dataesr/scientific-paragraphs-categorization huggingface dataset:
Similarly, valley magnitudes are the difference between the altitude of the ceiling vertically above pixel p and the altitude of pixel p. The distance from the city centre is an isotropic tendency term which implicitly describes the decrease in urban density (resulting therefore in a similar decrease in impervious surfaces) with distance from the city centre (Fig. 2d ). This tendency term is known to affect air temperature spatial patterns in urban environments  (Edmondson et al. 2016 ) and is computed as the Euclidean distance between each station and the city centre (Libération square: black cross in Fig. 1 ). Land cover descriptors have been derived using a hybrid product mixing the French BD TOPO database (version 2, 2020) developed by IGN and one satellite image from Pléaides (https:// www.
eopor tal. org/ satel lite-missi ons/ pleia des) acquired in August 2015 at 2 m resolution. The BD TOPO2 is preferred to other products (e. g. MAPuCE;  Bocher et al. 2018)  for consistency with other projects in which our team is currently involved. Vegetation in BD TOPO2 is obtained by automatic classification using supervised learning followed by a series of processing operations during which the unwooded vegetation-covered surfaces are removed (e. g. meadows and grasslands) for polygons of less than 50 ares (i. e. 5000 m 2 ). The Pléiades image is used to make up for this missing information by calculating the modified soil-adjusted vegetation index (MSAVI version 2;  Qi et al. 1994 ) based on the red (590 to 710 nm) and near-infrared (740 to 940 nm) spectral bands (Eq. 1). The MSAVI2 is an index designed to substitute the normalized difference vegetation index (NDVI) where it fails to provide accurate data due to low vegetation or a lake of chlorophyll in the plants  (Qi et al.
Acknowledgements We are grateful to  Météo-France  for providing weather parameters from the Longvic station. We thank the  Centre National d'Etudes Spatiales  for the acquisition and free access to the Pléiades image through the Dinamis catalogue (https:// catal ogue-dinam is. data-terra. org). Calculations were performed using HPC resources from  DNUM CCUB (Centre de Calcul de l'Université de Bourgogne). We also thank  PB Cerlini  and the other anonymous reviewer for their constructive comments.
Light Microscopy The ventral ovary was transversely cut in the median region to obtain 1 cm-thick pieces of gonad for each female. The tissue samples were fixed for histology in Davidson's fixative (10% glycerol, 20% formaldehyde, 30% ethanol 95 • , 30% sterile seawater, 10% acetic acid) at 4 • C for 48 h. They were then dehydrated in successive ethanol dilution baths, transferred to butanol (Carlo Erba, Val-de-Reuil, France), and embedded in paraffin wax (Roth, Lagny-sur-Marne, France). Five micrometer sections were stained according to the Prenant Gabe trichrome protocol  [18]. Microphotographs were taken with an Olympus AX70 microscope using the Olympus CellSens© software (v2. 3). Various cell types of the female germline were identified, and measurements of cellular structure sizes (i.
Quantitative Analysis of Ovarian Sections The procedure for quantitative analysis was previously described  [19]. Using a histology slide scanner (Aperio CS, Scan Scope Console software, v. 10. 2. 0. 2352), the slides were scanned and analyzed with the Aperio software (v12. 1. 0. 5029). To quantify the cellular structures, a stereological analysis based on Glagolev's  [20]  method was used. After detecting the cross sections on the scanned histological slides through segmentation using a threshold applied to the gray level image to extract the tissue area  [21] , a grid of 500 to 600 equidistant points was layered over the ovarian section using a random starting point. Each sampling point was assigned a single structure. This allowed for the computation of a percentage of times a structure was counted on a single slide, leading to a surface percentage for all ovarian structures.
The size class in which 50% of the female population is considered mature (L 50 ) was computed using a logistic regression between the maturity phase and the size using the function gonad_mature() of the SizeMat R package  [23]. The L 50 median and confidence intervals were estimated using a non-parametric bootstrap method with 1000 replications. The goodness-of-fit for the regression was evaluated using Nagelkerke's R-squared coefficient of discrimination  [24]. The L 50 assumptions describe the range in which maturity onset occurs, represented by a logistic curve centered on length  [25]. This curve indicates the point at which 50% of individuals start producing gametes relative to their body length, corresponding to the individuals presenting vitellogenesis. The data used to estimate this (L 50 ) was the maturity phase determined through stereology by implementing histological features previously described  [4] [5] [6].
Evolution during excavation and data collection During the excavation phases, dataloggers have been used to automatically collect data on some of the sensors. Four (4) measurements were realised every day (one every 6 hours) and stocked in the loggers. At the end of each excavation phase, the metallic pipe was sectioned to avoid risk of falling. To do so, the dataloggers were disconnected, the pipes were sectioned and then the dataloggers were reconnected. The measurements stocked in the datalogger were collected during this time to be processed.
