dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods In situ CO 2 mole fraction observations. We use discrete (weekly) air samples from 105 sites and continuous (hourly) observations from 52 sites that are part of the global atmospheric surface CO 2 observations network. These were taken from the Observation Package (ObsPack) obspack_co2_1_GLOBALVIEW-plus_v2. 1_2016_09_02 data product  7  for 2015, and from obspack_co2_1_NRT_v3. 3_2017-04-19 for 2016-2017  8  ; both datasets are produced by the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL). Satellite observations of column CO 2. We use X CO2 data retrieved from the Japanese Greenhouse gases Observing SATellite (GOSAT) and the NASA Orbiting Carbon Observatory-2 (OCO-2). GOSAT  11  was launched in January 2009 in a sunsynchronous orbit with an equatorial crossing time of 1300.
Gravity recovery and climate experiment. The Gravity Recovery and Climate Experiment (GRACE) provides information about changes in the water column  [37] [38] [39]. Rooting depths of tropical terrestrial ecosystems will likely be sufficiently deep that we cannot establish a direct and immediate relationship between vegetation and changes in precipitation. Changes in gravity, due to changes in water column depth, provide a much stronger relationship with vegetation access to water. We use the surface mass change data based on the RL05 spherical harmonics from CSR (Center for Space Research at University of Texas, Austin), JPL (Jet Propulsion Laboratory) and GFZ (GeoforschungsZentrum Potsdam). The three different processing groups chose different parameters and solution strategies when deriving month-to-month gravity field variations from GRACE observations.
Satellite observations of solar induced fluorescence (SIF) are retrieved by the UoL from the GOSAT instrument  46. SIF is a by-product of plant pigments absorbing incoming sunlight as part of photosynthesis. Of the solar radiation absorbed, ~20% is eventually dissipated as heat and typically <1-2% is emitted by SIF in the range 650-800 nm, peaking at 685-690 nm and 730-740 nm. GOSAT fits estimates of SIF at 755 nm  47. We use the GOSAT SIF data product as a crude measure of photosynthetic capacity of regional ecosystems. We use a physically based retrieval scheme  47  with a focus on the bias correction procedure. We use a two-stage method. First, we isolate GOSAT measurements over non-vegetated areas using the ESA CCI Land Cover product V2. 0. 7  48  at 300 m resolution. Second, we apply a bias correction as an explicit function of time to ensure that instrumental effects are accounted for the entire date range of the SIF product.
DM burned estimates. DM burned estimates are taken from the Global Fire Emission Database 49 (GFED4). These estimates were derived by combined by satellite remote sensing observation of burned area and active fire data from MODIS. Atmospheric transport models and inverse methods. To describe the relationship between surface fluxes of CO 2 and atmospheric CO 2 we use three atmospheric transport models: (1) GEOS-Chem global 3-D chemistry transport model  50, 51  v9. 02; (2) GSFC parameterised chemistry and transport model  52  (PCTM), and (3) Laboratoire de Météorologie Dynamique (LMDZ), version LMDZ3  53. We run GEOS-Chem with a horizontal resolution of 4°(latitude) × 5°( longitude), driven by the GEOS-5 meteorological analyses (GEOS-FP from 2013) from the Global Modeling and Assimilation Office (GMAO) Global Circulation Model based at NASA Goddard Space Flight Center.
Acknowledgements P. I. P. , L. F. , and H. B. acknowledge support from the  UK National Centre for Earth Observation (NCEO). The  Natural Environment Research Council provides national capability funding for NCEO  (grant #  PR140015 ). P. I. P. also acknowledges his  Royal Society Research Merit Award, and discussions with Chris O'Dell (CSU) , the wider OCO-2 science team,  Bertrand Guenet  (LSCE),  Ian Baker  ( CSU ),  Seifu Kebede  ( Addis Ababa University ),  David Mattey  ( Royal Holloway, U. London ), and  Tom Pugh  ( U. Birmingham ) on the results reported. F. C. was funded by the  Copernicus Atmosphere Monitoring Service , implemented by the  European Centre for Medium-Range Weather Forecasts (ECMWF)  on behalf of the  European Commission , and was granted access to the HPC resources of TGCC under the allocation  A0030102201.
