dataesr/scientific-paragraphs-categorization huggingface dataset:
One important achievement from GLODAP is the use of data to estimate the anthropogenic CO 2 inventory or its change over decades  (Sabine et al. , 2004; Gruber et al. , 2019). Both products, SOCAT and GLODAP, are relevant tools to detect oceanic acidification rates  (Lauvset et al. , 2015; Jiang et al. , 2019). Although these projects include many international ocean observations there are ocean CO 2 related observations all around the world (published or not published), such as total alkalinity and dissolved inorganic 1 ) goals  (Bockmon and Dickson, 2015). Hereafter this dataset will be cited as SNAPO-CO2. We describe the data assemblage and associated quality control and discuss some potential uses of this dataset.
Data collections The time series projects and research cruises from which data were collated are listed in Table  1  with references in the Supplementary file (SNAPO-CO2-cruises) and the sampling locations displayed in Figure  1. Sampling was performed either from CTD-Rosette casts (Niskin bott es) or fro the ship's seawater s pp y (intake at about 5m depth depending the ship and swell). Samples collected in 500 mL borosilicate glass bottles were poisoned with 100 to 300 μL of HgCl 2 depending on the cruises, closed with greased stoppers (Apiezon®) and held tight using elastic band following the SOP protocol  (Dickson et al. , 2007). Some samples were also collected in 500 mL bottles closed with screw caps. After completion of each cruise, discrete samples were returned back to the LOCEAN laboratory (Paris, France) and stored in a dark room at 4 °C before analysis generally within 2-3 months after sampling (sometimes within a week).
Some samples were also measured for specific processes studies on benthic corals (e. g. Maier et al. , 2012; McCulloch et al. , 2012)  or for mesocosm and culture experiments but the data are not included in this synthesis as they do not represent natural ocean state (e. g. addition of Sahara dust during the DUNE project,  Ridame et al. , 2014). As opposed to pCO 2 , surface A T or C T observations are generally obtained from discrete sampling (measured onboard or onshore). Few cruises offer sea-surface semi-continuous A T or C T observations (e. g. Metzl et al. , 2006)  but new instrumental developments now enable A T measurements on SOOP lines, Ship of Opportunity Program  (Seelmann et al. , 2020). In addition to discrete samples analyzed for various projects conducted mainly in the North Atlantic, Tropical Atlantic, Tropical Pacific, Mediterranean sea and coastal regions (Table  1 ), we complemented this synthesis with A T and C T surface observations obtained in the Indian and Southern oceans during the OISO cruises in 1998-2018  (Metzl et al.
For the projects in this data synthesis, inter-laboratory comparisons were performed occasionally and summarized below. As part of the time-series CHANNEL  (2012) (2013) (2014) (2015)  in the Western English Channel, Marrec et al. (2014) analyzed surface samples collected bi-monthly in 2011-2013. A T analyses were performed with a TA-ALK-2 system (Appolo SciTech. ) while C T measurements were acquired with an AIRICA system (Marianda Inc. ) Based on CRM analyses (Batch #92) the accuracy was estimated ±3 μmol kg -1 for A T and ±1. 5 μmol kg -1 for C T  (Marrec et al. , 2014). When comparing with the samples measured at LOCEAN/Paris for the year 2012,  Marrec et al. (2014)  concluded that between the two methods the concentrations were within ±2 μmol kg -1 and ±3 μmol kg -1 for A T and C T respective y.
, 2014). This leads to a rapid drop in fCO 2 in 2009-2010, such that the NASPG was a strong CO 2 sink  (Leseurre et al. , 2020). The winter-to-summer seasonal decrease of C T in 2010 in the north NASPG was on average -77 μmol kg -1 (Figure  4 ) much larger than in the climatology (range -50 to -55 μmol kg -1 ,  Takahashi et al. , 2014; Reverdin et al. , 2018). The OVIDE data in late June 2010 and SURATLANT in August 2010 confirmed this signal that was linked to a pronounced primary productivity in that period (Figure  4 ,  Henson et al. , 2013; Racapé et al. , 2014; Mc Kinley et al. , 2018). Notice that for this period no pCO 2 observations were available in July-September 2010 in SOCAT data-product and the A T -C T data presented here could be used to calculate pCO 2 to complement the pCO2 dataset in this region like was done for other periods  (Mc Kinley et al.
g. ±20 µmol kg -1 ). Flags 4 (bad or   4 ). This enabled to merge the different datasets for interpretations of the temporal trends and processes driving the CO 2 cycle  (Coppola et al. , 2019 (Coppola et al. , , 2020;; Ulses et al. , 2022)  or to train and validate a regional neural network to reconstruct the carbonate system (e. g. CANYON-MED,  Fourrier et al. , 2020 Fourrier et al. , , 2022)).