Documentary climate data describe evidence of past climate arising from predominantly written historical documents such as diaries, chronicles, newspapers, or logbooks. Over the past decades, historians and climatologists have generated numerous document-based time series of local and regional climates. However, a global dataset of documentary climate time series has never been compiled, and documentary data are rarely used in large-scale climate reconstructions. Here, we present the first global multi-variable collection of documentary climate records. The dataset DOCU-CLIM comprises 621 time series (both published and hitherto unpublished) providing information on historical variations in temperature, precipitation, and wind regime. the series are evaluated by formulating proxy forward models (i.
Background & Summary Information on past climates has played an essential role in climate science 1. While historically the primary research focus has been on reconstructing past annual temperature, the questions raised nowadays with the help of palaeoclimatological data are multifaceted, including changes in the water cycle, the occurrence of weather and climate extreme events, and atmospheric dynamics. This, in turn, is a challenge for producing palaeoclimatic datasets. New approaches, such as off-line palaeodata assimilation 2-5 , provide past climate fields at increasing spatial and temporal resolution. However, all reconstructions essentially depend on sufficient high-quality data inputs. Current climate field reconstructions are largely based on relatively high-resolution proxies measured in natural archives such as tree rings, corals, speleothems, bivalves, sediments, or ice cores.
The PAGES 2k multiproxy database 9 , for instance, only includes 15 documentary proxy series. However, historians have compiled documentary climate information in databases such as EURO-CLIMHIST for many years 10 (note that qualitative weather descriptions are also found in databases of early instrumental meteorological data, e. g. , Rodrigo 11 ). In recent years, a major international effort has been done to promote the use of the archives of societies in climate reconstructions. The PAGES CRIAS working group (Climate Reconstruction and Impacts from.
the Archives of Societies) was founded in 2018 and is working towards that goal. The Palgrave Handbook of Climate History  12  provided a first global overview of documentary climate data organized in regional chapters. Based on this and many other sources, Burgdorf  13  recently inventoried documentary climate series from a literature and databases search. The inventory contains 688 entries; not all are publicly available, and some have not yet been digitized. Here, we publish a subset of the data inventoried in Burgdorf  13  , termed DOCU-CLIM. The dataset contributes to a global monthly palaeoclimate reanalysis starting in 1420 and is based on assimilating monthly-to-seasonal proxies, documentary data and instrumental data into an ensemble of atmospheric model simulations using an offline Kalman filter approach similar to Valler et al.
Methods Compilation and data rescue. Over the past decades, climate historians and historical climatologists have produced numerous datasets in which documentary data have been translated into quantitative climate information. However, as their focus is commonly regional or local, these data are often not submitted to global data repositories such as the NOAA World Data Service for Palaeoclimatology database (https://www. ncei. noaa. gov/ access/paleo-search/) but instead, published on project or personal websites, or, unfortunately still very often, not published at all. Even if documentary records are incorporated into databases, they may not always be organized in a manner suitable for climate scientists, particularly when working with time series. In this work, we focus exclusively on quantitative document-based time series data, representing a small, albeit underexploited, subset of the body of documentary climate data.
The variables of interest were temperature, precipitation, and wind (e. g. , onset of seasonal wind regime) and hence only records were compiled that provide information on one of these variables (note that some of the series also depend on further variables). As detailed in Burgdorf  13  , the focus was predominantly on English-language literature that was accessible electronically and in which the authors state that the series contains information on one of the three variables (publications about the Mediterranean area and Central Europe in other languages exist and may contain additional series). Except for phenological data, we used only secondary material to ensure the inclusion of expert source interpretation. This includes derived indices, generally accepted to quantify descriptive and qualitative documentary data  14  or even reconstructed time series in physical units.
The next step was to compile the actual data. Not all inventoried series are available in electronic form, and some are subject to a restrictive data policy. We downloaded the series from 14 databases (Table  1 ) and contacted Fig. 1  Flow chart depicting the generation of the documentary dataset. many authors directly in cases when a dataset was not available in a repository. However, we only compiled data series that are open access and allow us to redistribute the data under a CC-BY license. In addition to compiling existing documentary data series, we also rescued a significant amount of data (this includes some series we recently presented in another study  15  ). This concerns 137 ice phenology series and 5 precipitation series (Fig. 2 ). Note that some of the rescued data might be available electronically but we did not find it.
