dataesr/scientific-paragraphs-categorization huggingface dataset:
5 The first involved drawing on a variety of existing possible types of vaccines, whatever their origin. Second, OWS re-engineered the traditional sequencing of vaccine production, collapsing early research and clinical trial phases; and third, production of vaccines was launched as testing was still going on  (Durand). To these points may be added the management structure of OWS itself, the willingness to commit substantial funding to the search for a vaccine as well as for pre-certification manufacture, and preparations for distributing a vaccine once certified and produced. To achieve its objectives of providing 300 million vaccine doses, OWS initially selected 14 candidate vaccines from more than a hundred, and then narrowed its choice to about eight (DHHSa). These were chosen using four criteria: i) candidate vaccines had to provide data rapidly suggesting safety and efficacy; ii) they had to be Another key factor in OWS's approach was to shorten the sequencing of traditional vaccine development and rollout.
#4: Merger, Duplicates Removal and Mapping of Pathologies With Orphanet Nomenclature The three RD tables previously obtained were merged (336 remaining RD entries-59 duplicate entries merged). For the 336 RD entries, a search for correspondence with the Orphanet nomenclature was carried out. A confidence index was introduced to characterize the degree of certainty on the correspondence (High/Medium/Low): 248 matches with a "High" correspondence (74%), 39 matches with a "Medium" correspondence (12%) and 28 matches with a "Low" correspondence (14%) were found. The list was finally reviewed by one of the co-authors, expert on rare diseases (SA), with proposals for modification, grouping or removal of pathologies. An output table including 273 RD entries was finally produced. Information on each RD (ORPHA number, ICD 10 code, synonyms, inheritance, age of onset and prevalence) was then collected from the Orphanet database for the purpose of producing statistics, and are thus not specific to France (all details can be found in the open-access Orphanet report series).
There is a potential bias on the age of onset as the age categories used in the Orphanet database overlap. However, despite potential redundant assignments, this does not call into question the general analysis presented further in the article. The inheritance codes were simplified in three categories: "Genetic origin" encompasses all diseases with a genetic origin whatever the mode of inheritance. "Partially genetic" includes diseases with a mix of different possible origins, some being genetic, some being acquired. "Non genetic" includes all other diseases, although some of them may have some genetic determinants as minor co-factor. The pathologies were classified by broad categories, following the logics applied in the International Classification of Diseases in its 11th edition  (Organization, World Health).
9%, comparable to the 75% for the whole set of RDs in the Orphanet database (Table  3 ). Moreover, despite a bias in the onset age entry in the Orphanet database (overlap of different entries), most of the 251 conditions are pediatric disorders (Figure  3 ), which is similar to what is generally described in the RD field. Finally, most of the 251 conditions are very rare (74 RD, 41. 8%), or ultra-rare (58 RD, 32. 7%), as displayed on the distribution of prevalence (Figure  4 ).
DISCUSSION This work explored the feasibility of identifying a subset of RDs at urgent needs for the development of digital-based solutions to reduce the delay to diagnosis. The two proposed criteria for disease selection are based on robust public data. Their use ended in the establishment of an appropriate list of RDs, considering the intended goal. A list ready to be submitted to expert clinicians for validation, before proposing it to potential development and test of both digital and/or organizational solutions. This work could notably be supported by the use of the data from the French National RD Database (BNDMR, 2021) (BNDMR). If the time to diagnosis is deemed not acceptable, this will clearly indicate that digital and/or organizational solutions should be considered in priority for those RDs.
However, even if the study gives relevant results, these sources have de facto several limitations. They may suffer from non-completeness. In addition, the dataset is a snapshot of the situation as of January 2018, based on information  sources from July 2017 to January 2018. It is representative of the situation at that time only. The proposed list will need to be updated and adapted to the local situation, for any further use. The work is based on the situation in France, as it was as a proof of concept in the context of a national initiative to develop digital-based solutions for the diagnosis of RDs. Only French clinical guidelines were considered, as the production of these clinical guidelines was a measure of the first and second French National Plan for RDs  (PNMR 1, 2004)   (PNMR 2, 2010; PNMR 3, 2018).
