dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, we added to our research binary variable Legal certainty that takes value 0, if ICO is banned or ignored in a particular jurisdiction (legal uncertainty arises). As well, the value 1 in the case of proper regulations by applicable laws and/or guidelines explaining the legal status and obligations according to the token type (regulatory certainty). We did not consider as a certainty if the regulator's activity is limited only to issuing warnings. Or if some ICO market practices had been assessed as suspicious. The variable is based on the authors' expert judgment made on the data presented in the comparative reports by PricewaterhouseCoopers (2018) and EY (2017). Additionally, we take into consideration legal system origin by employing binary variables denoting particular system types.
Methods We built separate databases and used different methodological approaches in two strands of our research. First, we followed  De Jong et al. (2018)  and employed a logit model to investigate determinants of ICO success and a post-ICO performance of the business venture. In the estimation tables, we report odds ratios. Thus, the value of less than one indicates the negative relationship between the particular independent variable and our measure of ICO success. Contrary, the odd ratio bigger than one is associated with the positive relationship between the given independent variable and achieving the ICO funding success or post-ICO performance success by the given business project. In the first strand of the research, we focused on the factors that are related to the strategy of individual ICO.
HTS methods detect both the most abundant community members but also the rare species, which cannot otherwise be retrieved by traditional culture-dependent methods. The Global Ocean Sample Expedition (GOS 4  ) led by J. Craig Venter Institute is exemplary for that. Recently, the most important step forward in elucidating world-wide eukaryotic biodiversity were cross-oceanic expeditions such as Malaspina, Tara Oceans, and Biosope  (Grob et al. , 2007; Claustre et al. , 2008; Bork et al. , 2015; Duarte, 2015; de Vargas et al. , 2015). These studies have also confirmed that much of the eukaryotic plankton diversity in the euphotic zone is still unknown and has not been previously sequenced from cultured strains. The Tara Oceans dataset also provided significant new knowledge to protistan diversity, identifying many new rDNA sequences, both within known groups and forming new clades  (de Vargas et al.
, 2016; Paz et al. , 2018; Weigand et al. , 2019). Although metagenomic data generated from eDNA samples can provide vast amounts of new information, datasets from complex microbial communities are difficult to process. Metagenome assemblies and their functional annotations are challenging  (Dong and Strous, 2019). A large fraction (>50%) of the detected genes has no assigned function. The use of functional metagenomics applications and tools (including metagenomics coupled with bioactivity screening/enzyme screening, metatranscriptomics, meta-proteomics, meta-metabolomics), is key to solve this problem. One of the most used applications of functional metagenomics is the activity-based screening of metagenomics libraries. Enzyme discovery is currently the largest field of its use, including marine enzymes  (Hårdeman and Sjöling, 2007; Di Donato et al.
Being coupled with subsequent network analyses, bioinformatics and biostatistics, these tools would enable faster detection of new marine species and their biomolecules and faster adoption of molecular protocols. Typically, biodiversity data is generated from long-term monitoring campaigns or novel exploratory expeditions and are presented as lists of species presence and (relative) abundance. As these lists can be lengthy, their manual inspection is time-consuming and limits access to important pieces of information. Visualizing these data using network analysis tools can uncover important associations and relations between species in a specific location  (Orlando-Bonaca and Rotter, 2018; Mozetič et al. , 2019). Importantly, other traits can be included in the biodiversity datasets (i. e.
Importantly, any data provider should develop internal metadata guidelines that provide minimal information about the data stored that will enable their use in the future. In 2011, the European Commission (EC) launched a new Open Data Strategy for Europe after realizing that data generated by the public sectors are vast and although financed by the public funds, data are not always available and accessible. Hence, the EC highlighted the unused potential of public sector data to spur innovation, economic growth, and answer to societal challenges such as food security and healthcare 8. As a result, Europe now hosts at least two infrastructure initiatives, publicly hosting many marine microbial strains: MIRRI (Microbial resource research infrastructure 9 ) and EMBRC (the European marine biological resource center 10 ).
MIRRI is an infrastructure initiative for preserving microbes that can be cultured for future exploitation and providing coordinated actions to facilitate access to genetic resources -data, microbial strains, and expertise. EMBRC and LIFEWATCH (European Infrastructure Consortium 11 ) connect marine stations that provide direct access to the sea, marine resources, and different services. A review of the past and current European initiatives is provided in the Supplementary Table  1  within  Rotter et al. (2020a). Also useful for marine biotechnology development are databases of chemical structure: ChemSpider, databases of carbohydrate structures and other glycobiologyrelated fields (e. g. , Glycosmos, Glycomics at Expasy, GlyGen, and CarboMet), MarinLit, a database of marine natural products literature that has been active since the 1970s, CyanoMetDB, a comprehensive database dedicated to cyanobacterial secondary metabolites  (Jones et al.
