dataesr/scientific-paragraphs-categorization huggingface dataset:
9 EFES is a fork of the open source XML publishing platform Kiln developed by the Department of Digital Humanities at King's College London between 2012 and 2019. See https://github. com/kcl-ddh/kiln and https://kiln. readthedocs. io/en/latest/ for the documentation. For a thorough analysis of EFES and its potential, see most recently Bodard, Yordanova forthcoming. magazén e-ISSN 2724-3923 1, 1, 2020, 101-128.
, 2002; Bard et al. , 2002; Cheng et al. , 2016). This may have led to a weaker overturning circulation due to the reduction of the density of the North Atlantic surface water, making the AMOC cell shallower during MIS 6 than during MIS 3  (Gottschalk et al. , 2020; Margari et al. , 2010). In order to investigate whether the different climate conditions between MIS 6 and MIS 3 could have impacted the relationship between atmospheric CO 2 and climate, we measured 177 new data points of atmospheric CO 2 concentrations from the EPICA Dome C (EDC) ice core (75 • 06 S, 123 • 24 E) spanning  MIS 6 (190 to 135 ka). This new CO 2 data measured within the scope of this study is complemented with two published datasets  (Lourantou et al. , 2010; Schneider et al. , 2013)  and one unpublished CO 2 dataset measured in 2003.
We use all of the available data from the EDC ice core to compile a composite dataset of atmospheric CO 2 covering MIS 6. In this work, we significantly improve existing records previously obtained from the Vostok ice core  (Petit et al. , 1999). The new composite dataset from the EDC core provides a temporal resolution of ∼ 230 years during MIS 6, as compared to 1000 years in the Vostok record. We also improved the relative age uncertainties between ice and gas in the EDC core using δ 15 N-based estimates of firn column thickness to better constrain leads and lags between peaks in δD (Antarctic Isotope Maxima, or AIM) in the EDC ice core and atmospheric CO 2 concentrations during the early MIS 6, thus establishing a new chronology using new δ 15 N data during the early MIS 6 in this study and published data from  Landais et al.
CH 4 measurements We measured the atmospheric CH 4 content of 63 ice core samples, using the wet extraction method at IGE as described in detail in  Spahni et al. (2005). This allowed us to improve the temporal resolution of existing CH 4 data  (Loulergue et al. , 2008)  from ∼ 600 to 360 years on the AICC2012 chronology (see Fig. S1  in the Supplement). The precision of the system is ∼ 11 ppb on average, which was estimated by blank tests. The previous CH 4 dataset  (Loulergue et al. , 2008)  from EDC has been produced at both IGE and CEP. CH 4 measured at CEP used to be systematically higher than CH 4 measured at IGE by 6 ppb  (Loulergue et al. , 2008). The offsets are due to differences in corrections for contamination caused by the analytical procedure between the datasets. Previously, 6 ppb had been added to the data obtained at IGE  (Loulergue et al.
, 2008). In this study, a new data correction was applied to data measured at IGE, and it is no longer necessary to add 6 ppb to the new data. The systematic offset between the new data and the previously published data points  (Loulergue et al. , 2008)  is only 1. 7 ± 2. 4 ppb (n = 63, the standard error of the mean) during MIS 6, (the datasets agree with each other within the measurement uncertainty).
Nitrogen isotopes Isotopes of molecular nitrogen in air bubbles were measured by a melting technique at the Laboratoire des Sciences du Climat et de l'Environnement (LSCE), France. The gas was extracted from the ice by a wet extraction technique, and the released air was analyzed by a dual-inlet mass spectrometer (Delta V Plus; Thermo Scientific). The analytical method and data correction are described in detail in  Bréant et al. (2019). In total, 151 samples from 88 depth intervals (63 duplicates) between the depths of 2124. 7 and 1875. 0 m below the surface were measured, corresponding to the time interval from 205 to 154 ka (Fig. S2  in the Supplement). The average resolution on the AICC2012 chronology is ∼ 580 years.