ACKNOWLEDGMENTS We would like to extend our gratitude to all those who contributed to the initiative and supplemented our work, especially:  Rémy Choquet  ( Orange Healthcare ),  Véronique Schneider  ( Sanofi Genzyme France ),  Claude Messaien ,  Elisa Salamanca ,  Arnaud Sandrin , and  Anne-Sophie Jannot  ( AP-HP BNDMR team ).
DATA AVAILABILITY STATEMENT Publicly available datasets were analyzed in this study. This data can be found here: https://www. orpha. net/orphacom/cahiers/docs/GB/ list_of_orphan_drugs_in_europe. pdf; https://solidarites-sante. gouv. fr/soins-et-maladies/medicaments/professionnels-de-sante/ autorisation-de-mise-sur-le-marche/article/autorisations-temporairesd-utilisation-atu; https://www. ansm. sante. fr/Activites/Autorisationstemporaires-d-utilisation-ATU/ATU-nominative-Liste-desspecialites-autorisees/(offset)/5; https://www. ema. europa. eu/en/ medicines/download-medicine-data#rare-disease-(orphan)designations-section; https://www. has-sante. fr/jcms/c_1340879/ fr/protocoles-nationaux-de-diagnostic-et-de-soins-pnds; https:// solidarites-sante. gouv. fr/soins-et-maladies/prises-en-chargespecialisees/maladies-rares/article/l-offre-de-soins; https:// www.
Xavier Raurich  thanks …nancial support from the  Spanish Government  and the  European Union  through grant  RTI2018-093543-B-I00. Thomas Seegmuller thanks the …nancial support of the  French National Research Agency  Grant  ANR-17-EURE-0020  and  ANR-15-CE33-0001-01. This paper bene…ts from the comments of  Anpeng Lee  and the participants of seminars at  Dongbei University, University of Guanajuato  and also of the participants in the workshop of the  Spanish Macroeconomics Network.
Dataset construction The sample is comprised of 4. 68 million public patent applications filed at the USPTO with a first action completed between 2001 and 2017 (variable descriptions in Table  1  and summary statistics in Table 2). These data were provided in a bulk downloadable format by the USPTO's Office of Chief Economist (OCE) in the Patent Examination Research Dataset (PatEx, see  Graham et al. , 2018). The application data includes overall prosecution outcome, filing and disposal dates, anonymized USPTO examiner identification numbers, U. S. patent classification (USPC), associated USPTO technology center (TC), and other patent application characteristics. In addition to application data, PatEx includes a history of all patent office events (both by the applicant and the USPTO) for each application from filing to disposal, disposal type, and expiration date (if the patent expired).
This transaction history dataset includes information on all USPTO office actions, including rejections, notices of allowance, restrictions, and Quayle actions. 7 We assume that the examiner's first action is the first instance of a non-final rejection, final rejection, notice of allowance, restriction or Quayle action in the PatEx transaction history. From the PatEx transactions data, we also obtain the occurrence and date of all examiner's amendments associated with applications in our sample, identified by the transaction code ''Ex. a''. Examiner promotion and grade data at first action were taken from internal USPTO databases, with examiner GS-levels 5 and 15 dropped from the sample due to infrequency (consistent with  Frakes and Wasserman, 2017). Examiner experience was calculated using the examiner promotion data by measuring the length of time, in months, between the examiner's start date and the application decision date.
Although institution decisions are publicly available, we use an internal USPTO dataset in our analysis. To account for application type, we consider application parent type (continuation, divisional, continuation-in-part, or new application), application priority type (e. g. , foreign priority), and whether the application stemmed from the U. S. national stage entry of an application filed under the Patent Cooperation Treaty (PCT). We combine these two variables (foreign priority type and parent type) from the PatEx data to create a modified application parent type variable. From this variable, we can differentiate by continuity (e. g. continuation application), application route (e. g. , PCT application), and whether an application had been filed previously within another jurisdiction. Finally, we develop a measure of examiner specialization, computed at the examiner-by-year level, which allows us to control for the degree to which an examiner specializes in a particular technology.
