Nadezhdina et al. , 2009) , focusing on plant-level data greatly simplifies the data structure. Hence, SAPFLUXNET only includes data already upscaled to the plant level by the data contributors. The main details of how this upscaling process was done for each dataset are provided together with other plant metadata (Table  A5 ), but these metadata show that within-plant variation in sap flow is often not considered (Table  2 ).
For those datasets without radial integration of point measurements, we show how to implement a radial integration based on generic wood porosity types (cf. Sect. 3. 7, Appendix B). The impact of not accounting for radial and circumferential variability when scaling single-point measurements of sap flow to the whole-plant level can be important  (Merlin et al. , 2020) , but the estimation of sapwood area can also cause large errors if it is not accurately determined  (Looker et al. , 2016). SAPFLUXNET does not provide information on the method employed to quantify sapwood area (e. g. visual estimation with or without the application of dyes, indirect estimation through allometries at species or site levels) or on the accuracy of sapwood area data. This precludes uncertainty estimation at the individual level (Fig.
B3 ). Future developments in the SAPFLUXNET data structure could include this information as metadata to better document the sensor-to-plant scaling process. Overall, this first global compilation of sap flow data will allow addressing uncertainties in sap flow upscaling in space and time in the same way that the development of FLUXNET stimulated the quantification and aggregation of uncertainties for eddy flux data  (Richardson et al. , 2012). While SAPFLUXNET makes global sap flow data available for the first time, we note that spatial coverage is still sparse and some forested regions are underrepresented in the database (Fig. 2a ). We note especially the relatively small number of datasets for boreal and tropical forests, two important biomes in terms of global water and carbon fluxes  (Beer et al.