dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collection and coding We collected data from the selected publications and coded information regarding women's participation in SSF management and related impacts, defined here as outcomes affecting the socio-cultural, environmental, or economic dimension of individuals, communities, or SES (Online Resource 3). Two coders read the publications and coded the information following a two-step process. First, each coder was allocated half of the publications to read and code. In a second stage, the lead author checked the quality of data entry and verified the uniqueness of each publication to avoid double counting those based on the same case study. Some publications reported information from more than one case study. In these cases, we collected information separately for each case study, resulting in a total sample size of 124 case studies (Fig.
Data analysis To assess the extent of women's participation in SSF management (RQ1) and explore related impacts (RQ2), we used descriptive statistics. Specifically, we counted the frequency of each reported women's participation level in SSF management as excluded, limited participation, and active. Likewise, we ordered the different categories of impacts (i. e. , socio-cultural, environmental, and economic) by frequency count. Where relevant, we illustrated quantitative results with quotes from the documents examined. To examine the relation between women's participation level in SSF management and impact direction (RQ3), we first distinguished between impacts related to either the exclusion or participation (i. e. , limited; active) of women in SSF management, as well as their direction (i.
e. , positive or negative). Then, taking the case study as unit of analysis, we used the Pearson's Chi-squared test  (Pearson 1900)  with Yates' continuity correction to statistically assess the relationship between women's participation level and impact direction. To provide a visual representation of this relationship, we used a non -metric multidimensional scaling (NMDS) as a rank-based approach that spatially displays the distance between objects in a low-dimensional space. This method can be used both with qualitative and quantitative variables  (Kruskal 1964). All statistical analyses were performed with R Statistical Software (v4. 2. 1; R Core Team 2021) using the vegan package  (Oksanen et al. 2022 ). We considered that a difference was statistically significant when p-values were below 0.
Results Women's participation in SSF management Among the 124 case studies documented, 75 (60%) provided information on women's participation in SSF management. The other case studies did not give details specific enough to categorize women's participation. In these cases, the authors often provided insufficient information on gendered differences in relation to SSF management or did not mention women's role in these processes. Furthermore, we found geographical variations in gender-data provided in the reviewed literature. While most case studies were in Africa (n = 36), Oceania (n = 23) and Asia (n = 22), we found that only about 50% of the cases in Africa and Asia reported on women's participation level in SSF management. Conversely, although we recorded fewer studies in Europe (n = 6), all of them provided this information.
A final finding of this work is that most of the reported impacts unfolded at the SES scale, suggesting a win-win situation between gender-inclusive SSF management and outcomes at the SES scale. Before commenting these results, we highlight some caveats of our work. We are aware that our review has several limitations streaming both from data availability and from the data collection methods. First, results from our work are limited by the overall lack of data on women's contribution to the SSF sector, as also underscored in the literature  (Alonso-Población & Siar 2018; Kleiber, et al. 2015; Williams 2010 ). We believe that the scarcity of gender information is not neutral but value-laden, since our dataset reflects authors' interests and priorities who may chose or not to report on women's participation in SSF management.
Contrasting with the variety of socio-economic impacts of including women in SSF management, we recorded fewer environmental impacts. While one explanation for this finding could be that women's participation in SSF management does not have noticeable environmental impacts, we suggest instead that women's participation in SSF management is mostly studied through a social lens, thus challenging a thorough gender analysis of the whole SES. Such findings align with  Kleiber et al. (2015) , who showed that gender and fisheries research is mostly characterized by social and qualitative approaches and identified a data gap with regards to the environmental dimension of women's fisheries-related activities. Such a gap might also reflect the common idea that women's practices represent a low pressure on coastal ecosystems.
Acknowledgements The authors acknowledge the financial support from the  ICTA-UAB "María de   Maeztu" Programme for Units of Excellence  funded by the  Spanish Ministry of Science, Innovation and Universities  ( CEX2019-000940-M ;  MDM-2015-055 ) and from the  Laboratories for the Analysis of Social-Ecological Systems in a Globalized world (LASEG)  and for the  Marine and Environmental Biogeosciences Research (MERS) ,  Universitat Autònoma de Barcelona  and  Generalitat de Catalunya  ( 2017-SGR-775 ;  2017 SGR-1588 )), and the  European Research Council  ( BIGSEA  project, Grant Agreement No  682602 ). This publication also contributes to the  Local Indicators of Climate Change Impacts (LICCI) Project , which is funded by the  European Research Council (ERC)  under grant agreement No  771056-LICCI-ERC-2017-COG.
