dataesr/scientific-paragraphs-categorization huggingface dataset:
The funded projects Six research teams were funded by the CIHR IGH to (1) generate evidence about whether applying SGBA to KT interventions involving human participants improves effectiveness; (2) contribute to a broader knowledge base on how to integrate sex and gender into KT interventions; and (3) facilitate the consideration and development of gender-transformative approaches (i. e. approaches that promote gender equity) in KT interventions  [6, 17, 18]. The call echoed several international recommendations, including the Madrid Statement, urging WHO and the member states to include sexdisaggregated information in their data collection systems and develop gender-sensitive indicators to achieve the highest standard of health and support effective health policy  [19, 20]. These six ongoing projects (Box 1) are taking place in the context of teaching hospitals, community-based health and social centres, workplaces, professional organisations, and KT developers.
Team 6, a pan-Canadian team, aims to develop and evaluate intersectional approaches to KT frameworks and associated tools with KT intervention developers working with older adults ('the intersectional team'). sex and gender on research design and outcomes, and challenges with data collection and datasets. These findings have been confirmed in a review of literature on sex-and gender-based analysis  [25]. While progress has been made in this respect in some research fields, such social science health research  [22, 26] , implementation literature reveals that attention to sex and gender has not yet infiltrated research methods in the field of KT  [4]. The strategies proposed by the teams may begin to fill this gap. Another challenge was the difficulty in operationalisation of intersectionality.
It is unsurprising then that the scientific community may not always welcome this complexity because it calls into question the paradigm of the scientific method itself  [30]. Further research is needed on the obstacles that prevent investigators from taking account of gender and sex, on papers that have influenced investigators in a positive way and how this happened, and on best forums for promoting discussion of these issues. The paper is subject to several limitations. The meeting was part of the CIHR Impact of Gender on KT Interventions grant and, as such, it was not initially planned for data collection and analysis; in addition, the discourse of team members present at the discussion may not have been representative of the key researchers in each project. Second, the meeting was held in a high-income country with a resource-intensive health system; thus, the findings may not represent all the challenges in this respect, although a few participants were from LMICs and one of the teams had a project in a LMIC (Table  1 ).
Acknowledgements We thank  Louisa Blair , editor at  Vitam -Centre de recherche en santé durable, Quebec, Canada , for her support. We also thank the  Gender Impact on Knowledge Translation (GIKT) Group :  Lionel Adisso ,  Department of Social and Medicine, Université Laval ;  Sylvain Boet ,  Department of Anesthesiology and Pain Medicine & Department of Innovation in Medical Education, University of Ottawa ;  Andreea C. Brabete ,  Department of Personality, Centre of Excellence for Women's Health, University of British Columbia ;  Angela Colantonio ,  Rehabilitation Sciences Institute, University of Toronto; Nicole Etherington, Centre for Practice Changing Research, Ottawa Hospital Research Institute ;  Amédé Gogovor ,  Department of Family Medicine and Emergency Medicine, Université Laval ;  Lorraine Greaves ,  Centre of Excellence for Women's Health, University of British Columbia; Marie Laberge, School of Rehabilitation, Université de Montréal ;  France Légaré ,  Department of Family Medicine and Emergency Medicine, Université Laval ;  Karen Messing ,  Biological Sciences, Université du Québec à Montréal ;  Tatyana Mollayeva ,  Rehabilitation Sciences Institute, University of Toronto ;  Sylvie-Marianne Rhugenda ,  Faculty of Social Sciences, Université Laval ;  Kathryn Sibley ,  Department of Community Health Sciences, University of Manitoba ;  Cora Siebert ,  Institute of Gender and Health, Canadian Institutes of Health Research; Sharon Straus, St.
Background: We performed a systematic review and meta-analysis of studies assessing the end-expiratory occlusion test (EEXPO test)-induced changes in cardiac output (CO) measured by any haemodynamic monitoring device, as indicators of preload responsiveness. Methods: MEDLINE, EMBASE and Cochrane Database were screened for original articles. Bivariate random-effects meta-analysis determined the Area under the Summary Receiver Operating Characteristic (AUSROC) curve of EEXPO test-induced changes in CO to detect preload responsiveness, as well as pooled sensitivity and specificity and the best diagnostic threshold. Results: Thirteen studies (530 patients) were included. Nine studies were performed in the intensive care unit and four in the operating room. The pooled sensitivity and the pooled specificity for the EEXPO test-induced changes in CO were 0.
