dataesr/scientific-paragraphs-categorization huggingface dataset:
In academic databases, fortunately, the scenario changes a little. PubMed uses, in its Medical Subject Headings (Mesh) Database, the term "antiobesity agents", in which more than 19,000 results appears, and "weight loss drugs/agents/medications" in PubMed leads to much fewer results (less than 500). As such, it can be concluded that the academic milieu is more aware of this difference (although weight-loss drugs is a term generally heard in conferences and medical communications), but there is a gap between the scientific production of knowledge in this area and how it is translated to the general public, especially in the media. As such, it is important that the academic community be aware of this difference and increases its efforts to improve language, bridge this gap and reduce stigma.
Nevertheless, in hypertension, "anti-hypertensive medications" is more used than "blood pressure reduction medications", and "hypotensive medications" is seldom used; in diabetes "anti-diabetic" is still more common than "glucose-lowering" or "anti-hyperglycemic". One exception is in hypercholesterolemia, in which "lipidlowering drugs" is a common and widely used term, although in the Mesh database, the correct term would be "anticholesterolemic agents". However, the stigmatization of hyperlipidemia is almost non-existent  (28, 74). Interestingly, non-adherence to anticholesterolemic agents is common, as well as their interruption after blood cholesterol goes down  (74) , and it is possible that pointing out the long-term importance not only of cholesterol reduction itself, but also of prevention of cardiovascular disease, may contribute to greater adherence to treatment  (75, 76).
Today, more than a thousand natural history museums exist, with the largest ones located in Europe and North America. The world's natural history collections provide a window into the planet's past and present, and they are increasingly being used to make actionable predictions relative to climate change, biodiversity loss, and infectious disease. For example, natural history museum data are the fundamental source of primary biodiversity knowledge underlying major policy frameworks. The 2018 Intergovernmental Panel on Climate Change (IPCC) Special Report on Global Warming of 1. 5°C used over 385 million species occurrence records, aggregated and tracked by the Global Biodiversity Information Facility (GBIF), from 5432 data providers, mostly natural history museums  (2, 3) , to show species movement in response to climate change [see supplementary materials (SM) for additional case studies].
Yet despite their enormous potential value to society, the information embedded in the collections housed in these museums is largely inaccessible. Fortunately, advances in digital, isotopic, imaging, and genomic technologies, as well as machine learning and artificial intelligence, are transforming and amplifying how natural history collections can be accessed and used  (1). These innovations are substantially broadening the range of possible applications to include human health, cultural revitalization, and environmental monitoring. Increasingly, Indigenous interlocuters are joining these conversations and enriching them  (4, 5). In the past few decades, several networks have increased cooperation between biodiversity-based institutions around the world. In addition to GBIF, the Taxonomic Databases Working Group (TDWG), the Global Genome Biodiversity Network (GGBN), the Catalogue of Life (COL), the Earth BioGenome Project (EBP), the International Barcode of Life (iBOL), and the Biodiversity Heritage Library (BHL) have provided global leadership for integrating specimen data, taxonomic observations, genomes, and published literature on the natural world.
Guiding principles for governing such data have emerged for traditional [e. g. , FAIR  (6)  Although these institutions and efforts are playing vital roles in aggregating data, they do not create the collections and fill gaps therein. It is the natural history museums that actively curate and expand the collections. Thus, it falls on the museums to lead the way to deploy strategic collecting in service of future collection and policy outcomes. It will not be possible to do this unless museums understand the present scope of the global collection and thus its gaps. Yet natural history museums have generally operated independently, and no interoperable data structure exists to provide open access to their collective holdings. Because most natural history museum data are not digitally discoverable, the networks of data aggregators have not been able to access these "dark data"  (7)  , the majority of museum specimens and objects that are the physical basis of natural history and cultural knowledge.
Collecting in a rapidly changing planet In the year 2100, scientists and policy-makers will look back to the collections made in the 21st century to inform their decisions about the 22nd century. Natural history museums must thus focus on the future. For example, the recent Global Biodiversity Framework  (10)  bridges the Convention on Biological Diversity, the Intergovernmental Panel on Biodiversity and Ecosystem Services, and other efforts by goal-setting 2030 as the time frame for changing trends in biodiversity loss. This is an opportunity to integrate museum datasets with conservation efforts. Yet even now, many regions of the world and many biological groups remain insufficiently studied and documented. Further analysis of the global collection will refine our understanding of the gaps and provide a roadmap for future collecting efforts.