Acknowledgments The authors are grateful to editor  Maryann Feldman  and the two anonymous reviewers for very insightful feedback which greatly improved this paper. We also thank  Jesse Frumkin ,  Alexander Giczy ,  James Forman ,  Andrew Toole ,  Nicholas Rada ,  Sam Ransbotham ,  Robert Fichman ,  Richard Miller ,  Amanda Myers ,  Juan Pablo Rud ,  Maris Goldmanis ,  Alan Marco ,  William Kerr ,  Tarun Khanna ,  Shane Greenstein ,  Gregory Mills ,  Benjamin Balsmeier ,  Georg von Graevenitz , and participants at Patcon9, Academy of Management Annual Meeting 2019, and  Midwest Macroeconomics  for valuable feedback. Author Mike Teodorescu thanks the  Carroll School of Management at Boston College  for the research funding provided as well as  Harvard Business School  for research funding provided from the  Doctoral Program  for research travel to the USPTO during the final dissertation writing year.
Antimicrobial shortages were also the most widely reported in a survey by the Society of Hospital Pharmacists of Australia, as well as the most widely reported of all unique drug product shortages (2). Similar results were observed in a recent survey in France (3) with a trend toward an increasing number of antimicrobial shortages over recent years. Using a US nationwide database, Quadri et al. estimated a median of 10 new antibacterial drug shortages per year between 2001 and 2013 with a sizeable rise since 2007 and median duration exceeding 6 months  (6). Noticeably, a considerable proportion of shortages were without clear reasons. Using the same US database and focusing on vaccines, Ziesenitz et al. showed that even though vaccine shortages were a smaller proportion of all drugs shortages, half of them concerned the pediatric schedule, therefore resulting in vaccine deferral  (7).
To avoid the effect of model inaccuracy from small sample size, we limited our analysis to species that had occurrence records of 30 and more  53. These resulted in 1,755 and 934 target tree species at a spatial resolution of fine and coarse grids, respectively. See Supplementary Data 1 for the list of these target species. For the present period, we obtained a dataset of 19 bioclimatic variables, calculated from monthly minimum temperature, maximum temperature, and precipitation at the resolution of fine grids downloaded from the WorldClim 1. 4 (www. worldclim. org)  54. Then, we obtained land-use variables at a resolution of fine grids from the MODIS (Moderate resolution Imaging Spectroradiometer) Land Cover Type for the year 2005 (glcf. umd. edu/data/lc). We used land cover classes of the global vegetation classification scheme of the International Geosphere-Biosphere Programme (www.
e. , α-diversity). The values of the elasticity of substitution were originally estimated based on forest inventory datasets collected at the local spatial scale 17. For avoiding a potential mismatch due to scaling issues  23, 74  , we estimated the changes in productivity at the scale of fine grids. We used a net primary productivity (NPP) dataset estimated using the MODIS imagery 26 for the year 2005 (note, in the terrestrial biomes analysed here, total NPP was approximately 43. 78 Pg C yr -1 ). We obtained NPP values at the scale of fine grids. Here we assumed that all fine grids in a given coarse grid showed the equivalent changes in productivity in a proportional scale, reflecting the mean change in α-diversity expected to occur in that coarse grid. Based on these estimations, we have obtained absolute changes in forest productivity (kg C m -2 yr -1 ) for different scenarios of climate/land-use changes at the scale of fine grids.
Data analyses We summarized our results at different spatial scales from local to global. Here, the fine grids are defined as the local scale. We also focused on the scales of countries and biomes. Terrestrial biome categories are based on the 14 terrestrial ecoregions used by the World Wildlife Fund (www. worldwildlife. org). We have obtained information for areas and names of individual countries from Natural Earth (www. naturalearthdata. com). To be relevant for global policy, we have summarized results also at the scale of the subregion used in the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES; www. ipbes. net/deliverables/2b-regionalassessments). For some territories and nations that are not explicitly classified into regional categories, we assigned their subregions based on their geographical locations.