H. B. also acknowledges funding from the  Copernicus Climate Change Service   C3S. We thank  Jasdeep Anand  for processing the University of Leicester GOSAT L2 data, distributed via the  C3S  and  ESA CCI  projects. We gratefully acknowledge all CO 2 ground observations contributors to the ObsPack and to  NOAA ESRL  for maintaining this database. We gratefully acknowledge the  NASA MODIS, OMI , and GRACE teams for providing their L2 data. We also thank the broader GOSAT team who provided their L1 data. The ACOS GOSAT and OCO-2 data were produced by the  ACOS  and  OCO-2  projects at the  Jet Propulsion Laboratory, California Institute of Technology. The ACOS/GOSAT data were obtained from the  JPL  website, co2. jpl. nasa. gov,while the OCO-2 data were obtained from the  OCO -2 data archive maintained at the  NASA Goddard Earth Science Data and Information Services Center.
Data availability GOSAT V7. 1 and SIF data are available from University of Leicester. OCO-2 retrievals were produced by the OCO-2 project at the Jet Propulsion Laboratory, California Institute of Technology, and obtained from the OCO-2 data archive maintained at the NASA Goddard Earth Science Data and Information Services Center. All correlative data are also freely available from NASA data repositories. The NOAA in situ data are freely available from the ESRL website (https://www. esrl. noaa. gov/gmd/ccgg/trends/data. html).
Code availability The community-led GEOS-Chem model of atmospheric chemistry and model is maintained centrally by Harvard University (http://acmg. seas. harvard. edu/geos/), and is available on request. The ensemble Kalman filter code is publicly available as PyOSSE (https://www. nceo. ac. uk/data-tools/atmospheric-tools/). The CAMS inversion system is available on simple request from FC. For access to PCTM, please contact DB.
The present study conversely assessed dredging impacts on maerl beds across a quantified and continuous abrasion pressure gradient representative of the range of the dredging footprint observed within the study area (Figs  1, 2 ). Because of the very fine spatial scale at which both dredging intensity and maerl bed structure were assessed, this approach therefore allowed for the establishment of real world pressure/impact relationships curves (Fig. 4 ). It should be underlined that this kind of dataset has been identified as the only way to go forward for an accurate estimation of the effect of fishing on biogenic habitats useful for effective protection  44. The shapes of these curves varied depending on the precise nature the relationship between the Maerl live/dead ratio, indicative of the vitality of the bed forming algae, clearly responded to dredging pressure according to an exponential decay tending toward 0 (Fig.
establishment of high resolution dredging pressure map. Spatiotemporal changes in the physical disturbance generated by clam-dredging activities have been assessed based on AIS (Automatic Identification System) data emitted by each fishing vessel working in the study site. This was facilitated by the strict regulation of clam-fishing in the bay that: (1) requires the fishing vessels to carry AIS transceivers, (2) only allows fishermen to daily work during a 2. 5 hours' time-slot (between 9:00 and 11:30 am) and (3) restricts the gears that can be used to a single 1. 5 m wide dredge. Briefly put, AIS recorded vessels id, position and speed at a high frequency. Vessels targeting clams were identified based on information from the The Comité Départmental des Pêches Maritimes et des Elévages Marins du Finistère (committee for marine fisheries and aquaculture for the Finistère department) that regulates the fishery, and typical fishing speed (when the vessel is actually dredging) for the fleet was estimated as between ca.
2. 5 and 5 knots. It was therefore possible to calculate the length and position of each dredging event. These data were then aggregated within 2500 m² cells (50 m × 50 m squares) and multiplied by the width of the dredge. So-obtained dredged areas were cumulated for the 5 last fishing seasons (2012-2017), and dredging pressure was expressed in times the considered cell had been totally dredged and mapped (Fig. 1B ). The choice of cumulating pressures over 5 years was motivated by the very slow growth rate of maerl algae and by their low resilience capacity to dredging  20. Calculation was done using R programing framework (R core team), and the map produced using QGIS software (QGIS Development Team, http://qgis. osgeo. org).
