dataesr/scientific-paragraphs-categorization huggingface dataset:
Here we describe the LegacyClimate 1. 0, a dataset of the reconstruction of mean July temperature (TJuly), mean annual temperature (Tann), and annual precipitation (Pann) from 2594 fossil pollen records from the Northern Hemisphere spanning the entire Holocene with some records reaching back to the Last Glacial. Two reconstruction methods, the Modern Analogue Technique (MAT) and Weighted-Averaging Partial-Least Squares regression (WA-PLS) reveal similar results regarding spatial and temporal patterns. To reduce the impact of precipitation on temperature reconstruction and vice versa, we also provide reconstructions using tailored modern pollen data limiting the range of the corresponding other climate variable. We assess the reliability of the reconstructions using information from the spatial distributions of the root-mean squared error of prediction and reconstruction significance tests.
Reconstruction methods Our reconstruction approach included  MAT (Overpeck et al. , 1985)  and  WA-PLS (ter Braak and Juggins, 1993)  by applying the MAT and WAPLS functions from the rioja package  (version 0. 9-21, Juggins, 2019)  for R (R Core Team, 2020) on our Northern Hemispheric fossil pollen synthesis. For each fossil location, we calculated the geographic distance between each modern sampling site and the fossil pollen record using the rdist. earth function from the fields R-package  (version 10. 3, Nychka et al. , 2020)  and selected a unique calibration set from modern sites within a 2000 km radius. For the reconstruction with MAT, we used the original pollen percentages of the selected fossil pollen taxa, looking for 7 analogues between the pollen data and the selected calibration dataset.
The dissimilarity between the fossil samples and the modern pollen assemblages was determined by squared-chord distance metrics  (Simpson, 2012; Cao et al. , 2014). For the reconstruction with WA-PLS, we used the square-root transformed pollen percentages in a leave-one-out cross-validation approach  (Cao et al. , 2014). In addition to the classic WA-PLS reconstruction, we provide WA-PLS_tailored. To reconstruct TJuly we "tailored" our modern training dataset with respect to the Pann range. For this purpose, we identified the range of the reconstructed Pann and extended it by 25% to both ends of the modern Pann range in order to reduce the influence of Pann on Tann and TJuly reconstruction due to co-variation. We applied the same method to the reconstruction of Pann. Tann and Tjuly were tailored by Pann; Pann was tailored by TJuly and, additionally, by Tann (illustrated for an example in Appendix Fig.
1 ). A statistical significance test  (Telford and Birks, 2011)  was performed for the reconstruction by using the randomTF function in the palaeoSig R-package  (version 2. 0-3, Telford, 2019). The reconstructed climate parameters were tested as single variables, as well as with partialling out the respective other variable. We applied a Canonical Correlation Analysis (CCA) to the modern training dataset in order to infer the explained variance in the modern dataset by using the cca function in the vegan R-package  (version 2. 5-7, Oksanen et al. , 2020). The ratio between constrained (1) and unconstrained (2) explained variance was determined for all modern training datasets. In total, we provide reconstructions for 2594 fossil pollen records, among them 670 records from Eastern North America, 361 records from Western North America, 1075 records from Europe and 488 Asian records (Fig.
However, such signals might be impacted by taphonomy of the record, for example pollen from azonal riverine vegetation might be over-represented in fluvially impacted pollen records. Access Earth System Science Data Discussions Preprint. Our dataset is based on taxonomically harmonized modern and fossil pollen datasets using a restricted number of taxa (i. e. , the most common 70 taxa on each (sub-)continent). Such an approach guarantees for reconstructions with a classification approach such as MAT. Our routine uses the modern pollen data from within a radius of 2000 km around the site of the fossil record. The information provided in the reconstruction metadata including number of modern pollen samples and ranges of reconstructed variables, allows an assessment of the modern dataset used for reconstruction.