To do this, we combined all occurrence records in the final dataset for target taxonomic groups (including species with < 30 records) and generated a set of background data, weighted by the sampling density of occurrence records. The number of background data was determined as 20% of grids that contained occurrence records of the target taxonomic group. In the model development procedure, the background data were extracted within the native range for each species. We identified a species-specific set of explanatory variables for each of the target species. First, by using the five land-use and 19 bioclimatic variables, we generated all possible combinations of these explanatory variables. We then excluded explanatory variables showing collinearity. We selected the most parsimonious combination of explanatory variables based on the corrected Akaike information criterion (AICc)  17.
MaxEnt is capable of building complex nonlinear functions of explanatory variables by combining simple mathematical transformations of explanatory variables, or so-called features 18 ; it selects the features for each explanatory variable that contribute most to model fit using regularization. We tested 31 regularization coefficients (from 0 to 15 at increments of 0. 5) and chose the one for each species that maximized model fit under the given combination of explanatory variables selected, based on AICc 19. Among the final models developed for all species, we discarded those with poor performance for subsequent analyses based on 10-fold cross validation, as follows. We assessed model performance using the continuous Boyce index (CBI), which is used to evaluate model quality for predictions based on presence-only data 20.
Because dispersal ability is strongly related to life-history traits 23 , we collected information on life-history traits for all target species and estimated dispersal distance for each species by the 2070s. Here dispersal distance per generation was estimated from the formula based on an earlier work 23 ; we adopted 'group 5' formula in dispeRsal function, which requires species-specific data for dispersal syndrome and growth form. For dispersal syndrome, we compiled the database from various sources (details are found in the Source Data of the earlier study 1 ). For growth form, we obtained the data from a database of The IUCN Red List of Threatened Species (www. iucnredlist. org). Generation length was estimated according to growth form 24. Based on the above estimates, we obtained habitat maps for individual species.
Species richness and productivity estimation For the calculations below, we have mainly relied on "vegan" 25 , "mobsim" 26 , and "xgboost" 27 package of the R software 28. We projected spatial distributions of individual species for the year 2005 and 2070s at the spatial resolution of 30 arcminutes (coarse grids; n = 32,670). For both periods, we obtained the total number of species present in each coarse grid (hereafter, γ-diversity). For the former year, we also projected spatial distributions of individual species at the spatial resolution of 30 arcseconds (fine grids; n = 115,426,714). Based on these projections, we calculated changes in species richness at the scale of fine grids (hereafter, α-diversity) from 2005 to 2070s for different scenarios of climate and land-use changes. The reason why we conducted this downscaling was that there could be a potential mismatch due to scaling issues  29, 30.
That is, changes in γ-diversity are not universally proportional to those in α-diversity across different coarse grains. Furthermore, we relied on the values of the elasticity of substitution (θ) of a previous study 31. Although assumed to be scaleindependent, these estimates were developed based on the forest inventory dataset collected at the local spatial scale. We thus aimed to estimate the changes in productivity at this fine-scale. Here, we first obtained species-area and endemics-area relationships (SARs and EARs, respectively) for the year 2005 that were unique to each of the coarse grids; 50 sub-grids were randomly chosen with 1 % intervals between the scales of the fine and coarse grid to construct the curves  26. If all fine grids were forested, a coarse grid had 3,600 fine grids.
e. , α-diversity). More specifically, the θ values represent the marginal productivity-the change in productivity resulting from the one-unit decline of species richness after accounting for climatic and soil covariates. The θ values were estimated across the forested biomes worldwide 31 , ranging between 0. 198 and 0. 299; a global mean was estimated as 0. 26 with a standard deviation of 0. 09. Note that 0 < θ < 1 represents a positive and degressively increasing curve of richness-productivity relationships. The θ values were estimated based on tree volume productivity (m 3 ha -1 yr -1 ) in terms of periodic annual increment, calculated from the sum of individual tree stem volume (m 3 ) 31. The values of the elasticity of substitution were originally estimated based on the forest inventory dataset collected at the local spatial scale 31.