This can lead to age scale inversions on depth intervals up to 1 cm  (Etheridge et al. , 1992; Fourteau et al. , 2017). These layer inversions should not strongly affect our record, as its resolution is larger than the scale of age inversion events  (Fourteau et al. , 2017). However, the change in depth between two different depths (z) in the ice core, denoted ∂ depth/∂z, deduced from the raw δ 15 N data, shows some values higher than 1, that could correspond to small stratigraphic inversions in the gas phase (Fig. 2 ). Therefore, a three-point moving average weighted by the time difference between a point and its two neighbors was applied to the δ 15 N dataset. The weights for the three points are equal if the time difference is less than or equal to 500 years, which is close to the average sampling resolution of the δ 15 N dataset.
Data compilation We measured 177 samples of atmospheric CO 2 from EDC during MIS 6 using two extraction systems (the ball mill at IGE and CIM system at CEP). To improve the resolution of our new dataset even further, we made a composite dataset by aligning previous sets of CO 2 measurements made over the MIS 6 period on the EDC ice core to our new data. First, we compared the two existing CO 2 datasets and the new CO 2 dataset from EDC (Fig. 4  and Table  S5  in the Supplement). There are two published CO 2 datasets for EDC during MIS6 -the first measured using the ball mill system at IGE  (Lourantou et al. , 2010)  and the second using a sublimation extraction system at CEP  (Schneider et al. , 2013). We also added unpublished data measured in 2003 using the CEO needle cracker measurement system in 2003  (Monnin et al.
, 2004; Siegenthaler et al. , 2005)  (see Supplement for details). All records are on the AICC2012 air age scale  (Bazin et al. , 2013). All datasets are corrected for the gravitational fractionation effect using the new δ 15 N data in our study. In total, the datasets contain 237 CO 2 measurement points. Two samples were excluded because of system operator error. Figure  4  shows CO 2 concentrations measured by the ball mill system, sublimation  (Schneider et al. , 2013) , the CIM and the needle cracker. Concentrations from the CIM, sublimation and the needle cracker are systematically higher than CO 2 concentrations measured using the ball mill system (Table S5 in the Supplement). The offsets between each additional dataset and our data were calculated and corrected using a Monte Carlo procedure to account for uncertainty and a Savitzky-Golay filter used to remove noise from our dataset (Supplement).
The offsets between the multiple datasets may be at least in part linked to differences in extraction efficiency between the measurement methods. The sublimation and CIM systems have high extraction efficiency on clathrates and should therefore present more unbiased baseline CO 2 values. However, since these datasets are of much lower resolution, we have aligned all datasets to the baseline value of our ball mill dataset. Despite the systematic offsets, the other datasets confirm the millennial-scale variations shown in the data from this study (Fig. 4  and Fig. S7  in the Supplement). Although none of the individual additional datasets is of high enough resolution to show millennial-scale variations in detail, when aligned to our data the new data follow the millennial-scale variations with very few outliers.
Comparison with Vostok CO 2 record for MIS 6 The new composite atmospheric CO 2 record from EDC was compared to the existing CO 2 data from the Vostok ice core, measured using the ball mill system (Fig. S7  in the Supplement), where the CO 2 record from Vostok was aligned to the AICC2012 gas age scale  (Bazin et al. , 2013). Atmospheric CO 2 data from Vostok are corrected for the gravitational fractionation effect using the existing δ 15 N data  (Bender, 2002). Atmospheric CO 2 variability in the EDC ice core shows similar general patterns as those retrieved from the Vostok ice core, although previously unidentified features are resolved due to the improved temporal resolution. CO 2 concentrations from Vostok appear systematically higher than those from EDC by 4. 6±3. 0 ppm on average and the difference increases to more than 10 ppm at the beginning of Termination 2 at around 135 ka.
Grey dots represent atmospheric CO 2 from the Vostok ice core  (Petit et al. , 1999). The error bars of the CO 2 data from Vostok  (Petit et al. , 1999)  show the estimated overall accuracy for CO 2 measurements. The grey line represents δD of ice in the EDC core  (Jouzel et al. , 2007). In summary, the individual datasets used for our data compilation and the Vostok ice core record all show the same millennial CO 2 variability over MIS 6. Analytical and icerelaxation-related offsets could be removed to synthesize all records. This allows us to compile a precise and high-resolution record; however, the true absolute mean level of atmospheric CO 2 is only known to be better than 5 ppm, due to this offset correction. However, this has no impact on the conclusions drawn from the millennial CO 2 variability, which is the focus of this study.