Reconstruction method and LegacyClimate 1. 0 quality Climate reconstruction methods all have different strengths and weaknesses. MAT and WA-PLS used in this study heavily rely on extensive collections of modern assemblage data covering diverse climatic and environmental gradients and are applicable on a broad spatial scale. However, both methods may struggle with complex species responses, are sensitive to spatial autocorrelation, can only deal with a certain extent of non-analogous situations and may produce poor results in so-called "quantification deserts"  (Chevalier, 2019) , where fossil pollen is hardly preserved or nearby modern surface pollen samples are missing  (Chevalier et al. , 2020). Nonetheless, for reconstructions on a local or regional scale, MAT and WA-PLS are most commonly used in climate reconstructions.
The format of the modern and fossil datasets as well as the provided scripts could also be easily adapted to apply to other reconstruction methods such as CREST, a Bayesian approach that combines presence-only occurrence data and modern climatologies to estimate the conditional response of a given taxon to a climate variable  (Chevalier et al. , 2014 and 2020). Through numerous physical processes that vary with both location and time, temperature and precipitation are interconnected, especially within the extratropical regions  (Adler et al. , 2008; Trenberth, 2011)  and thus temperature and precipitation may not be treated as independent variables. Due to the numerical mechanisms in the transfer function, the correlation between both climate variables may reduce the reliability of the reconstructions.
, 2013; Marsicek et al. , 2018; Routson et al. , 2019; Kaufman et al. , 2020a and 2020b). Our dataset is therefore a valuable addition. It may be used in a multi-proxy approach, synthesizing marine and terrestrial records in order to assess temperature development during the Holocene and can help to highlight possible interdependencies between oceans and land masses and such contribute to the "Holocene conundrum" debate  (Liu et al. , 2014). Temperature reconstructions from proxy data indicate peak temperatures during the Holocene Thermal Maximum around 6000 years BP followed by a pronounced cooling trend toward the late Holocene  (Liu et al. , 2014; Bova et al. , 2021) , which is also visible in our pollen-based reconstructions (Fig. 6 ). In contrast, climate models simulate a monotonic warming throughout the Holocene, which resulted in the "Holocene conundrum" debate  (Liu et al.
2021 ). The dataset files are stored in machine-readable data format (. CSV), which are already separated into  Western North America ,  Eastern North America, Europe, and Asia  for easy access and use. The R code to run the reconstructions for single sites is available at Zenodo (https://doi. org/10. 5281/zenodo. 5910989;Herzschuh et al. , 2022) including harmonized open-access modern and fossil pollen datasets so that customized reconstructions can be easily established. Acknowledgements. We would like to express our gratitude to all the palynologists and geologists who, either directly or indirectly by providing their work the Neotoma Paleoecology Database, contributed pollen data and chronologies to the dataset. The work of data contributors, data stewards, and the  Neotoma community  is gratefully acknowledged.
We thank  Andrej Andreev ,  Mareike Wieczorek , and  Birgit Heim  from  AWI  for providing information on pollen records and data uploads. We also thank  Cathy Jenks  for language editing on a previous version of the paper. Financial support. This research has been supported by the  European Research Council  ( ERC Glacial Legacy 772852  to UH) and the  PalMod Initiative  ( 01LP1510C  to UH). TB and MC is supported by the  German Federal Ministry of Education and Research (BMBF)  as a  Research for Sustainability initiative  (FONA; https://www. fona. de/en)through the  PalMod Phase II  project (grant no. FKZ:  01LP1926D ). CL holds a scholarship from the  Chinese Scholarship Council  (grant no. 201908130165 ). NR work was supported by the  Russian Science Foundation  (Grant No. 20-17-00110 ).
-M. , Stenni , B. , Syrykh, L. , Tarrats, P. , Taylor, K. , van den Bos, V. , Velle, G. , Wahl, E. , Walker, I. , Wilmshurst, J. , Zhang, E. , and Zhilich, S. : A global database of Holocene paleotemperature records, Sci. Data, 7, 115, https://doi. org/10. 1038/s41597-020-0445-3,2020a. Kaufman,  D. , McKay ,  N. , Routson , C. , Erb, M. , Dätwyler, C. , Sommer, P. S. , Heiri, O. , and Davis, B. : Holocene global mean surface temperature, a multi-method reconstruction approach, Sci. Data, 7, 201, https://doi. org/10. 1038/s41597-020-0445-3,2020b. Li, C. , Postl, A. K. , Böhmer, T. , Cao, X. , Dolman, A. M. , and Herzschuh,  U. : Harmonized  chronologies of a global late Quaternary pollen dataset (LegacyAge 1. 0), Earth Syst. Sci. Data Discuss. [preprint], https://doi. org/10. 5194/essd-2021-212, in review, 2022.