Toward that end, it is our intention to work with GBIF to make our data available to all natural history museums by integrating it with the Global Registry of Scientific Collections (GRSciColl). For example, although our data show that the scale of the global collection is vast, it also highlights conspicuous gaps with respect to tropical and polar regions, marine systems, and undiscovered arthropod and microbial diversity. Also, our study does not address the hundreds of smaller museums, their collections, and their staffs, which comprise the rest of the global collection; these are especially valuable because of their regional holdings and the specificity of their expertise  (8). Further, many current data aggregation efforts have largely focused on biodiversity and have been less inclusive of data from Earth and planetary sciences and anthropology-two fields that are traditionally major components of natural history collections.
j mation design and data illustration of the figures. Funding was provided by NMNH, NHM London, and the American Museum of Natural History. Data for the global collections survey have been published under an open license (Creative Commons Attribution 4. 0 International), and can be persistently accessed at https://doi. org/10. 5281/zenodo. 6985399. Any personally identifiable information was redacted from the dataset prior to publication. A visualization dashboard of these data can be accessed at https://rebrand. ly/global-collections.
ACKNOWLEDGMENTS The authors thank the collections staff from participating institutions for providing data from their collections. We thank GBIF,  GGBN ,  Consortium of European Taxonomic Facilities (CETAF) , and iDigBio for assistance with access to information. We thank  C. Butler ,  R. Corrigan N. Drew ,  H. Little ,  C. A. Meyer , and  R. Snyder  at the  NMNH  and staff at the  Natural History Museum (NHM), London , for their technical assistance with data collation and  C. Baldwin ,  J. Bell ,  C. Dove ,  S. Miller ,  E. Stofan ,  I. Krupnik , and  E. Strong  for comments and advice. We thank  G. Mérite  for infor-.
The details on these supplementary data can be found in the online documentation  1. This paper is organised as follows. Section 2 summarises the Gaia instruments and their wavelength ranges; Sect. 3 summarises the properties of the astrometric and photometric data already included in Gaia EDR3; Sect. 4 presents the BP/RP spectra; Sect. 5 outlines the RVS data processing, listing the improvements implemented in Gaia DR3, and presents the new RVS data products, the line broadening velocity (v broad ) and the G RVS magnitude. Section 6 deals with the variable source content of the catalogue including the Gaia Andromeda Photometric Survey (GAPS); Sect. 7 discusses the NSS content of the release; Sect. 8 comments on the SSOs; Sect. 9 provides information about the APs; Sect. 10 presents the extended object (EO) data processing and results.
Section 11 describes the extragalactic content of Gaia DR3, that is, information on galaxies and quasi-stellar objects (QSOs) or 'quasars' derived by several data processing modules. Section 12 comments on the quality of the release and Sect. 13 provides references to the software tools that are offered to the users to deal with Gaia DR3 data. Finally, Sect. 14 presents some concluding remarks. Each data product section briefly summarises the main limitations of the data, and makes reference to the relevant Gaia Collaboration and Gaia Data Processing and Analysis Consortium (DPAC) papers where more details can be found. For a number of technical details, we refer the reader to the online documentation. All the papers accompanying Gaia DR3 are published in the Astronomy & Astrophysics special issue on Gaia DR3.
Data processing The Gaia satellite has three main instruments on board: the astrometric instrument collecting images in Gaia's white-light G-band (330-1050 nm), the blue BP and red RP prism photometers for low-resolution spectra, and, finally, the RVS. BP/RP spectral data cover the wavelength ranges 330-680 nm and 640-1050 nm, respectively. The resolution is variable and ranges from 30 to 100 for BP and 70 to 100 for RP in λ/∆λ, depending on the position in the spectrum and on the CCD (see  Carrasco et al. 2021 ). In the RVS spectra, the starlight is dispersed over about 1100 pixels in the Gaia telescopes scanning direction (along-scan, AL), sampling the wavelength range from 845 to 872 nm. The resolving power is R ∼ 11 500 (with a resolution element of about 3 pixels). As the wings of the spectra are excluded from the processing, the effective wavelength range of the processed spectra is reduced to 846-870 nm  (Sartoretti et al.