, 2020) , and the newly developed Natural Products Atlas, an open access database for microbial natural products discovery  (van Santen et al. , 2019). These databases (Table  2 ) play an important role in facilitating track and trace of the specimen and the chemical compounds they synthesize, thus enabling a rapid dereplication  (Blunt et al. , 2012; Collins, 2019). Importantly, the policy making and scientific community are supporting the public availability of all information and materials from these databases to avoid the fragmentation of expertise, unbalanced database access, and the estimated $10 billion annual losses due to incorrect reference material  (Collins, 2019).
The Chemical Inventory There is a wide range of marine natural products, from relatively small primary and secondary metabolites, with molecular weight usually lower than 1,500 Da, which comprise around 34,000 compounds according to the latest version of the marine natural products database MarinLit (Table  2 ) with a wide range of biological activities, to macromolecules such as enzymes and polysaccharides. Proteins, carbohydrates, and lipids from marine organisms are crucial in our diet but can also be involved in the development of novel processes for the production of higher value-added products. A wide range of applications has been described for marine enzymes in the food industry and human health, and marine polysaccharides have also found multiple biomedical and tissue engineering uses  (Fernandes, 2014; Eswara Rao et al.
The remarkable advances in analytical instrumentation along with the development of suitable databases have enabled the 11 https://www. lifewatch. eu/ (Li et al. , 2018). Furthermore, biological activities can be mapped into such networks, facilitating the rapid discovery of (new) compounds that are responsible for bioactivity  (Fan et al. , 2019). Open-access knowledge bases containing tandem mass (MS/MS) spectrometry data such as the Global Natural Products Social Molecular Networking (GNPS, Table  2 ) or structures of microbial natural products (The Natural Products Atlas, Table  2 ) have been greatly enhancing the efficiency of dereplication processes leading to the identification of new molecules and natural product scaffolds  (Wang et al. , 2016). Small Molecule Accurate Recognition Technology (SMART), using NMR data, constitutes a step forward in the automatic identification or classification of new natural products, especially when combined with GNPS.
Metabolite identification based on high resolution -HR-MS/MS and NMR along with freely accessible and commercial databases was reviewed by  Wolfender et al. (2019). The next steps are: (i) the use of ZINC, Reaxys, ChEMBL, PubChem or StreptomeDB 3. 0 databases, and/or NMR databases for virtual bioactivity screening (Table  2 ). These use either structure-based or ligand-based computational approaches to predict quantitative structure-activity relationship (QSAR), based on active and inactive known molecule, or to predict binding sites based on the chemical similarity to known ligands to identify protein targets of bioactive molecules  (Pereira et al. , 2014 (Pereira et al. , , 2015;; Sterling and Irwin, 2015; Cockroft et al. , 2019; Cruz et al. , 2019; Dias et al. , 2019). Additionally, (ii) the use genomic data in combination with molecular networks enables dereplication also at the frontier between chemistry and biology (van der Hooft et al.
The European Green Deal [COM(2019), 640] is an EU growth strategy to transition into a prosperous society with a resource-efficient and competitive economy. National funding mechanisms can sometimes provide partial financing through research programs and national projects. At the European scale, Horizon2020 and its successor, Horizon Europe framework programs are the ones directly funded by the EU budget. The marine biotechnology sector significantly benefits from the Horizon funding mechanism, intending to directly address societal challenges and promote the development of innovative societies through international cooperation and collaboration of academic and industrial partners. Some financing opportunities have limited country participation depending on the governmental organizations (national ministries) that typically endorse the respective national participation.
MMe: thanks the Canceropôle Provence-Alpes-Côte d'Azur, and the Provence-Alpes-Côte d'Azur Region for the financial support provided to the MetaboCell project. DO: supported by the Doctorate Study program in Ecology and Environmental Sciences, Marine Research Institute, Klaipėda University, Lithuania. CR: we gratefully acknowledge the Research Council of Norway, the Møre and Romsdal County Council and Møreforsking AS for their financial contributions through the PROMAC (244244; www. promac. no), the Norwegian Seaweed Biorefinery Platform (294946; http://seaweedplatform. no/), and the Blå-Grønn (55031) projects. ER: this work benefited from financial support from the PACA Canceropôle, the National Cancer Institute, the PACA Regional Council and the French Government, managed by the National Research Agency as part of the Université Côte d'Azur JEDI Investissement d'Avenir project (ANR-15-IDEX-01).