Despite the intrinsic link to human activities and economies, forests remain under constant threat of conversion and disturbance. Climate change threatens forest goods and services, as well as the relationships between humans and forests including traditional ones, affecting all stakeholders who expect ecosystem services of forests into the future  12, 13. There have been many targeted efforts in the last decade to accurately estimate deforestation and forest degradation over time using earth observation data and help countries meet their climate targets and engagements. These datasets provide unprecedented, up-to-date information on various aspects of forest change for global or tropical forests. While there is a great scientific interest in providing wall-to-wall global forest data products and analysis, these data alone are often not sufficient for robust, accurate deforestation estimates and trends  14  and are often not relevant to decision making at national or subnational scales.
Objectives This study provides an open-source, statistically validated DD assessment at the regional scale covering six countries of the Congo Basin over the 2015-2020 time period. A dedicated approach was developed to make best use of all available global datasets and dense satellite time series to quantify forest cover disturbance and discriminate deforestation from degradation. The derived products were validated by a team of regional experts to enhance the validity to central African conditions. The relative contribution of multiple anthropogenic direct drivers in relation to deforestation and degradation were determined, and the impacts of changes within fragmentation classes, forest types, and their associated biomass were quantified. The methodology is globally applicable, replicable, and open access to support decision making activities such as land use planning.
Trends in Forest Disturbance (2015-2020) In comparison with existing global datasets on forest disturbances, we observe similar trends in the 2015-2020 time period for Congo Basin countries. Data from Global Forest Change (GFC)  75  and Tropical Moist Forests (TMF)  62, 76  report higher rates of forest disturbance after 2015, which decrease from 2017 until an increase in disturbances in 2020 in contrast to our study, which observes a smaller area of deforestation in 2020, with an increase in degradation. Estimated burned area has also been observed to be generally decreasing since 2001  77  which could be associated with the trends we are observing, particularly related to the drivers of change (see below). The FAO Forest Resources Assessment Remote Sensing survey also observed an overall slowing of deforestation in 2010-2018 in comparison to 2000-2010, which diverges from the Global Forest Resources Assessment (2020) which identifies an increase in rates of loss between 1990 and 2020  71  which demonstrates how different methodologies may not reach consensus, and that assessments should be tailored to regional or national scales.
This total area of disturbance is not insignificant, and justifies climate concerns from the international community  88. There are differences in rates of disturbance within the large study area -while countries might be classified as HFLD, some sub-jurisdictions are hotspots of change with higher rates of forest disturbance than the region  18, 33, 89, 90. We refrain from direct comparison between sample-based area assessments from our study with pixel counts from global products as it is fundamentally flawed. Global data have not been statistically validated, and the omission and commission errors are simply unknown, making direct comparisons impossible. Additionally, areas of forest loss reported by Global Forest Watch (GFW) from the Hansen dataset may overestimate forest disturbances relative to other datasets, as this product identifies tree cover loss, which is not necessarily deforestation  14, 78.
Image Composites Best pixel image composites were developed to provide cloud-free imagery for the 2015 baseline year. In some locations, up to three years of data (as early as 2012) were needed to fill gaps from clouds. The SEPAL optical mosaic module was used to develop medoid composites from all Landsat satellites, applying BRDF correction and excluding pixels in the 50% percentile of NDVI values. A radar composite was created from ALOS Palsar 2015 backscatter data, with a layover/shadow mask applied, with a quegan filter  124  and an additional band for the radar forest deforestation index (RFDI)  125.