Data availability The data that support the findings of this study will shortly be openly available in Figshare repository. The data are now available oneline: Chambon, Mouna; Minarro, Sara;  Alvarez-Fernandez, Santiago; Porcher, Vincent; Reyes-García, Victoria; Tonalli Drouet, huran; et al. (2023). Data for the Synthesis of Women's Participation in Small-Scale Fisheries Management: Why Women's Voices Matter. figshare. Dataset. https:// doi. org/ 10. 6084/ m9. figsh are. 24213 234v1.
The study was conducted in accordance with the guidelines of the Declaration of Helsinki. Patients with DLBCL (N=178; median follow-up time: 44 months) randomized into the GAINED trial (NCT01659099) were used as an independent validation cohort for the OS analysis  6. The GAINED study is a multicenter randomized phase 3 trial comparing Obinutuzumab to Rituximab in association with a chemotherapy administered every 14 days (doxorubicin, cyclophosphamide, prednisone plus vindesine, bleomycin [ACVBP] or vincristine [CHOP]) using an interim PET-scan driven strategy (supplemental Figure  1B ). Transplant-eligible patients (18-60 years,    n=670), enrolled between 2012 and 2017, were diagnosed with a de novo DLBCL with an age-adjusted International Prognostic Index (aaIPI) score ≥1. The median follow-up time of the GAINED cohort was 35.
Data and tissue samples collected in the G075 cohort 31/03/2023 Plasma samples collected at the time of diagnosis (i. e. before any treatment) were available for 123 of the 340 patients enrolled in the trial. Furthermore, post-treatment plasma samples were available for 50 of the 123 patients who had maintained a CR six months after the EOT (detailed information for CR patients in Supplemental table  1 ). For 178 of 670 patients of the GAINED cohort, plasma samples were collected at randomization and control plasma was obtained from healthy volunteer (HV) blood donors at the French National Blood Agency (age, 18-60). Plasma was collected in BD™ P100® tubes (BD Diagnostics, Le Pont-De-Claix, France), shipped at room temperature within 24 hours of collection to the core lab in Rennes (France), centrifuged, and stored at -80°C.
For all G075 patients, baseline clinical data and treatment responses were obtained during inclusion, interim and final assessments. For the 50 patients with a CR 6 months after the EOT, clinical data were obtained 7 to 10 years after inclusion. Interim and final assessment responses were based on the PET-CT and standard CT findings. The interim PET results were based on visual criteria and not the Deauville criteria, which were established after the G075-cohort study had started. As per protocol, patients with a positive interim imaging result received salvage therapy. The collection of data and tissue samples for all patients in the present study had been approved by the French national investigational review board.
cfDNA extraction, quantification, and size distribution analysis Samples from both trials were processed in exactly the same way and a maximum of 2 ml of plasma was reserved for this cfDNA study (supplemental Figure  2A ). In our study, the concentration of cfDNA corresponds to the amount of DNA detected in the 90-to 320-bp window (supplementary Figure  2B ), and the median DNA fragment 31/03/2023 size was significantly smaller for patients than for HVs (supplementary Figure  2C ). We established the best threshold above which patient samples at diagnosis were considered cfDNA-enriched using two different approaches. First, we applied the maximum selected rank statistics (maxstat R package  12  ) to determine the threshold that predicts the most significant differences in OS of patients independently in the GOELAMS 075 and GAINED cohort (supplementary Figure  2D ).
R-HDT is not superior to standard R-CHOP with regard to OS Between May 2005 and August 2010, 340 patients were enrolled in the G075 cohort. Seventeen patients were deemed to be ineligible, and the remaining 323 were randomized to receive either R-CHOP (n=161) or R-HDT (n=162). The two groups were similar, except for performance status (supplemental Table  2 ). There were no differences in demographic and clinical characteristics between the parent cohort and the 123 patients with cfDNA assay data (supplemental Table  2 ). Of the 123 patients, 68 were assigned to the R-CHOP and 55 were assigned to the R-HDT arm (Figure  1A ); the median follow-up time after randomization was 69 months (95%CI, 55. 3-88. 0). With the exception of the sex ratio, the R-CHOP and R-HDT arms were well balanced with regard to their demographic and clinical characteristics (Table  1 ).
