dataesr/scientific-paragraphs-categorization huggingface dataset:
In situ measures from automated data loggers of temperature, conductivity, dissolved oxygen and CO 2 acquired every 5 minutes at 0. 1, 0. 5 and 1 m depth (DOMLAC) and 0. 5m (PARLAC) for the entire period of the experiment. 2. In situ profiles (0-1 m) of temperature, conductivity, pH, dissolved oxygen (concentration and saturation) acquired twice a week during the experiment. 3. In situ measures of light spectral UV/VIS/IR irradiance (300-950 nm wavelength range) taken in the air and at 0, 0. 5 and 1 m twice a week on the same day of the profiles at point 2. 4. Laboratory chemical analysis of integrated samples taken twice a week on the same day of the in situ profiles at point 2 and 3 of conductivity, pH, total alkalinity, NO 3 , total and particulate nitrogen (Ntot, Npart), PO 4 , total and particulate phosphorus (Ptot, Ppart), total and particulate organic carbon (TOC, POC), Ca, K, Mg, Na, Cl, SO 4 and SiO 2.
Only for DOMLAC also analyses of NH 4 , NO 2 and dissolved organic carbon (DOC). 5. Laboratory analysis of pigments (Chl a , Chl c , carotenoids, phaeopigments) extracted from samples collected at point 4. 6. Only for DOMLAC, specific absorbance on the range 60 0-20 0nm of DOM (i. e. < 0. 7 μm) measured on samples collected at point 4. This dataset aims to contribute our understanding of how extreme climate events can alter lake subsidies and affect the regulation of ecosystem processes such as production, respiration, nutrient uptake and pigment composition. The data can be used for a wide range of applications as being included in meta-analysis aiming at generalising the effect of climate change on large lakes including simulating future scenarios in a broad range of geographical areas as we used different inputs of DOM leached from litters reproducing catchments characteristics typical of different latitudes, such as mostly dominated by large leaf forests and phragmites at middle latitude, and coniferous forests rich of peat mosses that spread along the water surface typical of Northern regions.
Value of the Data • These datasets improve our understanding of the effect of environmental forcing on lake physico-chemical characteristics (such as temperature, oxygen and nutrient concentration) under simulated intense weather events. Additionally, the DOMLAC study evaluated the impact of increased input of Dissolved Organic Matter leached from different litter types such as peat moss and phragmites, representative of different geographic areas and reproducing organic matter gradients from Northern to middle latitudes. • This open and raw dataset will benefit the scientific community working on environmental ecology as can be used for a wide variety of applications including further metaanalysis aiming at generalising the effect of climate change across different regions and latitudes including catchments and lake shores with different characteristics.
Data Description The datasets described in this section can be download at data repository INRAE. The data are stored as single excel file containing six (DOMLAC) and five (PARLAC) data sheets and two summary table sheets  [2 , 3]. In the DOMLAC dataset  [2]  the first rows of the sheets contain indication on the used device (row flagged by "# Device"), the depth of the measure (row flagged by "# Depth") and the unit of the measure (row flagged by "#"). Unique ID for each column (Sample_Name) includes the mesocosm treatment (named Control, Phragmite and Peat-Moss), replicate (from 1 to 3) and the sampling event (numbered S1 for 04. 10. 2021, S2 for 07. 10. 2021, S3 for 11. 10. 2021, S4 for 14. 10. 2021, S5 for 18. 10. 2021 and S6 for 21. 10. 2021), i. e. first row named S1_Control1 is the Control treatment, replicate 1 on the sampling day 1 04.
10. 2021. Samples labelled with "S1 + " in the column "Sample_Name" and "Sampling" are the TOC measures taken at the surface of all mesocosms few minutes after the DOC solution was added on 6 th of October 2021. Complete names abbreviations and sampling dates are in Table  1. Missing data and values below the limit of quantification are respectively identified by the code NA and LOQ. In the PARLAC dataset  [3]  the first rows of the sheets contain indication on the used device (row flagged by "# Device"), the depth of the measure (row flagged by "# Depth") and the unit of the measure (row flagged by "#"). Unique ID for each column (Sample_Name) includes the mesocosm treatment, named Control, Medium (Medium light reduction, i. e. 70% transmitted light) and High (high light reduction, i. e. 15% transmitted light), replicate (from 1 to 3) and the sampling event (numbered S1 for 05.