Identification of records Our aim was to identify all studies evaluating the ability of the EEXPO test to predict a significant increase in CO or surrogate (velocity time integral of the left ventricular outflow tract with echocardiography, blood velocity of the descending aorta with oesophageal Doppler) compared to the one induced by a subsequent volume expansion or by a PLR test. We included into our analysis only the studies that provided sensitivity, specificity and the area under the receiver operating characteristic curve (AUROC) of the EEXPO test with the corresponding diagnostic threshold. Moreover, only studies on adults, that were published in full text or accepted for publication in indexed journals, were included in our analysis. No language restriction was applied. We searched the US National Library of Medicine's MEDLINE database, the EMBASE database and the Cochrane Database of Systematic Reviews for relevant studies published from 1960 to 1st October 2019.
Data extraction Using a standardised form, two investigators (FG and SR) independently extracted several data from the selected studies, including demographic characteristics of the investigated population, ventilatory variables, the duration of the EEXPO test, the method used to assess its haemodynamic effects on CO or its surrogate, the amount and type of fluid infused and the duration of the infusion of volume expansion, when performed, as well as the criteria used to define preload responsiveness. Moreover, the number of true-positives, true-negatives, false-positives and false-negatives as well as sensitivity and specificity, the AUROC and the best EEXPO-induced increase in CO or surrogates able to detect preload responsiveness were collected.
Study description Study-specific sensitivity and specificity values have been computed considering a 0. 5 continuity correction as indicated in the literature (Additional file 1: Figure  S2 ). The 95% confidence intervals have also been calculated using the Wilson  [25]  method. A graphical representation of the data has been provided. Paired forest plots on sensitivity and specificity and confidence ellipses (95%) plots have also been reported. The correlation of sensitivities and false-positive rates has been reported to investigate a possible threshold effect. For the principal analysis, if more than one technique was used to assess the haemodynamic effects of the EEXPO test, we chose the one considered to be the most reliable: when both oesophageal Doppler and end-tidal carbon dioxide were used, we only considered oesophageal Doppler and when both echocardiography or oesophageal Doppler and calibrated pulse contour analyses were used, we considered only pulse contour analysis.
e. , greenscapes) shape surfing. We compiled a cross-continental dataset of   1 ). We quantified green-wave surfing behavior as the absolute difference in days between the date of peak green-up and the date the animal used a location [hereafter Days-From-Peak, 0=perfect surfing; 7]. In support of the Greenscape Hypothesis, animals surfed closer to the date of peak green-up when green-up occurred in a wave-like pattern, characterized as rapid green-up rate, consecutive green-up order, and long green-up duration (Figure  2 ). The robust and general support for the Greenscape Hypothesis indicates that surfing the green wave is an adaptation to enhance foraging in environments with fleeting green-up that moves like a wave across northern temperate regions of the globe. Because green-wave surfing leads to migration  [12] , wave-like greenscapes may predict the landscapes where migration should be a common movement tactic.
As landscapes become increasingly altered by climate change and anthropogenic development, identifying movement tactics that should be profitable but have been lost or altered will be critical to the conservation of animal movement and the ecological processes it sustains. data collection efforts. We thank the following organizations (with population and species that they funded in parentheses): Parks Canada (Jasper elk and mule deer); Muskwa-Kechika Trust  The proportion of migratory individuals in a population increases as green-up rate becomes more fleeting (A) and as green-up becomes more ordered (B). Specifically, odds of having a completely migratory herd increased 330 fold from an environment with no wave (i. e. order = 0) to an environment with an idealized wave (order = 1). Likewise, odds of the herd being completely migratory decreased 137 fold when comparing environments with the most ephemeral green-up to the most prolonged green-up.
Data and Code Availability The gps-collar data supporting the current study have not been deposited in a public repository because these are part of further investigation, but data and code is available from the corresponding author on request. MODIS data is publicly available through the NASA Earthdata database (https://earthdata. nasa. gov/). The Human Footprint Index data is available at https://doi. org/10. 5061/dryad. 052q5.