5 cm) between 8 and 11 cores per station were sampled. Maerl thalli were then carefully washed and dried. Thalli were then gently placed onto a mat black background and photographed using a Canon R EOS 500D digital camera (15. 1 Mpixels definition) for further image analysis. image analysis. The 450 so-obtained SPI images (6 M pixels resolution) were analyzed semi-automatically using a modified version of the SPIArcBase software  36. For each image, the water-maerl interface was first drawn manually. Each pixel below this interface was therefore automatically qualified as belonging to live or dead maerl, or interstice (that can be water or sediment, indistinctly) based on its RGB color composition. The distinction between live and dead maerl has been achieved according to a unique threshold in the red channel set on the basis of good agreement between software classification and expert (JG) judgment within a selected subset of images.
Solidity index then reflects the degree of complexity of maerl thalli and is comprised between 0 (fully complex shape) and 1 (no ramifications). These analyses were performed using a custom macro developed on ImageJ 1. 5. 2 free software  56. Data analyses and statistics. All SPI measured metrics were assessed using their Station -scale averages and standard deviations, the latest as a proxy for station-scale spatial heterogeneity. The relationships between dredging intensity and: (1) interface rugosity, (2) SPI penetration depth and (3) maerl live/dead ratio were qualified using exponential decay, exponential and linear combination, or polynomial cubic regression models. They were as well tested using the discretized grouping onto the three dredging intensity groups (Control, Moderate, High) using univariate Permutation Analysis of Variances (PERMANOVAS) and associated dispersion tests (PERMDISP, 57) on untransformed data.
Acknowledgements This work has been performed in the frameworks of the IMPECAPE research project granted by the  French agency for biodiversity (AFB)  as well of the  JERICO-NEXT European Horizon 2020 research and innovation program  under grant agreement No. 654410. The authors wish to thank the crew of the  R/V "Albert Lucas"  for valuable work during the sampling campaign as well as  Vincent Le Garrec  from for the establishment of fishing pressure files.
Here we present an atlas of subsistence livelihoods in prehistoric West and western Central Africa, based on a broad synthesis of archaeological, archaeobotanical, archaeozoological, historic, linguistic, genetic, and ethnographic data. The atlas uses a classification system we outlined in an earlier paper  (Kay and Kaplan 2015) , which is revised and simplified here to focus on agricultural livelihoods. The purpose of our maps is to represent in space and time the distribution of various livelihoods and therefore land uses. The maps and their underlying database will have a wide range of uses, including informing quantitative human-environment models, qualitative interpretation of palaeoenvironmental records, and future field data collection efforts.
The data for this study were primarily collected from published work. In addition, several of the authors contributed unpublished data or regional interpretations based on their field experience. In order to facilitate the collection and synthesis of large amounts of archaeological data, we constructed a database of archaeological sites, types, coordinates, contexts, radiocarbon or other dates, archaeobotanical and faunal data, and other information pertaining to land use, such as presence/absence of various metallurgical evidence, or soil management techniques. This database is an ongoing project that, at the time of writing, contains records from 1866 archaeological sites, including 3734 radiocarbon or luminescence dates. This database will be made publicly available online on the completion of the project, and a summary table is included as Appendix A (online supplementary material).
While primary site data were consulted whenever possible, there are also secondary sources in the database. The use of secondary sources, particularly previous syntheses, greatly facilitated our initial data-gathering efforts, and saved significant time in the collation of thousands of datasets. We have made a good-faith effort to make the database as complete as possible; however, there are likely still sites missing, or analyses to be entered. The database is therefore a work in progress, and will be as long as new research is conducted in the area. Despite this, the current geographic and temporal coverage is sufficient to allow the utilization of this resource. It should be noted that the evidence we are using to inform these maps is very sparsely distributed and is in many areas insufficient to reconstruct diet and land use with any degree of certainty.
It is hoped that in the future a detailed study of the historic records might be able to refine the 900-year range of our last three maps down to a centennial timescale. The maps were created by manually drawing polygons in ArcGIS, based on the authors' broad understanding of temporal and geographic trends; the polygons were then refined by comparing the boundaries to data available from sites in the database. The size of Lake Chad-and therefore the potential distribution of fishing around it-was roughly mapped according to the water levels published by  Maley (2000)  and  Brunk and Gronenborn (2004) , and shapefiles for different time-windows were produced using the ArcGIS spatial analyst contour tool on the Multi-Error-Removed Improved-Terrain DEM (MERIT-DEM) published by  Yamazaki et al.