09   2. Missing data and values below the limit of quantification are respectively identified by the code NA and LOQ. Both dataset  [2 , 3]  are constituted of the following parts: Continuous monitoring data: The first sheet (named "Continuous") contains data acquired continuously every 5 minutes from automated data loggers placed at the depths of 0. 1 m (Surface -S), 0. 5 m (Middle -M) and 1 m (Bottom -B) in each mesocosm. The list of data loggers with respective measured parameters and units is provided below: -Campbell Scientific: Temperature (Therm107) -°C, Conductivity CS547A-L -μScm -1 , pCO 2 GMP252 -ppm, PAR (Licor LI-193) -μmols -1 m -2 -HOBO: Temperature (HOBO onset) -°C -Minidot: Temperature -°C, dissolved oxygen (concentration and saturation) -mgO 2 L -1 , % In-situ vertical profiles data: The second sheet (named "CTD") contains in situ profiles (0-1 m) acquired in every mesocosm twice a week for the entire duration of the experiment using the multiparameter probe CTD 90M -(Sea & Sun Technology GmbH).
Integrated samples data: The fourth sheet (named "Chemical") contains data from laboratory chemical analysis according  [1]  on integrated samples (0-1 m) collected on the same day of in situ vertical profiles. Measured parameters and measure unit, instruments and protocols include: Conductivity (Cond, μScm -1 ) measured using conductometric method with temperature corrections systematically applied when performing lab measurements of the conductivity (ISO 10523:2008). pH measured with an automatic titrator (Basic Titrino 794, Metrohm ©) with a glass electrode. Total alkalinity (TAC) measured by potentiometric determination (ISO 9963-1).
Ca 2 + (mgCa 2 + L -1 ), K + (mgK + L -1 ), Mg 2 + (mgMg 2 + L -1 ) and Na + (mgNa + L -1 ) measured using flame (acetylene/air) atomic absorption spectrophotometry (AA240FS; Varian©). Lanthanum is added as a matrix modifier to minimize interference. (NF 90-020). Cl -(mgCl -L -1 ), NO 3 -(mgNL -1 ), and SO 4 2-(mg SO 4 2-L -1 ), quantified using ion exchange (861 Advanced Compact ion chromatograph; Metrohm©) with chemical suppression (ISO 10304-1). SiO 2 (mg SiO 2 L -1 ) analysed using a Smartchem 200 Discrete Analyzer (WESTCO Scientific In-struments©) (NF T90-007). Pigments data: The fifth sheet (named "Pigments") contains data from laboratory analysis of pigments extracted from integrated samples and analysed using spectrophotometry after extraction into 90% acetone (ISO 10260: 1992). In the DOMLAC dataset  [2]  only: Specific Absorbance data: The sixth sheet (named "Specific Absorbance") contains data from specific absorbance measured in the range 20 0-60 0 nm from integrated samples filtered on 0.
Instrumentation Continuous monitoring data were acquired using data loggers including: Temperature: Campbell, HOBO, MiniDOT Conductivity: CS547A-L -Campbell Scientific Dissolved oxygen: MiniDOT CO 2 : Vaisaila GMP252 MiniDOT and HOBO are autonomous sensors and were ready for deployment. Temperature, conductivity and CO 2 (CS547A-L -Campbell Scientific) are analog sensors and needed to be connected to data loggers. In the DOMLAC experiment data loggers Campbell CR10x were used in C1, CR10 0 0 in Phragmite1 treatment and CR10 0 0x for Peat-Moss1 treatment. In the PARLAC experiment data loggers Campbell CR10x were used in C1 and M1 treatment and CR10 0 0 for H1 treatment. Temperature sensors were calibrated in an environmental chamber. For Campbell and MiniDOT, calibration data were updated via the software.
Conductivity sensors were calibrated using a potassium chloride standard solution of 294 μS/cm at 25 ºC. The calibration included temperature compensation. Oxygen sensors were controlled at 100% saturation in water taking into account the barometric pressure. CO 2 sensors were calibrated in the air and in a closed chamber with standard gas at 500ppm. The intercalibration was done with certified reference CO 2 sensor (AMT). In-situ vertical profiles data for physical characterisation of each mesocosm included in situ profiles (0-1 m) of temperature, conductivity, pH, dissolved oxygen (concentration and saturation) acquired using a multiparameter probe CTD 90M. Light spectral measurements of UV, VIS, IR irradiance (300-950 nm wavelength range) were taken on the air above the mesocosm, at 0, 0.