The single most important source was a compilation of freezing and thawing dates of Russian rivers by Rykachev  16  (see example in Fig. 3 ) and a follow-up compilation by Shostakovich  17. Some (few) series were measured from graphs published in the 1970s where the underlying data were unavailable electronically. Many of the datasets digitized in the 1970s and even 1980s have not made it into the era of electronic publishing and open data policies (a list of rescued series including the sources is given in Supplementary file DOCU-CLIM_ Rescued. txt). evaluation. To identify any climate signal contained in documentary climate data, we formulated forward models  18  based on instrumental global monthly climate fields. These models not only serve for evaluation in this paper but are directly relevant for climate reconstruction approaches including data assimilation.
We used temperature fields from BEST  19  and HadCRUT5 20 , and precipitation fields from GPCC  21  to extract the time series from the closest grid point to each documentary site. The number of overlapping years between proxy and climate series had to be superior to 20 years (for all African wetness/dryness indices, as an exception, we accepted 10 overlapping years as otherwise no evaluation would have been possible on the entire dataset). The starting date was usually dictated by the start of the reference dataset, the end date by the end of the documentary dataset (see exceptions below), but never later than 1950 CE in order to avoid calibrating a forward model in a period in which climate or environment are no longer comparable with earlier periods. The forward model took the form of a multiple regression model (see Fig.
4 ), in which a documentary series was expressed as a linear combination of monthly series of the corresponding driving variable (either temperature or precipitation). If the season or month was specified in the source (e. g. , monthly, seasonal, or annual indices), these months were used. If this information was unavailable, we used annual mean values. In the case of events that were indicated as a specific date (e. g. , phenological data), we also included lagged predictor variables (i. e. , temperature from one or several previous calendar months). The window to be included was determined in a backward selection approach. In this case, the models initially included 6 months prior to the Table  1. Overview of available global and national repositories and databases containing documentary evidence.
N all is the total number of series or databases available on the platform, and N docu is the number of those series based on documentary evidence and available prior to 1880 CE (from Burgdorf  13  ). Note that there may be overlap between the repositories. event in question (defined as the 90 th percentile), such that an entire growing season could possibly be covered. Then a selection was carried out, retaining only months that were significant at p < 0. 1. Insignificant months between two significant months were also retained. If no significant months were identified, no model was calculated. For the phenological series covered in Reichen et al. 15  , we made use of the more detailed information available. For instance, strongly skewed variables were transformed logarithmically, and we used the reference dataset and reference period given in the paper.
The procedure is sketched in Fig. 4. We then fitted regression models with a least-squares estimator. As a measure of goodness of fit, we used the correlation between the observed and modelled documentary series, along with the p-value. The following information on the evaluation is indicated in the example data file (Fig. 5 ): the reference period used (1829-1879 CE in this example case), the reference dataset (BEST), the model (monthly mean temperature of March and April; any transformation of variables would be indicated here), correlation, p-value, and error variance of the residuals. For some of the ice phenological records, we have also digitized nearby temperature records as the existing global databases did not have any data in close vicinity. These new data have been published in Reichen et al.
15  and Lundstad et al. 22  (https://doi. org/10. 1594/PANGAEA. 940724), and in these cases "REFDATA" is denoted by the label "station". It should be noted that, first, the residual error is not a measure of the error of the documentary proxy, but of the difference between the actual observation and the forward modelled observation (regression error). Consequently, it also contains the inherent error in the instrumental climate data and in the interpolation (this is the error required for data assimilation approaches). Second, this evaluation measures the error only in recent times when instrumental climate data are available. As a result, the quality of the documentary data in the earlier period may differ  23. For all proxies that do not have a sufficiently long (see thresholds above) overlap with instrumental climate fields or nearby station records, an independent evaluation was not attempted here.
This is indicated with an "NA" throughout the evaluation section of the data file. Other methods of evaluation are possible, but this requires more knowledge and hence we refer to the original publications. It would be possible to compare these cases with reconstructions such as EKF400v2 5 , a global, monthly three-dimensional climate reconstruction covering 1600-2003 CE. EKF400v2 is based on an off-line assimilation approach of proxy data (e. g. , tree-ring width, maximum late wood density), documentary data, and early instrumental data into an ensemble of atmospheric model simulations. However, in many cases the documentary data were assimilated in EKF400v2 (and hence datasets are not independent), while in cases where no information is locally available, EKF400v2 basically represents a model simulation, so no strong correlation is expected.
Accordingly, we use EKF400v2 only in Sect. 4 for a case study. Some documentary indices continue into the instrumental era as the authors have complemented them with degraded instrumental data or have used instrumental data in addition to documentary data. These data may then not be independent of instrumental data. These values were not removed from DOCU-CLIM. However, in the evaluation conducted in this study, the calibration period in such cases is limited to years before 1900 CE. Where available, information on whether a value was from a documentary source or from degraded observations was added to the "META" column.