Experimental Model and Subject Details We compiled relocation data from GPS collared mule deer (Odocoileus hemionus) and elk (Cervus canadensis) in North America, and roe deer (Capreolus capreolus) and red deer (Cervus elaphus) in Europe. We only included adult (> 1-year-old) individuals within a given year with at least one relocation per day over the 120-day spring period (see below for details). Populations were only included in the analysis if there was a minimum of ten individuals in the population. These criteria resulted in a total of 2,446 animal-years derived from 1,696 unique individuals and 61 populations. Details of each population can be found in Table  S1.
Method Details The greenscape and green-wave surfing We quantified forage quality using remotely sensed data. We calculated the Normalized Difference Vegetation Index [NDVI; 35] from bands 1 and 2 of MOD09Q1 v006 surface reflectance data (250-m spatial resolution, 8-day temporal resolution). We cleaned and filtered the NDVI data as described by Bischof et al. [13] , except that we used the snow flag for the MOD09Q1 data to floor values during winter rather than using a fixed window  [3]. For each pixel in each of the 61 study areas, we fit a double logistic curve to the annual time series of the processed NDVI data. To pinpoint the date of peak green-up, we took the first derivative of the double logistic curve, resulting in a curve of the Instantaneous Rate of Green-up [IRG; 13] that peaks on the day when NDVI increases most rapidly.
We examined the potential confounding effect of human development and productivity on our test of the Greenscape Hypothesis. We compared the coefficient estimates of the original models and models that included the 2009 Human Footprint Index  [43]  to account for human development, or integrated NDVI to account for productivity. Human development or productivity had no effect on the test of the Greenscape Hypothesis or the examination of the effect of space-use on green-wave surfing (Figure  S4 ). We also examined the potentially confounding effect of an overrepresentation of elk in our dataset and found no evidence that this biased our inference. Specifically, our GPS collar dataset contains two potential drawbacks that could bias our inference. First, some animals were collared for more than one year resulting in temporal pseudoreplication for 31% of the animal-years in the dataset.
Second, elk were overrepresented in the dataset, composing 62% of the animal-years of data, with data from Wyoming elk composing 55% of all elk animal-years and 30% of all animal-years. To determine the degree to which these drawbacks influenced the inference drawn from our analysis, we subset the dataset by 1) randomly selecting one animal-year for any individual that was monitored for greater than one year, and 2) randomly selecting only three out of the 20 elk populations from Wyoming so that the number of populations for each species was evenly represented. We repeated the subsampling 100 times and conducted the greenscape analysis and the green-wave surfing analysis on each of the subsampled datasets. We compared the mean and 95% CIs for the coefficient estimates derived from the tests of the Greenscape Hypothesis and the influence of space-use on green-wave surfing for the 100 subsampled datasets to the coefficient estimates of the full dataset.
In all instances, the 95% CI for the subsampled datasets overlapped with the original estimates of the intercepts or slopes for both the greenscape model and the model examining the impact of space-use on green-wave surfing (Table  S2 ). We compared the median Days-From-Peak and IRG for migrants and residents of each species from the full dataset to that of the subsampled datasets. In all instances, the median of interquartile range of the 100 subsampled datasets always included the median value from the full dataset (Table  S3 ). Together, these findings suggest that temporal pseudoreplication and overrepresentation of elk from Wyoming are an unlikely source of bias in our analysis.
To examine the influence of the greenscape on migratory propensity at the population level, we first subset the data to only include individuals classified as migratory or resident (52% and 27% of animal-years respectively). Before calculating the proportion of migratory individuals in a population (hereafter migratory propensity), we excluded any populations with less than 10 individuals total that were classified as either migratory or resident, which resulted in 56 out of 61 original populations being retained. We calculated the population-level average of the available green-up rate, order and duration to quantify the greenscape for each population. To examine the relationship between the greenscape and migratory propensity, we conducted multiple logistic regressions because green-up order was collinear with green-up rate (Pearson's correlation = 0.