, 2018). The CARIOCA sensor was calibrated with regular A T -C T analyses performed at LOCEAN. Based on these data, the mean difference between CARIOCA-fCO 2 measurements and calculated-fCO 2 data was estimated to be around ±4. 4 µatm for 2013-2015, i. e. the same order than the precision of the CARIOCA sensor (±5 µatm,  Merlivat et al. , 2018). Here we extend the results for the period 2013-2018  (Golbol et al. , 2020; data also in SOCAT version v2021, Bakker et al. , 2016)  and compared the CARIOCA fCO 2 time-series with A T and C T data from different cruises (BOUSSOLE, DYFAMED and MOOSE-GE) selected in the layer 0-20m at that location (Figure  S4 ). For 67 co-located samples at different seasons and years, the mean difference between calculated and measured fCO 2 (fCO 2cal -fCO 2mes ) was -3.
Spatial distribution of A T and C T : a global view from the SNAPO-CO2 dataset The surface distribution in the global ocean based on the SNAPO-CO2 dataset is presented in Figure  7  for A T and C T. In the open ocean, high A T concentrations are identified in the subtropics in all basins  (Jiang et al. , 2014; Takahashi et al. , 2014)  with highest concentrations up to 2484 µmol kg -1 in the central North Atlantic (STRASSE cruise in August 2012, 26°N/36°W). In surface and at depth, the A T /Salinity and A T /C T relationships are clearly identified and structured at regional scale (Figure  8 ). Lefèvre et al. , 2010). Such low C T concentrations were also observed around 5°N/51°W in the Amazon River plume during the recent EUREC4A-OA cruise in 2020 and the TARA-MICROBIOME cruise in 2021 (C T = 1451 µmol kg -1 ) leading to low oceanic fCO 2 (< 350 µatm) and a CO 2 sink in this region  (Olivier et al.
, 2016). However, the signal of the C T increase is better identified north or south of the Equator in the eastern tropical Atlantic sector  (Lefèvre et al. , 2021). In the water column A T -C T data from dedicated cruises were used to evaluate the C ant distribution and pH change since pre-industrial era (e. g. PANDORA cruise,  Ganachaud et al. , 2017; OUTPACE cruise, Wagener et al. , 2018; SOMBA cruise, Keraghel et al. , 2020). Time-series at DYFAMED station also enabled to investigate the temporal variability of C T , A T and C ant in the water column  (Touratier and Goyet, 2009; Coppola et al. , 2020; Fourrier et al. , 2022). As an example of the observed temporal variations at depth we selected the data in the layer 950-1050m in the Ligurian Sea from different cruises (Figure  13 ). At that depth both A T and C T present some large anomalies especially noticed in 2013 (lower A T and C T in February 2013, DEWEX cruise) and in 2018 (lower A T and C T in May 2018, MOOSE-GE cruise) the later probably linked to episodic convective process that occurred in winter 2018  (Fourrier et al.
In 2017 the mean C ant concentration at 200m was 49. 1 (±9. 0) µmol kg -1. Below that depth, C ant decreased and reduced to +29. 8 (±8. 5) µmol kg -1 in the layer 300-400m. To complement the C ant inventories based on GLODAP data-product (e. g. Gruber et al. , 2019)  C ant estimates derived from BG-ARGO floats as evaluated here in the Southern Ocean could be applied in other locations as was previously tested in the North Pacific  (Li et al. , 2019). In surface water as the A T derived from the float data are deduced using MLR or LIAR methods  (Williams et al. , 2017; Carter et al. , 2016) , the A T data in the SNAPO-CO2 synthesis could also be used to identify A T anomalies not always captured from floats. This is particularly relevant in coccolithophores blooms areas when low A T concentrations and high pCO2 are observed (e.