ACKNOWLEDGMENTS ACCEPTED MANUSCRIPT / CLEAN COPY 31/03/2023  This work was funded by an internal grant from the  Laboratoire d'Hématologie at the CHU de Rennes. We are indebted to the  Centre de Ressources Biologiques (CRB)-Santé  ( BB-0033-00056 , http://www. crbsante-rennes. com) at the  CHU de Rennes  for help with processing biological samples, the  LYSA-P pathology facility  for extracting DNA from tumor tissues, and the  Lymphoma Study Association (LYSA)  for its support of the study. We also thank the patients and their clinicians for providing samples for research purposes. David Fraser  at  Biotech Communication  for editing services. Lastly, we thank  C. Gisselbrecht  and  M. -H. Delfau  for reading the manuscript in detail.
In 2005, we initiated a French, open-label, randomized, Phase 3 trial (GOELAMS 075, NCT00561379) of R-CHOP vs. a combination of rituximab, high-dose conventional chemotherapy and ASCT (hereafter referred to as R-HDT) for the first-line treatment of advanced DLBCL adult patients aged ≤60. The median follow-up period after randomization was >5. 8 years (70. 4 months; 95% confidence interval (CI), 55. 0 -88. 9). The bloodstream of cancer patients usually contains circulating, cell-free DNA (cfDNA) released from tumor cells after apoptotic, caspase-dependent cleavage. The ACCEPTED MANUSCRIPT / CLEAN COPY 31/03/2023.
Surface N2O from NOAA long-term monitoring sites Surface atmospheric N2O data from the late 1990s onward were obtained from the NOAA Global Monitoring Laboratory (GML) for comparison to GEOSCCM output. NOAA has two programs that measure N2O, the Halocarbons and other Atmospheric Trace Species (NOAA/HATS)  (Thompson et al. , 2004)  and the Carbon Cycle Greenhouse Gases group (NOAA/CCGG). NOAA/HATS provides in situ data measured every ~ 60 minutes using the Chromatograph for Atmospheric Trace Species (CATS) instruments at 5 baseline sites. NOAA/CCGG maintains a flask-air sampling network at ~55 widely distributed surface sampling sites, in which duplicate samples are collected about weekly and shipped to Boulder, Colorado for analysis by gas chromatography (GC) with electron capture detection and by a Tunable Infrared Laser Direct Absorption Spectroscopy (TILDAS) after August, 2019.
The instruments are calibrated with a suite of standards on the WMO X2006A scale maintained by NOAA GML  (Hall et al. , 2007). Uncertainties of the measurements (68% confidence interval) range from 0. 26 to 0. 43 ppb with GC-ECD and 0. 16 ppb with TILDAS. The mean uncertainties in the CATS GC data are 0. 2 to 1. 2 ppb (68% confidence interval) over most of the 2000s, with an increase in recent years as the instruments near their lifetime. This study used the NOAA combined HATS/CCGG N2O product from 1998-2020 (https://doi. org/10. 15138/GMZ7-2Q16), which is based on monthly medians from the CATS in situ program (at 5 sites) and monthly means from the CCGG flask program (at 13 background sites). The combined monthly data are first aggregated at the measurement program level for each sampling location.
NOAA Empirical Background for atmospheric N2O The NOAA empirical background (EBG) is a 4-dimensional (4-D) field, constructed from NOAA surface and aircraft N2O data, which is used in North American regional inversions to represent the background concentration of atmospheric N2O prior to the influence of continental surface fluxes  (Nevison et al. , 2018). The EBG is defined daily over North America from 500-7500 m every 1000 m, from 170°-50°W every 10° longitude and from 20-70°N every 5° latitude (or, prior to 2017, from 20-80°N every 10° latitude). To construct the 4-D field, NOAA data are categorized as marine boundary layer, free troposphere or continental boundary layer, depending on the location of each sample. These three categories are treated individually as follows: For the marine boundary layer, time-and latitude-dependent reference surfaces are computed separately for the Pacific and Atlantic  (Masarie and Tans, 1995, updated as described in Lan et al.