5 and 1 m using a RAMSES-ASC-VIS irradiance sensor. Integrated samples (0-1 m) for laboratory chemical analysis were collected on the same day of in situ vertical profile data according protocols and methods detailed in  [1]. Conductivity was measured using conductometric method. Temperature corrections are systematically applied when performing lab measurements of the conductivity (ISO 10523:2008). pH was measured with an automatic titrator (Basic Titrino 794, Metrohm ©) with a glass electrode. Total alkalinity was measured by potentiometric determination (ISO 9963-1). NH 4 + , NO 2 -and total nitrogen were measured using an ElementarVario© TOC/TNb coupled with a chemiluminescence detector (APNA-370; Horiba©) after high temperature digestion and catalytic post combustion (ISO 5664:1984; ISO 6777:1984; ISO 10304-1; EN 12260:2003; ASTM D2579-93e1).
Acknowledgments Sources of funding: OLA (Long-term observation and experimentation for lake ecosystems) and  AnaEE France  (Analysis and Experimentation on Ecosystems -https://www. anaee. com ) provided funding for the purchase of the experimental structures (mesocosm thanks adapted for experimental purposes). OLA and the  INRAE CARRTEL  supported the purchase of the probes and lab instruments. The analytical platform for chemistry analyses and the field survey platform "PRECCI" at the  UMR CARRTEL  supported all the personnel working on the project and the consumables for the analyses. The mesocosm experiments were supported by  INRAE -AQUA Department Program  " Paris-Scientique " and  EC2CO-Hybige CNRS. A CC-BY public copyright license has been applied by the authors to the present document and will be applied to all subsequent versions.
different in situ mesocosm experiment performed in 2023The Authors. Published by Elsevier Inc. Specifications Table Subject Environmental Science -Ecology Specific subject area Physico-chemical and high frequency monitoring datasets produced during two grounded base mesocosm experiments directly supplied with Lake Geneva water Type of data Tables How data were acquired Continuous: Temperature (Therm107), conductivity (CS547A-L), CO 2 (GMP252 -Vasaila), PAR (Li-93): Campbell Scientific Temperature: HOBO Temperature, dissolved oxygen (concentration and saturation): Minidot Probes: Temperature, conductivity, turbidity, dissolved oxygen (concentration and saturation): CTD 90M -(Sea & Sun Technology GmbH) Light irradiance (300-950 nm): RAMSES-ASC-VIS Laboratory analysis according [1] : Conductivity and pH: ISO 10523:2008 Total alkalinity: ISO 9963-1 NH 4 + , NO 2 -, Ntot and Npart: ISO 5664:1984; ISO 6777:1984; ISO 10304-1; EN 12260:2003; ASTM D2579-93e1 PO 4 3-, Ptot and Ppart: EPA 365.
368, 6. 454 Data accessibility The dataset described in this data paper is accessible as open file in the Data INRAE repository https://entrepot. recherche. data. gouv. fr/dataverse/inrae DOMLAC dataset uploaded in the repository as single excel file [2] : Repository name: Data INRAE Direct URL to data: https://doi. org/10. 57745/QJEJIJ PALAC dataset uploaded in the repository as single excel file [3]. Repository name: Data INRAE Direct URL to data: https://doi. org/10. 57745/N2NKA2 Related research article Complementary DIB articles on a This is an open access article under the CC BY license ( http://creativecommons. org/licenses/by/4. 0/ ) [5] same lab and collecting data using the same methods:[4]Tran-Khac V, Quetin P, Domaizon I, Jacquet S, Espinat L, Gallot C, Rasconi S. 2020. In situ pelagic dataset from continuous monitoring: a mesocosm experiment in Lake Geneva (MESOLAC).
Data in Brief. 32:106255. https://doi. org/10. 1016/j. dib. 2020. 106255Thecomplementarydatasetpublishedin[4]producedduring the same experiment is available at: https://data. inra. fr/dataset. xhtml?persistentId=doi:10. 15454/T3VCB0[5]Tran-Khac V, Perney P, Crépin L, Quetin P, Domaizon I, Jacquet S, Espinat L, Gallot C, Rasconi S. 2021. Physico-chemical dataset from an in situ mesocosm experiment simulating extreme climate events in Lake Geneva (MESOLAC). Data in Brief. 36:107150. https://doi. org/10. 1016/j. dib. 2021. 107150Thecomplementary dataset published in[5]produced during the same experiment is available at: https://data. inrae. fr/dataset. xhtml?persistentId=doi:10. 15454/PCOPYW.
Data and methodology In a mesoscale context, the SOFOG3D field experiment is located in Southwestern France, in the Aquitaine region (Fig. 1a ). The field campaign was carried out during the Autumn 2019 and Winter 2020 period leading to 15 intensive observation periods (IOPs). A unique dataset has been collected across a complex region with a very contrasted topography. This region is bordered in the east by the "Massif Central", in the west by the Atlantic Ocean, in the north by Bordeaux and in the south by the "Pyrenees". In the region, several dynamical effects can be observed such as sea breeze, land breeze, and mesoscale foehn circulations influencing the fog life cycle. At the local scale, the supersite under focused here is bordered by two rivers: "La Garonne" to the East and "L'Eyre" to the west (Fig.
