dataesr/scientific-paragraphs-categorization huggingface dataset:
2 ppm. Seasonal mean biases relative to the v10 OCO-2 XCO 2 product are of order 0. 1 ppm for observations over land. However, for ocean-glint observations, seasonal mean biases relative to OCO-2 range from 0. 2 to 0. 6 ppm, with substantial variation in time and latitude. The ACOS GOSAT v9 XCO 2 data are available on the NASA Goddard Earth Science Data and Information Services Center (GES-DISC) (OCO-2 Science Team et al. , 2019a, b). The v9 ACOS Data User's Guide (DUG) describes best-use practices for the data  (O'Dell et al. , 2020). This dataset should be especially useful for studies of carbon cycle phenomena that span a full decade or more, and may serve as a useful complement to the shorter OCO-2 v10 dataset, which begins in September 2014.
html;  Yoshida et al. , 2013)  a number of independent research institutes have developed their own products e. g. ,  Butz et al. , 2011; Crisp et al. , 2012; Cogan et al. , 2012; Heymann et al. , 2015; Parker et al. , 2020. One of these groups, the Atmospheric CO 2 Observations from Space (ACOS) team, used an L2FP retrieval algorithm developed for the NASA Orbiting Carbon Observatory (OCO) to derive estimates of XCO 2 from the GOSAT data  (O'Dell et al. , 2012; Crisp et al. , 2012). Early XCO 2 estimates from these efforts had large biases and random errors when compared to XCO 2 estimates from the Total Carbon Column Observing Network (TCCON) and other standards. For example, the v2. 8 ACOS GOSAT L2FP product had biases of 7 to 8 ppm relative to TCCON  (Crisp et al. , 2012). These biases were reduced to 1-2 ppm in the v2.
9 product. The next major release was v3. 5 in 2014, which spanned approximately 4 years. This data product showed additional reductions in bias and scatter against TCCON, as well as reasonable agreement in seasonal cycle phase and amplitude  (Lindqvist et al. , 2015; Kulawik et al. , 2016). These early space-based XCO 2 products were rapidly adopted by the carbon cycle science community. Early studies based on GOSAT ACOS retrievals included  Basu et al. (2013) ,  Deng et al. (2014) ,  Chevallier et al. (2014), and Feng et al. (2016). These studies provided the first comprehensive insights into regional flux estimates from space-based observations of carbon dioxide. Houweling et al. (2015)  conducted an extensive inter-comparison of the early GOSAT-based atmospheric inversion system studies and reported a reduction in the global land sink for CO 2 and a shift in the terrestrial net uptake of carbon from the tropics to the extratropics.
Palmer et al. (2019)  used this data product in a global study, concluding that the the tropical land regions were a net annual source of CO 2 emissions, including unexpectedly large net emissions from northern tropical Africa. Wang et al. (2019)  found that the ACOS GOSAT v7. 3 XCO 2 yielded a stronger carbon land sink than the v7 OCO-2 product. Byrne et al. (2020)  used the ACOS GOSAT 7. 3 product to study interannual variability in the carbon cycle across North America, and  Jiang et al. (2021)  investigated interannual variability of the carbon cycle across the globe with v7. 3. Most recently, the v9 ACOS L2FP retrieval algorithm, first applied to OCO-2  (Kiel et al. , 2019) , was used to generate estimates of XCO 2 from an eleven year record of GOSAT measurements, spanning April 2009 through June 2020.
This both extends the time record over v7. 3, and produces an ACOS GOSAT product that is more directly comparable to the newest OCO-2 product, which is now using version 10. The paper is organized as follows; Section 2 discusses the GOSAT TANSO-FTS instrument and measurements as related to the ACOS XCO 2 estimates. In Section 3 updates to the ACOS v9 L2FP algorithm are detailed, and an assessment is given of the v9 XCO 2 data product volume. The XCO 2 quality filtering and bias correction procedures, specific to ACOS GOSAT v9, are also discussed. Section 4 provides an evaluation of the v9 XCO 2 product using estimates of XCO 2 from TCCON and from a suite of 4 atmospheric inversion systems (models). In addition, a comparison to collocated XCO 2 estimates derived from NASA's OCO-2 sensor is presented.