Summary and suggestions The ocean data synthesized in this product are based on measurements of A T and C T performed in 1993-2022 with an accuracy of ±4 µmol kg -1. It offers a large data set of A T and C T for the global ocean and regional biogeochemical studies. It includes more than 44 400 surface and water column observations in all oceanic basins, in the Mediterranean Sea, in the coastal zones, near coral reef, and in rivers. For the open ocean this complements the SOCAT and GLODAP data-products  (Bakker et al. , 2016; Lauvset et al. , 2021)  and for the Mediterranean Sea the ongoing CARIMED dataset. For the coastal sites this also complements the synthesis of coastal time-series only done around North America  (Fassbender et al. , 2018; Jiang et al. , 2020; OCADS, 2023). 2018). Section produced with ODV  (Schlitzer, 2018).
The SNAPO-CO2 dataset enables to investigate seasonal variations to decadal trends of A T and C T in various oceanic provinces. In regions where data are available for more than 2 decades in surface water (North Atlantic, Ligurian Sea, Southern Indian Ocean, and coastal regions), all time-series show an increase in C T. Excepted in the Mediterranean Sea, A T appears relatively constant over time, although the A T content present significant inter-annual variability such as in the NASPG or in the coastal zones including near the Congo and Amazon Rivers plumes. This dataset represents independent data for validation of reconstructed A T or C T fields using various methods (e. g. Rödenbeck et al. , 2013 Rödenbeck et al. , , 2015;; Sauzède et al. , 2017; Turk et al. , 2017; Bittig et al. , 2018; Broullón et al.
g. Goris et al. , 2018 , Lerner et al. , 2021). For example, it has been shown that biases in A T in ESM models led to an overestimation of the oceanic fCO 2 trend and thus uncertainty when predicting the oceanic anthropogenic CO 2 uptake  (Lebehot et al. , 2019). The simulated This dataset would also serve for validating autonomous platforms capable of measuring pH and pCO 2 variables and, along with SOCAT and GLODAP datasets, provides an additional reference dataset for the development and validation of regional biogeochemical models for simulating air-sea CO 2 fluxes. It is also essential for training and validating neural networks capable of predicting variables in the carbonate system, thereby enhancing observations of marine CO 2 at different spatial and temporal scales. The data presented here are available online on the Seanoe servor  (Metzl et al.
, 2023,  https://doi. org/10. 17882/95414) and is divided in two files: one for the Global Ocean, and one for the Mediterranean Sea. The sources of the original datasets (doi) with the associated references are listed in the Supplementary Material (Table  S3 , S4). We invite the users to comment on any anomaly that would have not been detected or to suggest potential misqualification of data in the present product (e. g. data probably good although assigned with Flag 3, probably wrong). The SNAPO-CO2 dataset will be regularly updated on Seanoe data servor with new observations controlled and archived. (http://doi. org/10. 17882/42182, http://argo. jcompos. org). We thank Frédéric Merceur (IFREMER) for preparing the page and data availability on Seanoe. We thank Patrick Raimbault (retired, former at MIO, Marseille) for managing the MOOSE project until 2019.
https://doi. org/10. 5194/essd-2023-308 Preprint. Discussion started: 16 August 2023 c Author(s) 2023. CC BY 4. 0 License. The comparisons described above concern the open ocean region with A T and C T concentrations in a range of concentrations close to the CRM references (used by the different laboratories). Another example of comparison is presented here for samples obtained along a river and thus for waters with low salinity and A T concentrations (river Penzé in North Brittany). In 2019, A T was measured at SBR laboratory (Station Biologique de Roscoff) by a potentiometric method (using a Titrino-847 plus Metrohm) calibrated with CRM (Batch #131)for a final accuracy of ±2. 1 μmol kg -1(Gac et al. , 2020). Although the samples were measured with different technics the A T /Salinity relationships are very coherent for both datasets (Figure5).
Study sites Data in this paper were collected from two MSF-supported referral hospitals in humanitarian settings. The hospital in the Central African Republic is situated in Bangui, the country's capital, in an area affected by decades of armed con icts  [51]. The hospital in Northern Nigeria is located in Jigawa State, a fragile rural State reporting frequent intense oodings, kidnappings and in ux of displaced population because of armed con icts in neighboring States  [52]. Each facility had around 10 000 deliveries per year, a catchment area of more than 500,000 people, and the capability to provide comprehensive emergency obstetric care.