British Columbia Ministry of Environment, and the University of Northern British Columbia (Northern BC elk); Province of British Columbia and the Habitat Conservation Trust Foundation (Southeast BC elk); Idaho Department of Fish and Game and Wildlife and Sport Fish Restoration Program Funds, Federal Aid in Wildlife Restoration Funds (all Idaho elk and mule deer); Safari Club International, Montana Fish Wildlife and Parks, and Rocky Mountain Elk Foundation (Bitterrroot elk); NSF (LTREB grant #1556248 to Hebblewhite & Merrill), Parks Canada, Safari Club International Foundation, Alberta Fish and Game Association (MSL), Alberta Conservation Association Research program, Wild Sheep Foundation of Alberta, Alberta Conservation Association (grants in biodiversity), NSERC Discovery, NSERC PGS, Rocky Mountain Elk Foundation, International Association of Bear Research and Management, West Fraser, Weyerhaueser, Mountain Equipment Cooperative, and the Cannon National Parks Science Scholarship (Ya Ha Tinda Elk); Wyoming Game and Fish Department, Wyoming Animal Damage Management Board, Rocky Mountain Elk Foundation, USFS (Shoshone National Forest); Cody Country Outfitters and Guides, Prince Albert II of Monaco Foundation, Knobloch Family Foundation, George B Storer Foundation, Fran and Lenox Baker Foundation, Robert B and Emilie W Betts Family Foundation, National Geographic Society (Grant Award Number: WW-100C-17); Wyoming Outfitters and Guides Association (Clarks Fork Elk, Cody Elk and Wiggins Fork Elk); USDA APHIS VS, Greater Yellowstone Interagency Brucellosis Committee, National Science Foundation, Wyoming Wildlife Livestock Disease Partnership, and the Morris Animal Foundation (all remaining elk datasets from Wyoming); Habitat Conservation Trust Foundation and the Fish and Wildlife Compensation Program (Southeast BC mule deer); Wyoming Game and Fish Department, Muley Fanatic Foundation, Wyoming Governor's Big Game License Coalition, Bowhunters of Wyoming, Wyoming Wildlife and Natural Resource Trust, Knobloch Family Foundation, Boone and Crockett Club, Wyoming Outfitters and Guides Association, US Bureau of Land Management and Wyoming Animal Damage Management (Wyoming Range Deer); Département de l'Etude du Milieu naturel et Agricole (Hertogenwald and St.
Hubert red deer); "Move-It" ANR grant ANR-16-CE02-0010-02 to AJMH and NM (Aurignac roe deer); The Swedish Association for Hunting and Wildlife Management, the Swedish Environmental Protection Agency, Holmen Skog AB, Ittur AB, Virå Bruk AB, Högestads & Christinehofs Fideikommiss, Region Skåne, Karl-Erik Önnesjös Stiftelse för Vetenskaplig forskning och Utveckling, Stiftelsen Oscar och Lili Lamms Minne, Marie-Claire Cronstedts Stiftelse, Ericsbergs Fideikommiss AB, Helge Ax:son Johnsons stiftelse, Ågerups & Elsagårdens Säteri AB, Håkan Wikholm Assmåsa Gods AB, and Kolmårdens insamlingsstiftelse/Tåby Allmänning (Kolmården and Skåne red deer); the Silfverschiöld family, the Swedish Environmental Protection Agency Wildlife Research fund, Swedish Hunters Association Research fund and the private fund "Marie-Claire Cronstedts stiftelse" (Koberg roe deer); Ministry of Rural Affairs and Consumer Protection, state of Baden-Württemberg (Rhine Valley roe deer); Forestry and Wildlife Service, Autonomous Province of Trento (Bondone and Rendena-Giudicarie Roe deer); Research Council of Norway (grant 251112; NINA west roe deer).
The contribution of the Bavarian Forest National Park was funded by the program Ziel ETZ Free State of Bavaria -Czech Republic 2014-2020 (INTERREG V). F. Cagnacci was supported by the Sarah and Daniel Hrdy Fellowship 2015-2016 at Harvard University OEB. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the U. S. Government.
Acknowledgments: We thank  Bryan Lamont  for contributing data. Comments from  Bethann Merkle ,  Jacob Goheen ,  Brett Jesmer  and  Tristan Nuñez  helped to improve earlier versions of this manuscript. We thank  Alethea Steingisser ,  Joanna Merson  and  Jim Meacham  for creating the study area map. This project was conceived during a workshop led by  AM at the Centre for Advanced Study in Oslo, Norway , that funded and hosted our research project " Climate effects on harvested large mammal populations " during the academic year of 2015-2016. EOA was supported by the  National Science Foundation (NSF) Graduate Research Fellowship Program , the  University of Wyoming's Berry Fellowship , the  Wyoming NASA Space Grant Consortium  ( NASA Grant  # NNX15AI08H ) and a  Graduate Research Opportunities Worldwide award , funded by the  NSF  and the  Research Council of Norway.