Regional Land Cover Classification We developed a regional land cover achieved by synthesising vegetation classifications from each of the six countries and harmonising them using the universal Land Cover Classification System (LCCS) framework  127. The integration of four different national forest definitions was performed by assigning the specific percent tree cover thresholds from each country's definition (Table  4 ). Next the satellite image composites along with auxiliary information on elevation  128  , the percent tree cover dataset, and water sources  129  , were classified into the defined 19 land cover classes. A supervised training algorithm was executed in SEPAL using the random forest machine learning approach, calibrated with 2,190 training points provided by partners and derived from visual interpretation of 5m resolution image mosaics for 2015-2020 provided by Planet, through a program financed by the Norwegian Government (NICFI), along with other high-resolution images using Collect Earth Online, a tool provided by the Open Foris Initiative of the FAO  130.
Additional cleaning steps were applied, including defining montane and sub-montane forests according to elevation criteria; removing mangroves that were mapped inland or above 35m elevation; data on seasonal and permanent water areas  129  were used to identify aquatic grasslands and water bodies, while the Global Human Settlement Layer (GHSL)  131  was used to correct the developed areas class. We integrated the official 2015 national land cover data for Gabon  132. These classes were recoded into forest/non-forest based on the appropriate national forest definition (% tree cover) to effectively mask and target the analysis area.
BFAST demands large processing resources. In order to effectively analyse the entire study area, the study region was divided into 508 100km square blocks, which were then grouped into 103 batches comprising 4-6 connected blocks and distributed among the project partners to process in their SEPAL accounts. The results were then re-assembled for the region after processing. The raw outputs from the BFAST module in SEPAL consist of a 2-band image for each tile, which include the magnitude of change (positive or negative) at the estimated date of the detected break. The simplest approach to determine changes from magnitudes and breaks was applied using the mean and standard deviation of magnitudes by forest class. BFAST date and magnitude outputs were post-processed by forest type to separate stable areas from change, with more extreme negative magnitudes (mean + 2 standard deviations of magnitude for specific forest type) classified as deforestation, and smaller magnitudes (mean + 1 standard deviation of the magnitude for specific forest type) as degradation  136, 138.
Validation Validation data points were visually interpreted to identify forest type, change (deforestation, degradation or stable), the date of change and presence of direct drivers was performed using OpenForis Collect Earth Online, and in particular, the high-resolution optical image mosaics provided by Planet since 2015. A stratified random sampling scheme was developed to select spatially balanced samples that are proportional to map classes  61. Random samples were selected from the BFAST stratification layer with enough samples to achieve the desired confidence interval of 0. 05 and at least 150 points per class. This resulted in 359,978 random points distributed according to map class area (Table  5 ). However, as actual forest changes are rare, this results in a very large number of points that are potentially stable, which would be inefficient for visual interpretation assessments.
Direct drivers We first evaluate the location of disturbances with respect to roads and settlements, using available vector road data  37  converted to raster with the same resolution as the forest cover and change products (30m). Best available settlement data  143  were scaled to the same resolution combined with the roads to create a combined layer. Euclidean distance was calculated in QGIS (version 3. 22. 7)  144 d classified into 500m buffer rings, and the area of deforestation and degradation for each year was summed in each distance class. The identification of multiple drivers allow the understanding of the local context. Eight observable drivers were defined according to their visibility in high resolution satellite imagery available In Collect Earth Online. One or more drivers were observed within a 2km plot around all sample points.
Rapidly varying topography may inhibit the retrieval of good elevation data. Water level accuracies are also dependent on target size and surface roughness, which will limit worldwide surveying and limnological applications. However, there is the scope for systematic continental scale monitoring and the provision of new stage information where gauge data is absent. Anyway, the technique is sufficiently advanced to have enabled a number of inland water case studies. Lake water level time series are gathered in several on-line databases (DAHITI  [16] , Hydroweb  [17] , G-Realm  [18]  or TEP  [19] ) with contributions from altimeter observations. Altimetry has been originally designed to measure sea level height  [20]   [21]  and the first altimeters (TOPEX/Poseidon, Jason-1/Poseidon, Envisat/RA) have been optimized for the ocean.