ACOS GOSAT v9 L1b measurements The JAXA L1b algorithm, which has been updated more than 10 times over the eleven year data record, produces an internally consistent set of geometrically, radiometrically, and spectrally calibrated TANSO-FTS radiances. The raw spectral measurements are interferograms, which are calibrated and Fourier transformed to yield spectra. The version 205/210 Level 1b (L1b) geolocated and calibrated radiances provided by JAXA have been used for the ACOS v9 reprocessing. A list of L1b updates for v205/210 can be found in Table  3  of the ACOS v9 Data Users Guide (DUG)  (O'Dell et al. , 2020). Note that while the current L1b version is now 230, the only differences between this version and 205/210 are in the thermal infrared band (5. 6 -14. 3 µm), which is not used in the ACOS XCO 2 retrieval.
After obtaining the calibrated L1b product from JAXA, the ACOS team converts the files to the format needed as input to the ACOS L2 algorithms. The L2FP algorithm uses a simple average of the S and P linear polarizations to produce an approximation of the total measured intensity. Due to cooperation agreements between JAXA and the California Institute of Technology, the distribution of the ACOS GOSAT L1b product is restricted and therefore not publicly available on the NASA DISC. However, the data may be procured by submitting a request to the GOSAT project.
ACOS GOSAT v9 L2FP sounding selection and convergence GOSAT data from April 20, 2009 through June 30, 2020 were passed through the ACOS L2FP algorithm pipeline, which includes a series of stages where soundings can be rejected or selected for further processing. The throughput of each of these stages for ACOS GOSAT v9 is summarized in Table  2  and Figure  1. The pipeline begins with a series of preprocessing steps, which reject corrupted spectra and screen the remainder to eliminate those with optically-thick clouds and/or aerosols  (Taylor et al. , 2016). From the full set of measurements (Panel A of Figure  1 ), the remaining soundings are accepted by the L2FP algorithm (18. 8% of the 37. 4 M measured soundings contained in the ACOS GOSAT v9 record) (Panel B of Figure  1 ) and a retrieval of XCO 2 is attempted.
, 2018). The development of ACOS GOSAT v9 used XCO 2 truth metrics derived from both TCCON measurements, and the median CO 2 distributions determined from a suite of four atmospheric inversion systems. TCCON is a well established validation transfer standard for space-based estimates of XCO 2  (Wunch et al. , 2011a (Wunch et al. , , 2017b)). For the ACOS GOSAT v9 QF/BC, estimates of XCO 2 derived from TCCON measurements using the ggg2014 retrieval algorithm were used  (Wunch et al. , 2015). Individual GOSAT soundings were compared to TCCON daily mean XCO 2 values. TCCON data were included if: (i) they were flagged good (flag = 0), (ii) they fell within 3 standard deviations of a daily quadratic fit against time (to remove outliers, e. g. due to unscreened cloud), (iii) they covered at least 15 minutes within a given day, (iv) there were at least 3 good soundings within the day, and (v) the standard deviation of the good soundings for the day was less than 3 ppm.
ACOS GOSAT v9 XCO 2 versus TCCON A list of TCCON stations used in this work, including basic physical information and data citations, is given in Table  8. For the evaluation against the ACOS v9 XCO 2 data, the single sounding collocations described in Section 3. 3, were aggregated into overpass mean values. Essentially the same TCCON dataset was used for both the QF/BC procedure as for the evaluation, as no hold-over data was maintained. Also, as described in Section 3. 3, an averaging kernel correction was applied to the TCCON data in order to fairly compare to the satellite data. A one-to-one linear regression of the XCO 2 provides a simple quantification of the agreement, as shown in Figure  7. For Land H-gain observations (B), µ=+0. 10 ppm and +0. 14 ppm for the SS and OPM, respectively.