For avoiding a potential mismatch due to scaling issues  29, 30  , we estimated the changes in productivity at the scale of fine grids. We used a net primary productivity (NPP) dataset estimated using the MODIS imagery 43 for the year 2005. In the terrestrial biomes analysed here, the total NPP was approximately 43. 78 Pg C yr -1. Note that, because tree productivity was originally estimated based on an increment in stem volume of individual trees, the estimated proportional changes in forest productivity cannot fully capture NPP of terrestrial vegetation; however, a substantial fraction of primary productivity in forested biomes is derived from tree growth. We obtained NPP values at the scale of fine grids. Here we assumed that all fine grids in a given coarse grid showed the equivalent changes in productivity in a proportional scale, reflecting the mean change in α-diversity expected to occur in that coarse grid.
Data analyses We primarily relied on "maptools" 44 , sf" 45 , and "tidyverse" 46 package of the R software 28 for summarizing our estimates. Within the forested areas analysed in the previous study 31 , we summarized our results at different spatial scales from local to the globe. Here, the fine grids are defined as the local scale. We also focused on the scales of countries and biomes. Terrestrial biome categories are based on the 14 terrestrial ecoregions used in World Wildlife Fund (www. worldwildlife. org). We have obtained information for areas and names of individual countries from Natural Earth (www. naturalearthdata. com; as of August 1 st , 2019), which primarily follows the setup of the United Nations Statistics Division (unstats. un. org; as of August 1 st , 2019). To be relevant to the global policy, we have summarized results also at the scale of the subregion used in the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES; www.
ipbes. net/deliverables/2b-regional-assessments). For some territories and nations that are not explicitly classified into regional categories, we assigned their subregions based on their geographical locations. To quantify the effect sizes of mitigation efforts on conservation of species and productivity, we calculated the reductions in local-scale loss of species (ΔSR) and productivity (ΔP) as a log ratio scale, which assumes that zero corresponds to the true absence of the outcome (i. e. , no difference between the two scenarios). Estimates based on the baseline and mitigation scenarios were used for the denominator (control; ΔSRbaseline and ΔPbaseline) and numerator (treatment; ΔSRmitigation and ΔPmitigation), respectively. We used "ARPobservation" 47 package of the R software 28 to obtain logresponse ratios and the associated 95% confidence intervals for each of the combinations of SSP scenarios and GCMs.
To facilitate interpretation, we multiplied -1 with the effect sizes and thereby positive and negative values, respectively, indicate more and less effectiveness of climate mitigation policy in reducing species loss and the associated productivity loss (also see a schematic diagram in Supplementary Fig. 3 ). We confirmed that spatial patterns of the effect size of productivity responses were not driven by those of the θ values; R 2 values of linear regressions ranged between 0. 0005 and 0. 016 across all combinations of GCMs and SSPs. To ensemble results across the three GCMs, we adopted a meta-analytical approach; we used "metafor" 48 package to obtain global means and the associated 95% confidence intervals for each SSP scenario. We repeated the above calculations at the biome-and IPBES subregion-, and country-scale.
Also, little and large shrink of forest areas is expected for the SSP1 and SSP3 baseline scenario, respectively. Likely reflecting these contrasts, global ΔPbaseline and ΔPmitigation were both relatively small (SSP1) and both relatively large (SSP3), resulting in similar values of productivity conservation. Likewise, our estimations were primarily based on a comparison between the baseline and mitigation scenarios. • Background data: To incorporate sampling bias into MaxEnt, we took the bias background approach, which uses the prior information on the spatial distribution of survey effort to preselect background locations before running MaxEnt. In this approach, the effect of sampling bias cancels out because it is common to both occurrence and background. To do this, we combined all occurrence records in the final dataset for target taxonomic groups (including species with ≤ 30 records), and generated a set of background data, weighted by the sampling density of occurrence records.