Further south, in the Banda region of Ghana, sites such as Kuulo Kataa and Ngre Kataa contain evidence of the continued reliance on diversified crops during the Ngre and Kuulo periods. The large faunal dataset from these sites contained mostly wild animals with only a few definitively domestic animals included in the assemblage  (Logan 2012). Even further south, this area of diversified farming supported many different types of settlement type and population density, from dispersed rural farming villages to large urban centers along established trade routes. Some of these larger sites, such as Begho, Benin, Ile-Ife, and Oyo-Ile, were not only trading centers but also sociopolitical hubs with control over extensive hinterlands  (Ogundiran 2013).
Unfortunately, the use of flotation and other archaeobotanical sampling techniques is a relatively recent practice, and is still not standard on many excavations. Only 23% of the sites in our database (n = 425) have associated archaeobotanical results. Other types of data collected that are indicative of land use include: faunal remains, present at 7% of sites (n = 131); presence of metallurgy or other technological indicators; soil charcoal, indicative of burning frequency. General notes about the type of site can also give clues as to land use. Many archaeological publications focus on ceramic styles or architecture, which are less reliable sources of information concerning livelihood, but can indicate degrees of sedentism or cultural affiliation to other sites. We have provided a list of archaeological sites with coordinates, references, and associated data types in Appendix A (Table  2 ) (online supplementary material).
Further uncertainty is present in the source data due to the fact that a major radiocarbon calibration plateau falls within our period of interest  (Hajdas 2008) , and to the large standard error ranges in many of the older dates. While Bayesian agedepth models can be used to reduce the uncertainty associated with flat sections in the calibration curve  (Steier and Rom 2000; Steier, Rom and Puchegger 2001) , few archaeological sites in western Africa have been sampled at sufficient resolution to make this a viable solution for large regional-scale studies. Finally, many of the sites in the database are not radiometrically dated: only 61% (n = 1126) have at least one radiocarbon or thermoluminescence date (n = 3734). The remaining sites have been assigned to a general period, culture, or technological tradition for which a regional date range is known.
The numbers of sites with archaeobotanical analysis or radiocarbon dates are not the only uncertainties associated with the archaeological distribution; the spatial coverage of archaeological studies has been greatly affected by modern socio-political boundaries. Particularly poorly-represented archaeologically is the southwest portion of our study area (see Fig. 2 ). Our database contains only 44 archaeological sites in Guinea (n = 1), Guinea-Bissau (n = 0), Sierra Leone (n = 3), Liberia (n = 16), and Côte d'Ivoire (n = 24) combined. The sites in Côte d'Ivoire are either along the coast, or clustered far inland towards the borders with Mali and Burkina Faso. This leaves a broad swath of territory, from eastern Côte d'Ivoire at the Ghana border to the coast of Guinea-Bissau, that is unrepresented in the archaeological data.
2018; Garcin, Deschamps et al. 2018b, c; Giresse et al. 2018). Our maps provide a spatially continuous dataset that can be compared with pollen and other environmental archives throughout the study area. The category definitions and maps presented here also provide valuable information on land use that can be used to move beyond the current generation of global anthropogenic land-cover-change scenarios, such as KK10 and HYDE  (Kaplan, Krumhardt et al. 2010). These widely-used scenarios rely on population estimates and simple estimates of per-capita land use to quantify anthropogenic land-cover change-often equated with deforestation-over time. In considering the broad range of land uses present in western Africa in the prehistoric and precolonial past, and demonstrating how these change over time, our categories and maps challenge the simplistic idea that anthropogenic land-cover change can be modeled as a simple function of population, and highlight the need to account for land-use change that is frequently both nonlinear and uncoupled from the properties of the physical environment.