1a ). These two rivers and the surrounding surface heterogeneities can modulate the fog formation and dissipation times. During the campaign, several in-situ and remote sensing measurements were jointly deployed in the studied area of SOFOG3D. In this paper, our analysis focuses on the data collected in the surroundings of the supersite at Charbonnière, the most instrumented site (Fig. 1b ) during the field campaign. Below, the descriptions of the in-situ and remote sensing measurements and then the fog conceptual model are presented with emphasis on the main meteorological variables used in the study.
Surface measurement data A network of surface weather stations was installed in the study domain of SOFOG3D at the vicinity of Charbonnière, to document the spatial variability of fog and surface heterogeneities at the local scale (Fig. 1b ). Four weather stations were also deployed around the supersite in a northeast-southwest transect (Fig. 1b ). These stations were installed at Moustey, Cape Sud, Tuzan and Noaillan, almost at the same altitude, and operated continuously with very high temporal resolution (0. 1 s time interval) during the period from 18 October 2019 to 31 December 2020. In addition to temperature, pressure, relative humidity sensors and anemometer, a scatterometer provided the visibility used to estimate fog formation and dissipation times at each station. Temperature data are used to characterize the spatial variability of the radiative cooling.
Summary and Conclusions The SOFOG3D field campaign provided a unique dataset documenting thermodynamic and dynamical atmospheric circulations to further understand the processes driving fog formation and dissipation over Southeastern France. Based on an innovative instrumental synergy combining insitu and remote sensing measurements gathered in an adiabatic fog conceptual model, this study has documented the processes favoring fog evolution. The analysis has focused on four fog case studies: two radiative and two advective-radiative fogs. For each case study, we have defined the different phases characterizing the fog life cycle, namely (i) its formation, (ii) an initial phase where the fog develops under thermally stable conditions, (iii) a transition phase towards an adiabatic fog, (iv) an adiabatic phase during which the fog vertical profile is adiabatic, and (v) a dissipation phase where the fog base lifts.
Acknowledgments. The  SOFOG3D  field campaign was supported by  METEO-FRANCE  and  ANR  through grant  AAPG 2018-CE01-0004. Data are managed by the  French national center for Atmospheric data  and services AERIS. The  CNRM/GMEI/LISA  team supported the deployment, monitoring and data processing and supplying of Wind lidar and microwave radiometer.
Abstract. Accurate accounting of emissions and removals of CO 2 is critical for the planning and verification of emission reduction targets in support of the Paris Agreement. Here, we present a pilot dataset of country-specific net carbon exchange (NCE; fossil plus terrestrial ecosystem fluxes) and terrestrial carbon stock changes aimed at informing countries' carbon budgets. These estimates are based on "top-down" NCE outputs from the v10 Orbiting Carbon Observatory (OCO-2) modeling intercomparison project (MIP), wherein an ensemble of inverse modeling groups conducted standardized experiments assimilating OCO-2 column-averaged dry-air mole fraction (X CO 2 ) retrievals (ACOS v10), in situ CO 2 measurements or combinations of these data. The v10 OCO-2 MIP NCE estimates are combined with "bottom-up" estimates of fossil fuel emissions and lateral carbon fluxes to estimate changes in terrestrial carbon stocks, which are impacted by anthropogenic and natural drivers.
These flux and stock change estimates are reported annually (2015-2020) as both a global 1 • × 1 • gridded dataset and a country-level dataset and are available for download from the Committee on Earth Observation Satellites' (CEOS) website: https://doi. org/10. 48588/npf6-sw92 (Byrne et al. , 2022). Across the v10 OCO-2 MIP experiments, we obtain increases in the ensemble median terrestrial carbon stocks of 3. 29-4. 58 Pg CO 2 yr -1 (0. 90-1. 25 Pg C yr -1 ). This is a result of broad increases in terrestrial carbon stocks across the northern extratropics, while the tropics generally have stock losses but with considerable regional variability and differences between v10 OCO-2 MIP experiments. We discuss the state of the science for tracking emissions and removals using top-down methods, including current limitations and future developments towards top-down monitoring and verification systems.