2022). Similarly to Gaia EDR3, Gaia DR3 is based on data collected over a 34-month time interval (for details see Gaia Collaboration 2021). The Gaia data processing is the responsibility of DPAC and is presented in Gaia Collaboration  (2016). The basic statistics on the source numbers for each of the data products in Gaia DR3 can be found in Table  1 , with further details in Table  2. The categories listed in the tables are described in the text below. A visual impression of the release contents is given in Fig. 1  which shows histograms of the distribution in G of the main categories of data products in Gaia DR3.
Astrometry and broad band photometry The astrometric and broad band photometry content of Gaia DR3 is the same as that for Gaia EDR3, except for the addition of the G RVS photometry, but for convenience we summarise here the properties of these data products. Gaia EDR3 provided celestial positions and the apparent brightness in G for 1. 8 billion sources. For 1. 5 billion of those sources, parallaxes, proper motions, and the (G BP -G RP ) colour were also published. Gaia DR3 therefore contains some 585 million sources with five-parameter astrometry (two positions, the parallax, and two proper motion components), and about 882 million sources with six-parameter (6-p) astrometry, including an additional pseudocolour parameter. We refer to Gaia Collaboration (2021) for details on Gaia EDR3, a summary of the astrometric and photometric data processing, and a summary of the limitations of these data and guidance on their use.
1. G-band photometry for 6-p and 2-p sources Section 7. 2 of Gaia Collaboration (2021) and Sect. 8. 3 of  Riello et al. (2021)  describe a correction to be applied to the Gband photometry for sources with 6-p and 2-p astrometric solutions. This correction was provided in the form of Python code and Astronomical Data Query Language (ADQL) recipes. We note here that these corrections are included in Gaia DR3 and therefore should not be applied when working with photometry extracted from the Gaia DR3 data tables in the Gaia archive. As noted in Gaia Collaboration (2021) the G-band photometry for a small number of sources is not listed in Gaia EDR3. This issue has not been fixed for Gaia DR3. The magnitudes can be found in a separate table to be provided through the Gaia DR3 'known issues' web pages (for details see Sect.
Systematic errors The systematic errors present in the astrometry and broad band photometry published in Gaia EDR3 carry over to Gaia DR3. The conclusions from investigations during the data processing for Gaia EDR3 astrometry were that the global parallax bias for Gaia DR3, as measured from quasars, is -17 µas. The RMS angular (i. e. source to source) covariances of the parallaxes and proper motions on small scales are ∼26 µas and ∼33 µas yr -1 , respectively (see  Lindegren et al. 2021b , for details). The parallax bias (and the proper motion systematic errors) varies as a function of magnitude, colour, and celestial position. This is extensively investigated in  Lindegren et al. (2021a)  and a recipe for correcting the parallaxes is given. The systematic errors in the broad band photometry are described in  Riello et al.
GCRF-3 Solar system object transits Fig. 1. Distribution of the mean values of G for the main Gaia DR3 components shown as histograms with bins of 0. 1 mag in width. Top panel: histograms for the basic observational data in Gaia DR3 (spectra, radial velocities, v broad , photometric time series). Middle panel: histograms for the stellar astrophysical contents, and bottom panel: non-stellar astrophysical contents. The sharp transitions in the top and middle panels at G = 17. 65 and G = 19 are caused by the limit on the brightness of sources for which BP/RP spectra are published and the limit up to which astrophysical parameters were estimated. The SSO histogram shows the distribution of the transit-level G-band magnitudes (see  Tanga et al. 2023 , for a distribution of solar system objects in absolute magnitude H).