Acknowledgements Open access funding provided by  Max Planck Society. This work was supported by Grants to JOK from the  European Research Council  (COEVOLVE,  313797 ), the  Swiss National Science Foundation  (ACACIA,  CR10I2_146314 ), and the  Fondation Herbette  ( 2016-2-E-16 ). JMR was funded by the  Funds for Research Training in Industry and Agriculture (National Funds for Scientific Research, Belgium)  and the  Leopold II Funds (Belgium). Data were also acquired in the framework of the  Era-Net Bio-divERsA CoForChange  project, funded by the  French National Research Agency and National Environment Research Council (UK). We thank  Mats Widgren  and the other participants of the 2015 PAGES and  SNSF  ( IZ32Z0_164147 ) supported workshop  Mapping Land Use and Subsistence in West Africa , which led to this paper;  Kathy Morrison  and other members of LandUse6k for their input on category nomenclature;  Richard Oslisly  for his input on raised field systems; and  Kevin MacDonald  for advice about the.
The topography and pressure fields over the MASP are shown in Figure  2c. Horizontally inhomogeneous initial and boundary conditions are specified by GFS 1 • resolution dataset. The simulation runs started at 1800 UTC on 21 August 2014 until 0000 UTC on 23 August 2014 (30 h). The 1. 5 order turbulent kinetic energy (TKE) closure scheme for sub grid boundary-layer turbulence. Ice microphysics scheme by  [41]  was used. It includes two liquid phases (cloud and rain) and three ice categories (ice cloud, snow and hail or graupel). The scheme assumes spherical hydrometeor exponential size distributions for rain, snow and hail/graupel. Control runs were performed with surface physics of sandy loam soil type and semi-desert vegetation. Urban and vegetation grid cells from the Global Ecosystems Database that adapts biophysical parameters such as the leaf area index, fractional coverage, displacement height, roughness height, albedo and emissivity.
These urban parameters are shown in Table 4  [9, 18]. All tTEB parameters were kept constant and for average conditions. Non-urban land-use classes in the model domain were always parameterized via the force-restore scheme  [47]. Average parameters in Table  3  drove the model with an acceptable degree of approximation. As an aside, updated and publicly available urban-morphology datasets for urban modelling is needed. The GFS one-degree resolution sea surface temperature (SST) used by ARPS were replaced by Tropical Rainfall Measurement Mission (TRMM) 0. 25 • resolution daily SST  [21]. TRMM daily SSTs are lower with higher gradients near the coast of São Paulo State compared to GFS ones (not shown). SST differences yield realistic gradients generating a convergence zone above the MASP. Table  4.
Datasets ARPS-tTEB simulations precipitation outputs were verified against SPWR precipitation estimation (Figure  6a ) and rain gauge 10 min resolution measurements from the Center for Monitoring and Alerts of Natural Disasters (CEMADEN) located at 54 automatic pluviometric stations located on buildings of the city of São Paulo (Figure  6b ). The SPWR is a 650 kW dual S-band Doppler system that surveys within a 240 km radius. Rainfall rates are estimated at 2 × 2 km 2 every 5 min. For the present contribution, were used the Climate Prediction Center (CPC) morphing technique (CMORPH) precipitation estimates, that use low orbiter satellite microwave observations obtained entirely from geostationary satellite infrared data at 0. 25 • resolution every 3 h, as well as the Global Precipitation Measurement (GPM) mission, that estimates precipitation every half-hour at 0.
Bayesian estimation provides rich and complete information about the joint distribution of credible parameter values, including the means, standard deviations, effect size, and normality between data from two groups. This method minimizes extraneous influences on the data contaminated by random variability. Bayesian estimation can accept the null value by using a decision procedure involving the high density interval (HDI) and provides precise power analysis for multiple goals of research  [52]. For these reasons, ARPS-tTEB precipitation simulations were verified against the SPWR rainfall estimates between 1600 UTC on 12 January to 0000 UTC on 13 January 2015. A Bayesian approach described in Section 4. 4 was used to verify the time series of mean precipitation. Figure  9a, b  show histograms of effect size parameter (Equation (  2 )) between the median precipitation simulated by the tTEB and control runs compared to the ones of SPWR, respectively.
Acknowledgments: We thank to  Ming Xue  for very important suggestions to improve the present work. Part of the present work was done using computational resources,  HPC-Linux-Cluster , from  Laboratorio de Dinámica de Fluidos Geofísicos Computacionales at Instituto Geofísico del Perú  (grants  101-2014-FONDECYT ,  SPIRALES2012   IRD-IGP, Manglares IGP-IDRC ,  PP068 program ).