This allows us to quantify the sensitivity of top-down carbon budget estimates to the inversion modeling system and the atmospheric CO 2 dataset used to constrain flux estimates. This paper is outlined as follows. The remainder of Sect. 1 describes the objectives of this work (Sect. 1. 1) and provides background information on both the global carbon cycle (Sect. 1. 2) and top-down atmospheric CO 2 inversions (Sect. 1. 3). Section 2 defines the carbon cycle fluxes of interest. Section 3 describes the flux datasets and their uncertainties, including fossil fuel emissions, the v10 OCO-2 MIP, riverine fluxes, wood fluxes, crop fluxes and the net terrestrial carbon stock loss. Section 4 provides an evaluation of the v10 OCO-2 MIP flux estimates. Section 5 presents two metrics for interpreting the top-down constraints on the CO 2 budget.
Objectives This is a pilot project designed to start a dialogue between the top-down research community, inventory compilers and the GHG assessment community to identify ways that topdown CO 2 flux estimates can help inform country-level carbon budgets (see  Worden et al. , 2022 , for a similar pilot methane dataset). To meet this objective, the primary goal of this work is to provide two products: (1) annual net surfaceatmosphere CO 2 fluxes and (2) annual changes in terrestrial carbon stocks. These products are provided annually over the 6-year period 2015-2020 both on a 1 • × 1 • global grid and as country-level totals with error characterization. These products are intended to be used to help inform inventory development and identify areas for future research in both top-down and bottom-up approaches, including informing strategies for operational top-down carbon cycle products that can be used for tracking combined changes in managed and unmanaged carbon stocks and that can help quantify the impact of emission reduction activities.
The atmospheric CO 2 inversion problem is generally illposed such that the solution is underdetermined by the observational constraints. In this case, additional information is required to produce a unique solution and prevent overfitting of the data  (Lawson and Hanson, 1995; Tarantola, 2005). Typically, this is performed using Bayesian inference, where prior mean fluxes and their uncertainties provide additional information required to estimate fluxes  (Rayner et al. , 2019). Prior mean fluxes of net ecosystem exchange are usually obtained from terrestrial biosphere models (such as CASA, ORCHIDEE and CARDAMOM), while prior mean air-sea fluxes are derived from surface water partial pressure of CO 2 (pCO 2 ) datasets or from ocean models (e. g. ,  Peiro et al. , 2022). The resulting posterior flux estimates combine the constraints on surface fluxes from atmospheric CO 2 data with the prior knowledge of the fluxes.
Fossil fuel and cement emissions Gridded 1 • × 1 • fossil CO 2 emissions, including those from cement production, are calculated as follows. Monthly gridded emissions up to 2019 are taken from the 2020 version of the Open-source Data Inventory for Anthropogenic CO 2  (ODIAC2020, 2000 (ODIAC2020, -2019) )  emission data product  (Oda and Maksyutov, 2011; Oda et al. , 2018). The 2020 emissions were not part of ODIAC but were projected using the Carbon Monitor (CM) emission data product (https://carbonmonitor. org/, last access: 19 May 2021). For each month in 2020 and later, the ratio between that month's emissions and the emissions from the same month in 2019 was calculated from the CM emission data. Since CM provides daily emissions per sector for a handful of major emitting countries and the globe, CM emissions are summed over sectors and days in each month to create monthly total emissions per named country and the rest of the world (RoW).
For each experiment, each inversion group imposes a common fossil fuel emission dataset identical to the one described in Sect. 3. 1. All other prior flux estimates were chosen independently by each modeling group and are listed in Table  1. The inversions assimilate the standardized v10 OCO-2 and in situ data from 6 September 2014 through 31 March 2021 (see Sect 3. 2. 1), with the length of spinup period and in situ data assimilated during that period being left up to the discretion of each group in the MIP. Each modeling group submitted net air-sea fluxes and NBE across 2015-2020, interpolated from the native resolution to a 1 • × 1 • spatial grid at monthly resolution, which are publicly available for download from https://gml. noaa. gov/ccgg/ OCO2_v10mip/ (last access: 6 February 2023).
To characterize the representativeness error, we used an empirical scheme based on simulations from the v7 OCO-2 MIP  (Crowell et al. , 2019). In situ CO 2 measurements are simulated in a forward simulation, and then the model-data mismatch statistics are calculated to characterize the representativeness errors at each measurement location and for each season. Although this was the standard method for characterizing uncertainties for modeled in situ measurements, each v10 OCO-2 MIP group was free to choose how to set the uncertainties in their specific setups. Of the in situ measurements designated as being appropriate for assimilation, about 5 % were withheld for crossvalidation purposes. These data were chosen to be as independent as possible from the measurements that were assimilated. For quasi-continuous measurements, such as those taken every 15 min at NOAA tall towers, measurements were withheld for entire days: we chose 5 % of the days in the dataset, and we withheld every assimilable measurement on that day.