F. CPU and memory usage LPP has been optimized in term of CPU and memory usage. 500 MB memory and 30 seconds of CPU time are required per km. It remains some margins of improvement to get a full operational processing for ground segments, which was out of the scope of our work. VI. VALIDATION In the following sections, our technique will be called Lake Processing Prototype (LPP). LPP has been applied on two set of lakes in two different regions: Occitanie (South of France) and Switzerland. Whole S3A and S3B missions have been processed from the beginning up to July, 2020 (which corresponds to 52 passes for S3A and 21 passes for S3B). Lake contour are extracted from CARTHAGE database  [53]  for French lakes and from SWOT Lake database  [52]  for Swiss lakes. Daily in-situ measurements have been provided by EDF (Electricité De France) for Occitan lakes, and by OFEV (Office Fédéral de l'Environnement, Switzerland, https://www.
B. Switzerland lakes Sentinel3A and Sentinel3B data have been processed using the LPP over 17 lakes in Switzerland for 24 virtual stations in total. Characteristics of each virtual stations are given in table  3  and figure  17  provides a map of the region. They are small (Silvaplana, Sils), middle size (Thoune, Zoug, Walenstadt, …) and large lakes (Geneva, Constance, Maggiore) in mountainous areas. This set offers a high variety of situations. Some overflights do not represent any difficulty, with long transect and cross-track distances to land, for example: Geneva-N4644, Constance-N4752, Neufchatel-N4683 or Lugano-N4595. Others have shorter transect and/or satellite overflight close to land, for example: Luzern-N4698, Zurich-N4722 or Brienz-N4675. Here the complexity of the scene is due either to the surrounding relief or to the relative position of the tracks with respect to the lakeshore.
C. Comparison to HYDROWEB The Hydroweb database (http://hydroweb. theia-land. fr) gather water surface height time series over rivers and lakes. The calculation is done using the OCOG retracking measurement, with editing on the backscatter coefficient and the geographical location of the measurements. In contrast to the LPP data processing, if the nadir point measurement is out of the lake contour, the lake is excluded. The editing is thus more severe leading to less observations. Some additional outliers are also removed using a filter which considers historical water level variations in an iterative process. Details on the full calculation is given in  [Cretaux and Birkett, 2006]. In this section, we compare the WSH using the LPP method to the WSH from Hydroweb using range from OCOG retracker which is released in the PDGS.
For the Swiss lakes, we simply have extracted the WSH of the lakes from Hydroweb. The Occitan lakes are not included in Hydroweb: we have recalculated the WSH using the OCOG retracker and the same data processing as in Hydroweb. As shown previously, the sizes of the lakes vary from hundreds of meters of width for the smallest to few kilometers for the largest. The surrounding topography is very different from one site to another: some are in mountainous areas, whereas others are in open areas. This influences drastically the quality of the data processing with the approach used in Hydroweb. Moreover, for the lake we have drawn special attention on tracks that are in the close vicinity to lakeshore. For the narrower lakes in Swiss Alps and in Occitanie, the OCOG retracker fails to retrieve a WSH.
But for all others, an accurate WSH has been retrieved in Hydroweb. The estimated height time series were then compared to in situ data, as done with LPP, and it highlights that the performances with OCOG remain quite acceptable with accuracy at the decimeter level for the narrowest at the centimeter level for the biggest Lake Geneva. For these lakes, LPP and Hydroweb give similar performances while for narrow lakes or for tracks very close to the shoreline (as over lake Constance with S3A track 313, see figure  26 ) the performances are at least twice better for LPP than for Hydroweb. All the results are summarized in the figure  28. Indeed, Lake Constance (S3A-313 N4766) presents a complex overflight. The LPP data processing is not at all degraded, the accuracy remaining at the level 5 cm.
For the processing using OCOG/Hydroweb, the WSH accuracy is significantly degraded to around 20 cm. This clearly shows that the LPP data processing is very robust whatever the geometrical configuration, since OCOG remains accurate only in favorable conditions. For the majority of large lakes, OCOG can be used with high accuracy as demonstrated in many publications, while for more complicated cases the LPP is significantly better. This may allow increasing considerably the potential of the Sentinel-3 (A and B) missions to survey an increase number of small lakes worldwide than what is currently done, in particular within the global database like Hydroweb, GRML or Dahiti. The comparison between in situ, LPP and OCOG results over the reservoir in Occitanie, further accentuates those obtained in Switzerland (Figure  28 ).