Conclusions Using new and existing CO 2 data from the EPICA Dome C ice, we reconstruct a high temporal resolution record of atmospheric CO 2 during the MIS 6 period (189-135 ka). In this study, we investigate how different climate background conditions during the last two glacial periods may have impacted atmospheric CO 2. Millennial-scale atmospheric CO 2 changes are revealed during the last two glacial periods, with amplitudes ranging between 15 to 25 ppm, mimicking similar patterns in Antarctic δD variations  (Ahn and Brook, 2014; Bereiter et al. , 2012). On the other hand, during short NA stadials which last less than 1300 years, atmospheric CO 2 variations are negligible and decoupled from δD in EDC. This finding suggests that during the last two glacial periods the amplitude of millennial CO 2 variations is strongly influenced by the NA stadial duration (r = 0.
Acknowledgements. This work is a contribution to the " European Project for Ice Coring in Antarctica" (EPICA ), a joint  European Science Foundation/European Commission scientific program , funded by the  European Union  and by national contributions from  Belgium, Denmark, France ,  Germany, Italy , the  Netherlands, Norway, Sweden, Switzerland  and the  United Kingdom. The main logistical support was provided by  IPEV  and  PNRA. This is EPICA publication no. 316. It also received funding from the  European Community 's  Seventh Framework Programmes ERC-2011-AdG  under grant agreement no. 291062  ( ERC ICE&LASERS ). As it is part of the PhD work of  Jinhwa Shin , it was also supported by the  LabEX OSUGat2020  project of the  Grenoble Observatory of Sciences of the Universe (OSUG). The Swiss authors also acknowledge long-term financial support for ice core research at the  University of Bern  by the  Swiss National Science Foundation  under grants  200020_159563 ,  200020_172745 ,  200020_172506  and  20FI21_189533.
The authors would like to thank  Grégoire Aufresne  for providing assistance with the additional methane measurements,  Xavier Faïn  and  Kévin Fourteau  for their help with the CH 4 analytical system, and  Dominique Raynaud  for the valuable discussions. We thank  Eric Monnin  and  Urs Siegenthaler  for providing additional CO 2 data. We would like to thank  Julia Gottschalk  for the discussions about CO 2 variability and climate change during the last two glacial periods. Financial support. This research has been supported by the  ERC-2011-AdG  (grant no. 291062 ,  ERC ICE&LASERS) , the  Swiss Na-tional Science Foundation  (grant no. 200020_159563 ), the  Swiss National Science Foundation  (grant no. 200020_172506 ), the  Grenoble Observatory of Sciences of the Universe (LabEX OS-UGat2020  project grant), the  Swiss National Science Foundation  (grant no.
Data availability. Data available in the Supplement. All data will be available on the NOAA (National Oceanic and Atmospheric Administration) and PANGAEA (Paleoclimatology database websites) shortly. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/cp-16-2203-2020-supplement. Author contributions. The research was designed by JS, RG, FP and JC. The CO 2 measurements were performed by JS with contributions from GT, LS and BB. The data analyses were led by JS with contributions from JCB, RG, FP and JC and HF. The methane data was provided by GT and JC. The nitrogen isotopes data was provided by AL. JS wrote the manuscript with inputs from all authors.
Despite his increasing success and popularity, especially after Netflix acquisition of RPDR, the data emerging today show that in Italy RPDR has a cult following, but quite limited, starting from the narrow availability -on the Internet and on visual social media -of particularly frequented fandom places. Some Italian YouTube make RPDR a topic of discussion and sharing: Matteo Fumagalli (@mattfumagalli) is one of the most well-known Italian book influencers (or booktubers), and he has dedicated more than one video, as well as various Twitter posts, to RPDR. Video 8. Matteo Fumagalli on RPDR. In the video, we can listen to a free conversation about the show, where Fumagalli shows an in-depth competence, describing several characters. It could be considered a vision suggestion for newbies, but at the same time it is evident that he is sharing something which is widely known by his followers, as demonstrated by the large number of comments to his post.