The correlation in the XCO 2 between the datasets in all observation modes is high, with Pearson R 2 =0. 98, 0. 98, and 0. 99 for Ocean-Glint, Land H-gain, and Land M-gain, respectively. Overall, these results indicate excellent agreement between the bias corrected and quality filtered ACOS GOSAT v9 XCO 2 product and collocated estimates from TCCON. and 4 ). The MAEs tend to be lower for v9 compared to v7. 3, with smaller error bars, and increased number of collocations. This is especially true for the SH Ocean-Glint data, where the MAE ranges from 0. 4 to 0. 7 ppm in v9 for all seasons, in contrast to v7. 3, which had higher MAE ranging from 0. 5 to 0. 85 ppm in that region. In the v9 Land H-gain data, the MAE is roughly a function of latitude, with the highest values ( 1. 0 ppm) seen between 60 • N -90 • N, and the lowest values ( 0.
Acknowledgements. The CSU contribution to this work was supported by  JPL  subcontract  1439002. TET acknowledges assistance from  Peter Somkuti  and  Heather Cronk  at  CSU/CIRA  with Python map plotting. CarbonTracker results provided by  NOAA ESRL, Boulder, Colorado, USA  from the website at http://carbontracker. noaa. gov. The GEOS data used in this study were provided by the  Global Modeling and Assimilation Office (GMAO)  at  NASA Goddard Space Flight Center. HL acknowledges support from the  Academy of Finland  (project  331829 ). AM's contributions to this work were supported by JPL subcontract 1577173. P. I. P. and L. F. acknowledge support from the  UK National Centre for Earth Observation  funded the  National Environment Research Council  ( NE/R016518/1 ). The TCCON stations at Rikubetsu,  Tsukuba , and Burgos are supported in part by the  GOSAT  series project.
Local support for Burgos is provided by the  Energy Development Corporation  (EDC, Philippines). The TCCON site at Réunion Island is operated by the  Royal Belgian Institute for Space Aeronomy  with financial support since 2014 by the  EU  project  ICOS-Inwire and the ministerial decree for ICOS  ( FR/35/IC1  to  FR/35/C6 ) and local activities supported by LACy /UMR8105  and by  OSU-R/UMS3365  - Université de La Réunion. The TCCON stations at Garmisch and Zugspitze.
, 2009). Deer browse the regeneration strata and wild pigs scour the soil. Impacts of deer on forests equilibrium are a general concern  (Martin and Baltzinger, 2002; Davis et al. , 2016). As invasive ungulate species impact soil and root properties, consequences are expected on the hydrological regime of degraded surfaces. However, very few studies dealt with effects of ungulate species on the hydrological and erosion regime of forests  (Nakagawa, 2021). The HydroThiem observatory is devoted to hydrological studies of individual surfaces degraded by low intensity wildfires and by invasive ungulate fauna by comparison with a healthy forest surface as well as with a mosaic of such surfaces at the scale of a small catchments. Since the setup of this observatory in 2017, it produced an unprecedented dataset in the Southern Pacific region allowing an understanding of flow generation and erosion at the scale of erosion plots and small catchments.
Rainfall Five automatic tipping bucket raingauges were installed. SAV, REGEN and SCAR are equipped with Campbell CS 701 raingauges, which allow to record rainfall with a 2 % accuracy for intensities in the 0-250 mm/h range and a 3 % accuracy in the 250-500 mm/h range. Rainfall data were recorded at a 6 min time step by CR 200X data loggers. These three raingauges were calibrated in the field in 2019 and 2021 according to the manufacturer's specifications and rainfall data are available continuously between May 2018 and Oct. 2021. Two additional Onset RG3-M raingauges were installed in the upper CAPT watershed and called Tipo and Tiotei. Their accuracy is 1 % up to a rainfall intensity of 130 mm/h and they were calibrated in the laboratory before installation in Feb. 2020. Due to power shortages and failures, only discontinuous records are available for these two raingauges.
The calibrations provided by Delta-T for organics soil and for mineral soil were used above and below 0. 3 m depth. However unrealistic high moistures where produced during rain events with these calibrations, reaching 0. 8, 0. 7 and 0. 85 at the superficial probes of SCAR, SAV and REGEN, respectively. By comparison, total porosity was measured by the weight difference of soil samples saturated during 12 h in water and oven dried during 24 h. The measured porosity at 0. 2 m was 58 % ± 5 % at SCAR, 55 % ± 5 % at SAV and 60 % ± 5 % at REGEN. Therefore moisture data presented here provide only qualitative information on the soil saturation. Some gaps in the dataset result from failure of one probe or of the datalogger and from removal of the electrodes before setting fire at SAV. Comparison of the moisture signal at REGEN, SAV and SCAR is provided in Fig.