Data Records The DOCU-CLIM dataset can be downloaded from the BORIS repository (https://boris-portal. unibe. ch/ handle/20. 500. 12422/207)  24. The dataset comprises 621 files (note that a monthly index series is split into 12 files), totaling more than 100,000 values (Fig. 6 ). Information on all series, including links to the original holding, is given in the readme-file of the dataset  24. The references of the original series are included in this paper (refs. 16, 17,  ). Th The files are in ASCII format with 27 columns and a variable number of lines. The files are structured in a way that allows for straightforward inclusion into data assimilation schemes (see Fig. 5 ). In each file, one line covers one year. As a consequence, monthly data are stored in 12 files, one for each calendar month.
7 ). During the 19 th century, when many weather stations were already measuring temperature in Europe, the number of documentary temperature series decreases and ice phenological records dominate. As an example of series in DOCU-CLIM, Fig. 8  shows three time series of freeze-up dates of Russian rivers. Ice phenology is the dominating type of documentary data in DOCU-CLIM during most of the 19 th century. While the Neva series is continuous, the one from the Ob in Barnaul has a long gap. The series from the Volga in Saratov are shorter and contain an outlier (freeze-up date: 14 Feb). Note that outliers were not filtered out for the following evaluation.
technical Validation For many of the time series, a technical validation was performed by the original authors. These evaluations include arguably the most specific, expert judgement, combining local knowledge on both the historical sources and the local-to-regional climate characteristics. Readers are encouraged to consult the original publication for specific details (references are indicated in the inventory file as well as in the data files; a link is also given to the inventory by Burgdorf  13  where further information is available). Here we report the results of our independent validation, as described in Section 2. In Fig. 9  we show correlations for forward models that we calibrated in gridded instrument-based datasets. All records that have no overlap with observations (and thus evaluation was not possible) are denoted by grey dotted circles.
We found robust and highly significant correlations across Europe, North America and Asia. Many of them are related to derived indices, plant phenology, or ice phenology. Somewhat lower but still highly significant correlations are found over South America. Spatially-varying correlations are found over Africa, where most of the series were evaluated based on only 10 years of overlap. Moreover, the precipitation dataset  21  , which the documentary data were compared with, may have substantial errors in these pre-1900 years. However, several significant correlations can be found in locations including southern Africa and Australia. The distribution of the correlations (Fig. 9 ) shows that an overwhelming number of series exhibits correlations above 0. 5 and the peak of the distribution occurs at correlations between 0.
Usage Notes The DOCU-CLIM dataset  24  provides climate information from documentary data with the main aim of facilitating climate reconstructions. The dataset contains information on the correlation with corresponding forward models. This information should be carefully considered before using the data. Although climate reconstruction is the primary aim, the data can also be used in the form of individual time series. To demonstrate the potential of this new documentary dataset for quantitative climate analysis, we present a case study for the year 1835. In January 1835, the volcano Cosigüina in Nicaragua erupted and released massive amounts of sulfuric aerosols into the atmosphere. It is considered one of the largest historical volcanic eruptions in the Americas and led to widespread environmental impacts  130.
We investigated temperature-related series, and precipitation or wetness/dryness-related time series, for the year 1835 CE. For temperature, we differentiated two extended seasons: boreal spring to summer (March to July), and autumn to early winter (August to December), as this division fits best with the material contained in the documentary data (thawing dates and spring/summer phenology, freezing dates and autumn phenology). Monthly series were averaged to these seasons. For precipitation and wetness/dryness indices, we considered annual indices or means of all seasons. As a reference we chose the period 1841-1870 CE (retaining only series with more than 20 years of data in this period) and standardized the series with respect to this reference. Finally, the sign of series was adjusted such that, for temperature, positive indicates warmer conditions (e.
The large-scale cooling and the drying of areas influenced by the African monsoon agree with the expected effects of a tropical volcanic eruption  131. Overall, our analysis shows that our documentary dataset (DOCU-CLIM)  24  can capture spatial climate variability associated with the prominent volcanic eruption of 1835. The DOCU-CLIM dataset  24  can be used for climate reconstruction, particularly for data assimilation, which can make full use of the data and the metadata provided on the forward modeling. Some documentary time series could not be validated and should be further analyzed. DOCU-CLIM is a global dataset  24  and can now be combined with other multi-proxy compilations such as the PAGES 2k 6 datasets, or instrumental datasets such as H-CLIM  22  , to generate new climate reconstructions.
acknowledgements This work was supported by the European Commission (ERC Grant PALAEO-RA, 787574) and by Swiss National Science Foundation project WeaR (188701). Simulations underlying EKF400v2 were performed at the Swiss National Supercomputing Centre CSCS. ET is supported by a Marie Skłodowska-Curie Action ("ITHACA-101024389", and the Government of Aragón through the "Program of research groups" (group H09_20R, "Climate, Water, Global Change, and Natural Systems").