QCLS atmospheric N2O data from vertical profiling campaigns Atmospheric N2O measurements have been made in situ with the Harvard/Aerodyne Quantum Cascade Laser Spectrometer (QCLS) on a variety of aircraft campaigns designed to study the atmospheric profiles of greenhouse and related gases  (Wofsy et al. , 2011; Stephens et al. , 2018). QCLS N2O data are retrieved at 1-Hz with 1s precision of 0. 09 ppb and reproducibility with respect to the WMO N2O scale of 0. 2 ppb  (Kort et al. , 2011; Santoni et al. , 2014)  on the NOAA-2006 scale  (Hall et al. , 2007). The first of the vertical profiling campaigns used here, the HIAPER Pole to Pole Observations (HIPPO) project, consisted of 5 roughly month-long sets of flights centered over the central Pacific Ocean extending from the surface to the upper troposphere/lower stratosphere and nearly pole to pole.
Comparison to data at long-term monitoring sites suggests that GEOSCCM captures the mean seasonal cycle in N2O relatively well in the Southern Hemisphere but overestimates the amplitude of the cycle at high northern latitudes, with a ~1-2 month delay in phasing relative to observations (Figure  3 ). The NOAA N2O empirical background has similar features to those simulated by GEOSCCM. When viewed as a 12-month sequence of NH altitude vs. latitude contours, extending up to 8 km, the NOAA data indicate that the North American background signal of stratospheric depletion originates at polar latitudes in the upper troposphere in spring and is felt in the midlatitude lower troposphere by July, with a peak influence around August (Figure  4 ). The effect on the troposphere is strongest near the pole and weakens substantially moving equatorward.
A comparison of altitude vs. month contours for GEOSCCM and NOAA suggests a faster, more direct propagation of the stratospheric signal down to the surface in the NOAA data compared to the model (Figure  5 ). As a result, the phasing of the GEOSCCM surface minimum is delayed ~1-2 months relative to the NOAA empirical background, consistent with the comparison to NOAA surface monitoring data in Figure  3. HIPPO aircraft data extend up to 14 km and thus provide a broader perspective with respect to altitude of the stratospheric influence on tropospheric N2O, but with sparser temporal coverage than the NOAA empirical background. The southbound transects from the five HIPPO deployments, when detrended and arranged chronologically over an annual mean cycle, form a sequence that is most readily seen in the NH (Figure  6 ).
N2O-depleted air accumulates in the polar lower stratosphere in January and crosses the tropopause by March/April. By June it has descended into the troposphere and moved equatorward, reaching its maximum influence at the surface in August. By October/November, the stratospheric signal is no longer visible at the surface following tropospheric mixing and dilution. Notably, this seasonal progression is less apparent although still discernible in a fuller dataset that also includes the ATom and northbound HIPPO transects (Supplementary Figure  1 ).
e. , delayed by about 1 month relative to NOAA surface observations (Figure  7 ). In support of the observed surface correlations, altitude-latitude contour plots of QCLS aircraft data suggest more depleted N2O values in the extratropical SH in February In the NH in contrast, PLST from the previous winter is not correlated significantly to N2O monthly anomalies at extratropical NOAA surface sites, either in August, the month of the seasonal minimum, nor in June and July, when N2O is descending into its seasonal minimum. GEOSCCM also does not predict significant correlations between PLST and summer N2O anomalies at most northern NOAA sites, with the exception of Mace Head, Ireland (MHD), where a negative correlation is found in July at MHD for both N2O (R=-0. 72) and N2OST (R=-0. 78) (Supplementary Figure  S2 ).
This result is somewhat puzzling, especially given the significant correlation between PLST and NOAA surface station N2O monthly anomalies in January and February (Figure  7 ), which are corroborated by the ATom-2 and ORCAS data (Figure  8 ). It appears that the impact of the stratosphere in austral summer as tropospheric N2O descends into its seasonal minimum is not sufficient to influence the SH N2O AGR over a full 12-month period. The SH N2O AGR results may reflect the strong preservation of the surf zone QBO signal that mixes into the polar region and is ultimately transported into the troposphere as per  Strahan et al. (2015)  combined with the relatively weaker BDC in the SH and/or the interference of ENSO-driven signals discussed below. The fact that PLST does correlate with the SH N2O AGR in GEOSCCM output suggests that GEOSCCM may overestimate the influence of the BDC in the SH or that the correlation may be cleaner due to the lack of a competing ENSO influence in GEOSCCM.