Acknowledgements. This work presents results from the  European Space Agency (ESA) space mission Gaia. Gaia data are being processed by the  Gaia Data Processing and Analysis Consortium (DPAC). Funding for the DPAC is provided by national institutions, in particular the institutions participating in the  Gaia MultiLateral Agreement (MLA). The Gaia mission website is https://www. cosmos. esa. int/gaia. The Gaia archive website is https://archives. esac. esa. int/gaia. Full acknowledgements are given in Appendix A. A1, page 14 of 22 Gaia Collaboration: A&A 674,  A1 (2023 )  Agence Nationale de la Recherche (ANR)  through grant  ANR-10-IDEX-0001-02  for the ' Investissements d'avenir' programme , through grant  ANR-15-CE31-0007  for project ' Modelling the Milky Way in the Gaia era ' (MOD4Gaia), through grant  ANR-14-CE33-0014-01  for project 'The  Milky Way disc formation in the Gaia era ' (ARCHEOGAL), through grant  ANR-15-CE31-0012-01  for project ' Unlocking the potential of Cepheids as primary distance calibrators' (UnlockCepheids ), through grant  ANR-19-CE31-0017  for project ' Secular evolution of galxies ' (SEGAL), and through grant  ANR-18-CE31-0006  for project ' Galactic Dark Matter' (GaDaMa ), the  Centre National de la Recherche Scientifique (CNRS)  and its  SNO Gaia of the Institut des Sciences de l'Univers (INSU) , its  Programmes Nationaux: Cosmologie et Galaxies (PNCG) ,  Gravitation Références Astronomie Métrologie (PNGRAM) ,  Planétologie (PNP) ,  Physique et Chimie du Milieu Interstellaire (PCMI) , and  Physique Stellaire (PNPS) , the ' Action Fédératrice Gaia' of the Observatoire de Paris , the  Région de Franche-Comté , the  Institut National Polytechnique (INP)  and the  Institut National de Physique nucléaire et de Physique des Particules  ( IN2P3 ) co-funded by  CNES ; the  German Aerospace Agency (Deutsches Zentrum für Luft-und Raumfahrt e.
Chemin); -the National Natural Science Foundation of China (NSFC) through grants 11573054, 11703065, and 12173069, the China Scholarship Council through grant 201806040200, and the Natural Science Foundation of Shanghai through grant 21ZR1474100; -the Tenure Track Pilot Programme of the Croatian Science Foundation and the École Polytechnique Fédérale de Lau-sanne and the project TTP-2018-07-1171 'Mining the Vari-able Sky', with the funds of the Croatian-Swiss Research Programme; -the Czech-Republic Ministry of Education, Youth, and Sports through grant LG 15010 and INTER-EXCELLENCE grant LTAUSA18093, and the Czech Space Office through ESA PECS contract 98058; -the Danish Ministry of Science; -the Estonian Ministry of Education and Research through grant IUT40-1; -the European Commission's Sixth Framework Programme through the European Leadership in Space Astrometry (ELSA) Marie Curie Research Training Network (MRTN-CT-2006-033481), through Marie Curie project PIOF-GA-2009-255267 (Space AsteroSeismology & RR Lyrae stars, SAS-RRL), and through a Marie Curie Transfer-of-Knowledge (ToK) fellowship (MTKD-CT-2004-014188); the European Commission's Seventh Framework Pro-gramme through grant FP7-606740 (FP7-SPACE-2013-1) -the French Centre National d'Études Spatiales (CNES), the icy Office (BELSPO); for the Gaia European Network for Improved data User Ser- -the software products TOPCAT, STIL, and STILTS vices (GENIUS) and through grant 264895 for the Gaia (Taylor et al.
Acknowledgements The  ESMRMB-GREC (Gadolinium Research and Education Committee)  is a group of multidisciplinary ESMRMB members, including clinicians, scientists, chemists, physicists, pathologists, and clinical epidemiologists who all share a common interest in the study of gadolinium-based contrast agents in a wide variety of clinical and preclinical conditions. The authors would like to thank all the members for their insights despite their specific competence and experience. The list of authors includes the members who developed the project and those experts who have revised and approved the final version of the manuscript.