Fund, British Columbia Ministry of Environment, and the University of Northern British Columbia (Northern BC elk); Province of British Columbia and the Habitat Conservation Trust Foundation (Southeast BC elk); Idaho Department of Fish and Game and Wildlife and Sport Fish Restoration Program Funds, Federal Aid in Wildlife Restoration Funds (all Idaho elk and mule deer); Safari Club International, Montana Fish Wildlife and Parks, and Rocky Mountain Elk Foundation (Bitterrroot elk); NSF (LTREB grant #1556248 to Hebblewhite & Merrill), Parks Canada, Safari Club International Foundation, Alberta Fish and Game Association (MSL), Alberta Conservation Association Research program, Wild Sheep Foundation of Alberta, Alberta Conservation Association (grants in biodiversity), NSERC Discovery, NSERC PGS, Rocky Mountain Elk Foundation, International Association of Bear Research and Management, West Fraser, Weyerhaueser, Mountain Equipment Cooperative, and the Cannon National Parks Science Scholarship (Ya Ha Tinda Elk); Wyoming Game and Fish Department, Wyoming Animal Damage Management Board, Rocky Mountain Elk Foundation, USFS (Shoshone National Forest); Cody Country Outfitters and Guides, Prince Albert II of Monaco Foundation, Knobloch Family Foundation, George B Storer Foundation, Fran and Lenox Baker Foundation, Robert B and Emilie W Betts Family Foundation, National Geographic Society (Grant Award Number: WW-100C-17); Wyoming Outfitters and Guides Association (Clarks Fork Elk, Cody Elk and Wiggins Fork Elk); USDA APHIS VS, Greater Yellowstone Interagency Brucellosis Committee, National Science Foundation, Wyoming Wildlife Livestock Disease Partnership, and the Morris Animal Foundation (all remaining elk datasets from Wyoming); Habitat Conservation Trust Foundation and the Fish and Wildlife Compensation Program (Southeast BC mule deer); Wyoming Game and Fish Department, Muley Fanatic Foundation, Wyoming Governor's Big Game License Coalition, Bowhunters of Wyoming, Wyoming Wildlife and Natural Resource Trust, Knobloch Family Foundation, Boone and Crockett Club, Wyoming Outfitters and Guides Association, US Bureau of Land Management and Wyoming Animal Damage Management (Wyoming Range Deer); Département de l'Etude du Milieu naturel et Agricole (Hertogenwald and St.
Introduction The FES2014 global ocean atlas is the latest release of a twenty-years-long effort to improve tidal predictions needed in satellite altimetry de-aliasing. It is based on the hydrodynamic modelling of tides (T-UGOm unstructured grid model) coupled to an ensemble data assimilation code (SpEnOI). It is a very significant 30 upgrade compared to both FES2004  (Lyard et al. , 2006)  and FES2012  (Stammer et al. , 2014)  atlases, thanks to the improvement of the assimilated data accuracy and the model performances. To some extent, FES2014 can be considered as an iterative step of the FES2012 atlas, mostly motivated by the overwhelming progress made in the hydrodynamic solutions accuracy late at the end of the FES2012 project and which could not be incorporated due to the project schedules.
Therefore, considering the significant potential improvements and thanks to the financial support of CNES, decision was made to rapidly upgrade the FES2012 atlas toward the FES2014 atlas. The FES2014 atlas denomination is quite misleading, as its final version has been delivered in early 2016. This has left time to the project team to precisely assess the FES2014 accuracy and performances in altimetry data  de-15  aliasing correction, and to make some final adjustments to guarantee the best possible quality at that time. It results in 3 available FES2014 releases. FES2014a is the first guess based on the self-attraction and loading (LSA) provided by GOT model and used for internal verification checks and then the production of the selfconsistent FES2014b SAL atlases used within FES2014b altimetry assimilation data processing.