However, when two turbidimeters were placed in the reservoir, these peaks were only observed on one of them. Therefore, they were considered as artifacts resulting from macrosediments (vegetal debris, by example) and replaced by the mean of the previous and the following data points. Moreover, turbidity in the reservoir was no more considered as soon as the flow from the plot stopped. Suspended matter concentration (SMC) was measured from samples collected during two different rain events at the SAV and SCAR plots. Given the low runoff and turbidity Fig. 5. Discharge observed at the three plots (Oct. 2018-Apr. 2020). Cumulated runoff is also compared to cumulated rainfall (see right scale). Note that the discharge scale is reduced for the two lowest subfigures. observed at the REGEN plot, it was estimated that this kind of surface produced a negligible amount of suspended matter and the relation between SMC and turbidity was not calibrated there.
Suspended matter at CAPT. Turbidity is measured by a Campbell OBS501 dual sensor probe and recorded at a 6 min time step by a Campbell CR300 datalogger. Both turbidity sensors use light emitted in the near infrared domain (850 nm). The sidescatter sensor measures right angle scattered light and is devoted to low turbidities. The backscatter sensor works with light scattered at angles ranging from 125 • to 170 • from the incident beam and is devoted to measurements of large turbidities. Turbidity data are lacking for a few short time periods resulting from power failures. Laboratory tests with successive dilutions of a concentrated solution showed that the backscatter sensor provided a linear response up to 1050 FBU. This sensor is best suited for erosion studies. At the 6 min time step, turbidity data from both the sidescatter or the backscatter sensors presented a continuous signal with some isolated peaks which were removed and replaced by interpolation from the previous and the next data point.
We noted that although the optical windows was cleaned at each data upload, the minimal observed turbidity was increasing, from 0. 2 FNU at the beginning of the data record toward 10 FNU at the end of the record. This results in a low accuracy of the lowest turbidity values. SMC was computed by the SMC_SS equation if Turb_BS < 60 FBU, by the SMC_BS equation if Turb_BS > 80 FBU and by a weighted mean of SMC_SS and SMC_BS between 60 FBU and 80 FBU, the weight of Turb_BS increasing linearly from zero at 60 FBU to one at 80 FBU. The SM discharge was computed as the product of SMC and discharge interpolated at the SMC measurement points. Maximum and minimum erosion values resulting from errors in discharge and SMC are provided. The SM discharge rate is strongly impacted by uncertainties on the rating curve since highest SMC are observed during highest discharges events.
Conclusion The dataset presented here evidence contrasted hydrosedimentary regimes at a 100 m 2 plot scale between forest and a savanna submitted to a humid tropical climate. Scale effect can be addressed by comparison of results obtained at the plot scale and at the two watersheds. Healthy forest allows infiltration of rainwater, while degraded forest is associated to large runoff. Infiltrated water returns to the atmosphere with ET and promotes a smooth hydrological regime of the Thiem river through subsurface flow. The moderate intensity artificial bushfire set up at SAV in Oct. 2019 did not induce a strong change in runoff or erosion. However bushfires may impact the hydrological regime of a watershed by inducing retreating of forests and replacement of forest by savannas. Therefore conservation strategies of watersheds should include regulation of ungulate species in forest and protection of forest/ savanna borders from bushfires.
The dataset gathered in the HydroThiem observatory may help fine-tuning of hydrological models such as those of  Cerbelaud et al. (2022)  or provide a comparison for researchers working on tropical forests and savannas. Moreover, since individual effects of moderate wildfires and of ungulate invasive species are considered in the HydroThiem project, comparison with other degradations, such as deforestation, can be achieved at the 100 m 2 scale as well as at the scale of a small watershed.
Acknowledgements The setup and functioning of the HydroThiem site results from strong collaboration with local people from the Tiwae and Poyes tribes and particularly with the Cemi Acuut a Mulip and Tipwoto associations. Funding from  IRD, Suez (Agir pour la Ressource en Eau call ),  ANR  ( Spiral  project),  Cressica  ( Assurpluhyt  project) and continuous support by the  Northern Province of New Caledonia  are acknowledged. Luc Decker  helped for data formatting and data deposit.