Overall, these results clearly show defective granulopoiesis, similar to that observed for primary cells. To gain more insights into this defect, we investigated the transcriptomic profile of the patients' granulocytic progenitors: iPSC-derived granulocytic progenitors (CD43 + CD11b + CD14 -) were sorted and profiled by RNA sequencing. We found 24 genes significantly upregulated and seven significantly downregulated (P<1x10 -5 ) in patients' cells compared to controls (Figure  4E , Online Supplementary Table  S4 ). Gene set enrichment analysis performed with two different databases (KGEA and GOBP) revealed a significant enrichment in the JAK/STAT signaling pathway in patient-derived iPSC progenitors (Figure  4F ) with a tendency to increased granulocytic count and delayed myeloid development (Online Supplementary Figure  S6B ) confirming the results obtained with primary patients' cells and with shANK in cord blood-derived progenitors.
Acknowledgments The authors thank the patients for their participation in this study and  Prof. MC Alessi  who coordinates the  Centre de Référence des Pathologies Plaquettaires (France). The authors also thank  P Rameau ,  C Catelain , and  T Manoliu  from the  Imaging and Cytometry Platform PFIC, UMS AMMICA  ( Gustave Roussy Villejuif, France ) for their expertise in cytometry,  MK Diop  and  T Dayris  from the  Bioinformatics Platform, UMS AMMICA  ( Gustave Roussy Villejuif, France ) for RNA-sequencing analysis,  M Breckler  from the  Genomic Platform UMS AM-MICA  ( Gustave Roussy Villejuif, France ) for RNA-sequencing performed thanks to the  TA2016_P27_ALDO ,  S Peressini  from  Servizio Di Analisi Chimico-Cliniche, IRCCS Policlinico S. Matteo (Pavia, Italy)  for helping with the erythropoietin measurements, and  Unité de Thérapie Cellulaire, CRB-Banque de Sang de Cordon, Hôpital Saint-Louis, AP-HP (Paris, France)  for cord blood samples.
The objective of this chapter is to collect insights from different sources and different people about institutional features that may slow down economic development in Tanzania or threaten its sustainability and inclusiveness. It essentially follows three approaches, and these are presented in separate sections. First, by exploiting the numerous institutional indicators available in international databases, insights were collected about the quality of Tanzanian institutions in comparison with a set of relevant countries. Insights aim to identify those institutional features that may possibly differentiate Tanzania. Second, an original questionnaire survey was undertaken among various types of decision makers operating in Tanzania. The survey asked them about their own perception of how institutions worked there and how they affect development.
I Institutional Indicators: How 'Different' Is Tanzania among Developing Countries? The development community has long known that institutions matter for development, and several country-level indicators describing various aspects of institutions, especially those that have to do with governance, have developed over time. They are meant to facilitate cross-country comparisons and to correlate, in a rough way and most often on a cross-sectional basis, institutional or governance quality with growth or other development indicators. Many such international databases now exist. They either focus on a specific institutional area -democracy, corruption, ease of doing business -or cover a wide range of themes. The Worldwide Governance Indicators (WGI) provide synthetic indicators obtained from extracting from these datasets some common factors in pre-defined institutional areas.
1  Quantitative indicators reported in these cross-country datasets generally reflect expert opinion on some specific aspect of institutions in a country. They may not coincide with the way people within a country perceive them. This is the reason why this analysis of the specificity of Tanzania in the space of cross-country institutional indicators is extended to more specialised and more pragmatically oriented databases that are not included in the WGI. This is the case of the World Bank enterprise surveys that collect the opinion of firm managers or the African Barometer, which surveys the public on some more focused institutional issues. A How Different Is Tanzania Using the Synthetic WGI? Figure  3. 1 compares Tanzania with two sets of comparator countries and according to the six synthetic indicators present in the WGI database for 2018.
These outperforming peer countries are all in Asia: Bangladesh, Lao and Vietnam have gained between 60 and 150 per cent in GDP per capita over Tanzania since 1990, and Cambodia substantially less (30 per cent). The issue is whether these outperformers present institutional features significantly different from Tanzania, which might explain their better performance or be a consequence of faster growth. Before discussing the charts shown in synthetic indicator results from the combination of those individual indicators in the original datasets that belong to each institutional area being considered -corruption, regulation, rule of law, and so on. Synthetic indicators thus capture the common information in the underlying set of individual indicators; that is, how they differ across countries.