Study design and population Data from four components of the AMoCo study were used: 1) a health facility assessment, 2) a knowledge, attitudes, practices, and behavior (KAPB) survey among health professionals, 3) a prospective medical records' review of women presenting with abortion-related complications in the two hospitals and 4) a quantitative patient survey among a sample of hospitalized women. The AMoCo study protocol, available in the MSF science portal  [53] , is registered with ClinicalTrials. gov. NCT04331847. A detailed description of its study design and settings is provided elsewhere  [10].
Prospective medical records' review All women presenting to study hospitals with signs and/or symptoms of complications related to spontaneous or induced abortions were included. Women with threatened abortions were excluded. Eligible women were identi ed by the study clinicians through hospital registers tracking and participations to daily gynecologic-obstetrics clinical meetings  [10]. After checking their eligibility, women were included in the study after an informed opt-out consent process. Their medical records were reviewed prospectively daily with the help of the clinician in charge of the woman's care. Data were extracted in the standardized Case Report Form adapted from the WHO-MCS-A  [48]. It included sociodemographics and obstetric characteristics, presentation clinical characteristics, severity criteria including WHO near-miss criteria  [44] , the detailed medical management received, and the outcome.
Counts and proportion were also used to summarize the percentage of women with responses ful lling each of the process and outcome quality-of-care indicators. We adopted a case-study like approach. Each site was unique and not representative of the population of hospitals in humanitarian settings. Therefore, even if we contrasted some characteristics and indicators, we did not do statistical testing. We calculated 95% con dence interval (95% CI) using exact methods and performed analysis using Stata 16. 0 software (College Station, Texas, USA).
Acknowledgements The  AMoCo study  is a research project implemented by three international organizations ( Medecins Sans Frontiere/Epicentre ,  Ipas  and  Guttmacher institutes)  and the ministries of health of each country (CAR and Nigeria). The authors would like to thank the women and health professionals who participated in this study. They also would like to acknowledge the contribution of all members of the two local steering committees who participated in the protocol development, provided study oversight, and contributed to the interpretation of the results, and dissemination of the ndings. In particular, authors would like to thank in Nigeria the representatives of the  College of Medicine  at the  Federal university of Dutse ,  Jigawa State Ministry of Education, Science and Technology, Ministry of Health , and  Ministry for Women   Affairs and Social Development, Jigawa  mother-care development association,  Jigawa development association ,  Federation of Muslim  women association of  Nigeria-Jigawa State ,  Jigawa State Imams  and  Marie Stopes Nigeria ; and in CAR, representants of the  Ministry of Health, University of Medicine of Bangui, Youth Association of 3 rd arrondissement of Bangui ,  Association of Muslim  women of CAR, Women Association of  Sara Dah , Bangui coges and the heads of Bangui neighborhoods.
Availability of data and materials The study protocol is available in the MSF science portal: https://scienceportal. msf. org/assets/7660. The dataset collected during the study, including deidenti ed participant data, data dictionary and additional related documents are available from the corresponding author or dpco@epicentre. msf. org on reasonable request, following MSF's data sharing policy which ensures that data will be available upon request to interested researchers while addressing all security, legal, and ethical concerns, especially for sensitive subjects like abortion in vulnerable populations. (https://www. msf. org/sites/msf. org/ les/msf_data_sharing_policycontact_infoannexes_ nal. pdf). ".
This was particularly evident during the COVID-19 pandemic, which exacerbated the need for online support. Strict social distancing and containment measures isolated patients, and in response, many patients and caregivers began to seek emotional support and information through social media  [18]. The provision of an ever-increasing amount of information and communication to these patients and caregivers is a matter of prevention and public health, especially concerning cancer  [19, 20]. The aim of this retrospective study was to assess patient and caregiver perceptions of advanced BC treatments, specifically any line of chemotherapy or immunotherapy, using data from US social media posts.
Study Design This retrospective, real-world study retrieved and analyzed data posted by patients and caregivers on social media concerning the treatment of advanced BC. Data posted between January 1, 2015, and March 4, 2021, were considered for the study. Posts on publicly available domains, written in English, and geolocalized in the United States were included. Posts from all public sites, including social media sites such as Twitter and forums such as patient association forums, were included. In contrast, posts on Facebook and Instagram were not included, since not all posts on these sites are publicly available.