Such varying user needs for environmental data are not restricted to LC (e. g. ,  van Dijk, 2014; Moomaw et al. , 2017). Although some excellent routinely produced LC products exist (e. g. , from the MODIS sensor and the Copernicus Land Monitoring Service), such standard products lack flexibility. Many LC products are also produced on an ad hoc basis with specific user needs in mind, but while satisfying Table  1  Land cover has a direct role for seven  UN Sustainable Development Goal Targets (GOFLCD, 2017; IA, 2017; UN-GA, 2015). Goal Target.
Earth observation data repositories Access to very large numbers of images is critical to developing accurate, useful, and timely LC datasets. The amount of freely available (i. e. , no-cost) EO data available today is unprecedented due to the open data policies that have been adopted within the last decade by United States Geological Survey (USGS) and NASA for Landsat, MODIS, and other sensors, and by the European Space Agency (ESA) and the European Commission (EC) for the Copernicus programs. Even so, important gaps in coverage remain, particularly in cloudy regions such as the biodiversity-richand rapidly changingtropical rainforests. Furthermore, accurate detection of LC changeone of the key applications of LC datarequires extensive image time series to provide sufficient accuracy; so the need for more data continues.
, 2018). The Copernicus program is currently the largest producer of freely available EO data globally. The Sentinel-1 radar mission, the Sentinel-2 high spatial resolution optical mission and the Sentinel-3 optical mission are now operational, each with a twin platform constellation. The Sentinels currently produce about 25 TByte of observation data each day, disseminated through ESA's primary distribution channels (i. e. , the Sentinel Data Hub) [https://scihub. copernicus. eu/dhus/]. For Sentinel-2AB, which likely has the highest relevance for LC mapping applications, routine production of global surface reflectance (L2A) data began in early 2019  (Szantoi and Strobl, 2019). The global Landsat archive holds more than 6 M unique scenes that provide more than 40 years of data, and higher-level science products such as surface reflectance are available through on-demand interfaces  (Wulder et al.
, 2019). In recognition of the value of Analysis Ready Data (ARD), which is gridded to a standard tile size, atmospherically corrected and complemented with masks for clouds, cloud shadows, snow/ice, etc. , ARD is now produced routinely for the US land area and global ARD production is being planned  (Siqueira et al. , 2019). Private EO imaging activities have also greatly increased in scope and imaging capacity. For example, Planet Labs' Dove microsat constellation acquires global images daily, and ICEYE is setting up a constellation of small SAR satellites. However, although these missions will be valuable, especially in combination with calibrated science missions such as the Sentinels, their data comes under a commercial licence model and thus their impact on future LC mapping may be limited.
In addition to satellite imagery, reference data are needed for training LC classification algorithms and validating LC maps. Reference data are often collected by visually labelling LC types using very high resolution satellite images. Reference data can also be collected in situ but the cost is considerably higher than visual interpretation. In fact, access to high quality reference data is often the limiting factor to generating useful LC datasets. The coordinated international program Global Observation for Forest and Land Cover Dynamics (GOFC-GOLD) promotes the sharing of reference data through their portal, though this represents only a small fraction of the data available. Reference data have been collected through Geo-Wiki, a tool for crowdsourcing land cover data from very high resolution satellite imagery  (Fritz et al.
Earth observation processing environments Today the main EO data repositories (i. e. , Sentinel and Landsat) are mirrored in commercial cloud environments in addition to the archives at their parent organizations. For example, Amazon Web Services (AWS) stores copies of both the global Sentinel and Landsat archives, and makes them available for cloud-based processing. Similarly, Google Earth Engine links to mirrored EO data in the Google cloud and provides highly scalable processing capabilities. In Europe, the EC has initiated five DIAS (Data and Information Access Services) systems. These distributed storage and processing environments are designed to simplify access to Copernicus data. Currently most DIAS systems host and provide access to the main EO data repositories, which are co-located within a distributed processing environment.