We present an overview of best practices for publishing data in astronomy and astrophysics journals. These recommendations are intended as a reference for authors to help prepare and publish data in a way that will better represent and support science results, enable better data sharing, improve reproducibility, and enhance the reusability of data. Observance of these guidelines will also help to streamline the extraction, preservation, integration and cross-linking of valuable data from astrophysics literature into major astronomical databases, and consequently facilitate new modes of science discovery that will better exploit the vast quantities of panchromatic and multi-dimensional data associated with the literature. We encourage authors, journal editors, referees, and publishers to implement the best practices reviewed here, as well as related recommendations from international astronomical organizations such as the International Astronomical Union (IAU) and International Virtual Observatory Alliance (IVOA) for publication of nomenclature, data, and metadata.
INTRODUCTION Modern telescopes and instruments are being used to observe larger areas of the sky over wider ranges of the spectrum, and with greater frequency than ever before. The volume and complexity of resulting data are growing at an exponential rate, not only within the astronomical archives that capture the original data products, but also in the scientific literature where more highlyprocessed data are published alongside scientific results. It is therefore extremely important that data appearing in journal articles are prepared and published with great diligence in order to: accurately present new data, avoid any loss of data, preserve and support the integrity of scientific results, and enhance the reusability of data to facilitate further analysis and followup studies. The impacts of publishing complete and accurate data are far-reaching across all astrophysical disciplines and encompass all types of objects, and are important to the scientific process for providing transparency and reproducibility in the primary record of scientific exploration and discovery.
These data also serve another vital function in modern science as they are continuously being integrated into astronomical archives to provide comprehensive information for astronomical objects, and to support planning new observations, performing data analysis, making new discoveries, and preparing publications of new results. Uniformity and adherence to established norms in published data are essential to improving automation, efficiency, and accuracy of procedures required to integrate data from the literature into astronomical archives in a timely fashion. The high degree of connectivity between the digital journal articles and the astronomical archives, with the NASA Astrophysics Data System (ADS 1  ) as a nexus through its extensive bibliographic database, provides an ecosystem used 24/7 by thousands of scientists around the world who depend on astronomical data that are as current and accurate as possible.
Much of data appearing in the astronomical literature are presented with great care and serve two scientific needs well: 1) a high quality scientific record with results that can be reproduced and expanded upon in follow-up studies, and 2) clean data that can be straightforwardly integrated into astronomical databases. However, there are a substantial number of journal articles published each year where this is not the case. Various issues continue to severely detract from the quality and utility of the data and hinder both scientific goals. The most common and severe example is the publication of ambiguous object names, typically truncated coordinate-based names, that make it nearly impossible to reproduce observations or accurately cross-identify these sources with those in other journal articles or catalogs.
To assist in this process, we provide in this article recommendations on best practices for publishing data and metadata in astronomical journals. The topics discussed here are essential to achieving the goals of data sharing, open access, and reproducibility of science results. Active and consistent participation from authors in presenting their (meta)data in the literature is also crucial for the publication record and to facilitate integration into astronomical information systems (Schwarz  2005). A number of issues discussed here have also been addressed by Cambrésy et al. (2011) in the context of the SIMBAD 2  and VizieR 3  services of CDS 4. We encourage all authors, referees, editors and publishers to follow these best practices during the preparation, submission, refereeing and editing stages of the publication process.
New objects For newly discovered objects, please follow the IAU conventions mentioned in §2. 1. 1 for designations. Additional recommendations include: • Always assign a name. Without a proper name, it is difficult for both the readers and the databases to unambiguously reference information in a publication. • Verify the name is unique. The key to object designations is that each one must be unique when compared to objects identified at other observatories and wavelengths, especially within the same article. When creating new acronyms, please consult the IAU Dictionary of Nomenclature of Celestial Objects as noted in §2. 1. 1 to avoid reusing existing ones. • Keep the appropriate number of significant figures in coordinate-based names. When the name of an object is generated from the coordinates of the object, too many significant figures would imply a much higher accuracy than the measured position of the object.