Social Media Content Extraction and Selection Data (social media posts) were retrieved from publicly available social media sites by identifying and extracting posts, eliminating irrelevant data, and then filtering the posts to obtain only messages concerning advanced BC. The Brandwatch extractor (Cision Ltd, Chicago, IL) software was used to identify all public posts available on the web using combinations of words related to BC (the full query is available in Multimedia Appendix 1). These discussions were extracted with the associated metadata (eg, publication date or country) and anonymized. Irrelevant posts such as those from discussion forums not related to BC, those not pertaining to patients or caregivers, and those not featuring advanced BC were then eliminated by applying a three-step process.
Initially, posts from irrelevant sources such as potential advertising sites or forums related to pets and animals were removed. Then, a machine learning algorithm was applied to the data set. The algorithm recognized three different variables (lexical field, syntactic aspects of the post, and semantic style) to identify and classify patients and caregivers according to their respective vocabulary and grammar. Next, a manual review was performed to remove inconsequential posts unrelated to patient and caregiver perceptions. Finally, the messages were filtered using keywords characteristic of advanced BC (eg, stage IV BC or terminal BC). Once these relevant posts had been identified, the users or usernames associated with these posts were considered to be directly concerned with advanced BC.
Study Strengths and Limitations This study design has several strengths. A large sample size collected over a 6-year period was analyzed. The results include data from a variety of social media sources and could provide another dimension to research on treatment perceptions. Accessing publicly available social media data is quick, inexpensive, and has no access restrictions. However, our innovative research approach does entail several limitations. The posts extracted were limited to publicly available sites, which excluded popular social media networks such as Facebook and Instagram, meaning that many data were not included. Furthermore, relevant posts may have been inadvertently discarded during the filtering process. Duplication may have also been possible if users were active on more than one forum.
Acknowledgments Medical writing support was provided by  Nathalie Baudry of the microenterprise NB-Pharmaconsult and Joelle Malaab of Kap Code, Paris. Editing assistance was provided by Speak the Speech Consulting and Clinical Thinking. Medical writing support and editorial assistance were funded by the healthcare business of  Merck KGaA, Darmstadt, Germany  (CrossRef Funder ID:  10. 13039/100009945 ), and  Pfizer. Data from this study were previously presented in poster format at the ISPOR 2022 meeting.
Data Availability The derived data sets generated and analyzed during this study are not publicly available due to Kap Code's private ownership of this database. Indeed, these public social media data have been extracted, cleaned, and consolidated in a database using Kap Code tools and are not found anywhere else in this format. If, however, this database is to be reused in a future study, a partnership with Kap Code could potentially take place. For this, please contact the Kap Code team at the following email address: hello@kapcode. fr.
137 Cs was measured by its emissions at 662 keV. The absolute efficiencies of the detectors were determined using calibrated sources and sediment samples of known activity. Corrections were made for the effect of self-absorption of low energy -rays within the sample  (Appleby et al. , 1992). Unsupported (fallout) 210 Pb was calculated by subtracting 226 Ra concentrations from the total 210 Pb activities (Supplement S3-5). The age-depth model does not include the lowermost 210Pb/137Cs age, as only the upper 61 cm of sediment core SAS22-2 are part of the composite profile. Except for this one age, all ages were used for age-depth modelling using the R-package 'rbacon  ' (v2. 5. 8, Blaauw and Christen, 2011)  with the IntCal20 calibration dataset  (Reimer et al. , 2020)  for calibration of radiocarbon data (Supplement S5-6).
Scanning techniques Spectral analysis on sediment cores was carried out directly on the cling wrap covered freshly opened core surface using a Konica Minolta CM-2600d spectrophotometer (8 mm spot) in a 5 mm resolution. The spectral composition was recorded at https://doi. org/10. 5194/cp-2023-73 Preprint. Discussion started: 16 October 2023 c Author(s) 2023. CC BY 4. 0 License. 10 nm steps from 360 nm to 760 nm wavelength. Sediment core colour was calculated from L*a*b* provided by the SpectraMagic NX software (Konica Minolta) to RGB using the R-package farver (v2. 1. 1. 9,  Pedersen et al. , 2022)  and displayed on an age scale using Grapher (v20, Golden Software). Hyperspectral imaging was carried out at the Université Rouen Normandie. Measurements were performed on U-channels previously extracted from the cores in Greifswald using a VNIR-PDF hyperspectral camera (SPECIM) and subsequently processed as described by  Jacq et al.