Acknowledgments CDN and QL acknowledge support from the  NASA   MAPS program  (award  16-MAP16-0049 ). The authors are grateful to  Arlyn Andrews ,  Colm Sweeney ,  Bradley Hall ,  Ed Dlugokencky ,  Steve Wofsy ,  Bruce Daube , and many others who have made this study possible, through collection and analysis of surface station and  NOAA  aircraft flasks, in situ NOAA station measurements, and QCLS aircraft campaign observations. The HIPPO and ORCAS observations, and the contributions of BBS were supported by the  National Center for Atmospheric Research , which is a major facility sponsored by the  National Science Foundation  under Cooperative Agreement No. 1852977.
Data Availability NOAA N2O data can be obtained by contacting xin. lan@noaa. gov or through the NOAA Global Monitoring Laboratory at https://gml. noaa. gov/aftp/data/trace_gases/n2o/flask/. QCLS N2O data are openly available and archived in the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) https://doi. org/10. 3334/ORNLDAAC/1925 (ATom), https://doi. org/10. 5194/egusphere-2023-2877 Preprint. Discussion started: 8 December 2023 c Author(s) 2023. CC BY 4. 0 License.
II. Structure and bonding analyses A CCDC database search reveals 77 X-ray structures of (P,C)-naphthyl cyclometalated complexes. The key geometric features were collected (See supporting information, Table  S1 ) so as to analyse the geometric constraints and distortions. First, looking at the bond distances, the P-M and M-Cperi distances were compared with the sum of the corresponding covalent radii,  44  referring to the r parameters (Charts 3a and 3b). None of the structurally characterized complex shows a r(P-M) above 1. 00. About 20% of the structures, mainly Au complexes, possess a r(P-M) factor of 0. 95-1. 00. All the other complexes fall in the 0. 90 ± 0. 05 range. The (P,C)-naphthyl backbone is thus very suited and very well accommodate all metals. It does not induce significant constraint on the P-M and C-M bond lengths.
Acknowledgements Financial support from the  University of Geneva  and the  Swiss National Science Foundation  (grant number  CRSII5_ 173720 ) is kindly acknowledged. This work benefited from the use of the SasView application, originally developed under  NSF  award  DMR-0520547. SasView contains code developed with funding from the  European Union 's  Horizon 2020 research and innovation program  under the  SINE2020  project, grant agreement No  654000. The help of K. K. Ramankutty with fluorescence measurements is kindly acknowledged. LMLD acknowledges grants from the  Swiss National Supercomputing Centre CSCS  under project IDs  s894  and  s1072. Open Access funding provided by  Université de Genève.
Soil analyses and data treatment All topsoil samples were finely ground in a disc mill. The soil organic matter bulk chemistry and the SOC thermal stability was characterized by Rock-Eval® thermal analysis (Rock-Eval® 6 Turbo, Vinci Technologies). A detailed description of this method can be found in  Behar et al. , (2001)  and  Disnar et al. , (2003). It consists in carbon measurement as gaseous effluent during two phases. The first one is a pyrolysis stage (3 min isotherm at 200 • C followed by a 200 to 650 • C temperature ramp with a heating rate of 30 • C min -1 ) in N 2 atmosphere during which CO and CO 2 were quantified with an infrared detector. Volatile hydrocarbon effluents (CH) were quantified with flame ionization detector. The second phase is an oxidation stage (1 min isotherm at 300 • C followed by a 300 to 850 • C temperature ramp with a heating rate of 20 • C min -1 and a 5 min isotherm at 850 • C) in laboratory air atmosphere during which CO and CO 2 were quantified with an infrared detector.
Acknowledgements We thank  Florence Savignac  for her help with the Rock-Eval® thermal analysis of soil samples. This study was partially funded by the  Ville de Paris  under the  Emergence(s) Programme  ( SOCUTE  project), by the  LabEx OSUG@2020  (Investissements d'avenir - ANR10 LABX56 ), by the  DIPEE/FREE-Alpes federation , the  French Agence Nationale de la Recherche (ANR)  through the  TransAlp  project ( ANR-20-CE02-0021 ) and the  Lautaret Garden-UMS 3370  ( Univ. Grenoble Alpes, CNRS, SAJF, 38000 Grenoble, France ), a member of the  Zone Atelier Alpes and eLTER net-work and of AnaEE-France  ( ANR-11-INBS-0001AnaEE -Services, Investissements d'Avenir frame). This research was conducted within the  Long-Term Socio-Ecological Research (LTSER) platform Lautaret-Oisans , a site of the European Research Infrastructure eLTER.