Most also provide access to a range of EO tools and offer a variety of pricing models (e. g. , pay per use, subscription). The commercial EO data repositories hold data from various sources, which allows the users to generate complex and/or continuous (time) datasets after harmonization, which is often done by the repository provider. ESA has initiated several Thematic Exploitation Platforms (TEPs, https://tep. eo. esa. int/), each dedicated to an EO thematic area such as Forests or Food Security, offering a wide range of predefined tools specifically targeted at non-EO expert users. The private EO industry is increasingly offering cloud-based EO services, for example, the Sentinel-hub (https://www. sentinel-hub. com) is now receiving 2 million requests to their cloud-based service per week.
It provides predefined tools for visualization and the possibility to script user-defined processing steps. Planet (www. planet. com), which operates a large constellation of Earth-imaging satellites, also offers an automated, cloud-based imagery and analytics platform. As the number of platforms providing access to EO data increases, so does the importance of data product standards and, more broadly, interoperability. This is because many products will require data sourced from different providers and, without standards, users will be forced to work with a cumbersome number of independently developed systems. Fortunately, the largest satellite data providers are already moving in this direction  (Siqueira et al. , 2019). For example, NASA now provides a Harmonized Landsat and Sentinel-2 (HLS) surface reflectance dataset for North America as well as some globally distributed areas at 30 m, while ESA is working on a pre-operational demonstration service, where they harmonize and fuse Sentinel-2 (S2) and Landsat 8 data at a common 10 m resolution.
Towards customized, on-demand land cover information products The wide range of LC products needed by users can best be met by processing systems able to accommodate user-defined tailoring via user-provided parametersfor the number, types and definition of classes, area of interest, and temporal needsand then generate, ondemand, the desired, customized product. While this has some challenges it is technically feasible, and here we explore the key characteristics of such systems as well as the challenges in developing and operating them. Such on-demand products would provide a valuable means for consistent national reporting across regions. Achieving this requires open data policies and freely accessible EO data archives that provide the necessary observation data at suitable temporal frequency, and spatial and radiometric resolutions to perform automated mapping.
Customized land cover products: user-driven systems One of the biggest hurdles to generating user-needs-specific LC products is that it is, traditionally, a labor-intensive process. Generating customized on-demand LC products, as well as consistent, periodic ones, requires automation, and although automated LC product generation itself is not new -NASA has been generating regularly updated LC products using MODIS data for yearsgenerating customized products requires a somewhat different approach. Advances in science and technology now make such systems possible and some of these advances are starting to be incorporated into existing systems. For example, USGS has developed a system called LCMAP that will largely replace the current approach for generating national (USA) LC products, using freely available Landsat (4, 5, 7 and 8) data.
The system is capable of using the entire Landsat archive (∼40 years) to generate land cover and land surface change products, and further support our call for the use of Analysis Ready Data  (Brown et al. , 2020)  since Landsat ARD is the fundamental data type for their processing chain. Tsinghua University in China is also developing a LC product generation portal that will allow users to specify the characteristics of the product they need by defining their own classification legend and samples through an interface and perform image classification with data available on the portal, or use a combination of user-collected samples and pre-collected ones stored on the portal to produce new LC products. The Copernicus Global Land Service (CGLS) in Collection 2 adopted the production of fractional cover layers to complement the core global land cover products.
g. , with the land parcel reference data for agricultural subsidies in the EU) but often simply the unwillingness to share labor-intensive datasets. 3 LC definitions are not harmonized among the different reference sources, e. g. , the definition of "forest" varies among countries and organizations. This happens because reference data are usually collected for specific applications and developers use their own nomenclatures. Part of this problem is also the lack of harmonized protocols for new reference data collections. 4 The lack of systematic data curation mechanisms is another key issue. Large quantities of reference datasets exist on old storage media scattered across research laboratories or even on paper from field surveys. A centralized, long-term data curation environment for reference and training data with standardized data formats, access and metadata is a critical requirement in this context.