JS: work was supported by the Slovenian Research Agency (P4-0127 and J4-1771). IS: financial support from Ministry of Education, Youth and Sports of the Czech Republic (project CZ. 02. 1. 01/0. 0/0. 0/17_048/0007323). XT: the tool "RRI Roadmap" was developed as part of the European Horizon 2020 project MARINA "Marine Knowledge Sharing Platform for Federating Responsible Research and Innovation Communities" under the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement.
with financial assistance of the  Interreg MED Programme , cofinanced by the  European Regional Development Fund  (Project No. 7032 , internal ref. 8MED20_4. 1_SP_001 )-B- Blue  project. AB: acknowledges the support from the  Research Council of Norway  through the grant  267474  from the  HAVBRUK2 program. MLC: acknowledges the  Portuguese Foundation for Science and Technology  ( UIDB/04326/2020 ), the  European Maritime and Fisheries Fund  ( MAR2020 OSTEOMAR/16-02-01-FMP-0057  and  ALGASOLE/16. 02. 01-FMP-0058 ), the  European Regional Development Fund (Atlantic Area   BLUEHUMAN/EAPA/151/2016  and  INTERREG V-A Spain-Portugal ALGARED+) , and the  European Commision  ( H2020-MSCA-ITN BIOMEDAQU/766347 ). MFC: wishes to acknowledge the funding from  CEEC program  supported by  FCT/MCTES  ( CEECIND/02968/2017 );  ACTINODEEPSEA  project ( POCI-01-0145-FEDER-031045 ) co-financed by  COMPETE   2020 , Portugal 2020, ERDF and FCT; Strategic Funding  UIDB/04423/2020  and  UIDP/04423/2020  through national funds provided by  FCT  and  ERDF.
MC: financial support from the  Programme of the Institute of Oceanology, PAS  (grant no. II. 3) and  National Science Centre in Poland  (project number  NCN 2016/21/B/NZ9/02304 ). MCu: acknowledges the funding from the  Ministerio de Ciencia e Innovación of Spain  ( SAF2009-0839  and  RTA 2015-00010-C03-02 ) and  INTERREG-MAC2/1. 1b/279(AHIDAGRO). AD-M: acknowledge financial support from  INTERREG-MAC/1. 1b/042 ( BIOTRANSFER2 ) and  Agustín de Betancourt Programme  ( Cabildo de Tenerife and Universidad de La Laguna ). AD: work has been supported by the  ERDF Activity 1. 1. 1. 2 " Post-doctoral Research Aid" of the Specific Aid Objective 1. 1. 1,Operational Programme "Growth and Employment " (No. 1. 1. 1. 2/VIAA/1/16/048 ). RJF: funding for this research was provided under the  Marine Research Programme 2014-2020 , through the  Marine Institute of Ireland  under grant  PBA/MB/16/01  " A National Marine Biodiscovery Laboratory of Ireland (NMBLI) " and through the  Food Institutional Research Measure , administered by the  Department of Agriculture, Food , and the  Marine, Ireland  under grant issue  17/F/260  (MaraBioActive).
SG: this work was supported by the  Applied Molecular Biosciences Unit-UCIBIO  which is financed by national funds from  FCT/MCTES  ( UID/Multi/04378/2019 ). SG thanks financial support provided by  FCT/MCTES  through grant  IF/00700/2014  and  OceanTresaures  project  PTDC/QUIQUI/119116/2010. NID: wishes to acknowledge the funding from the  Croatian Science Foundation  Project  CELLSTRESS  ( IP-2018-01-5840 ). MMa and TD: we wish to acknowledge funding from the  General Secretariat for Research and Technology (GSRT)  and the  Hellenic Foundation for Research and Innovation (HFRI)  under grant no. 239  ( SPINAQUA  project). AM-G: acknowledges the financial contribution from the project  BYTHOS  funded by the  European Union 's  Interreg V-A Italia-Malta Programme  under project code  C1-1.
1-9. HM-M: financial support from  National Science Centre in Poland   2016/21/B/NZ9/02304  and  2017/25/B/NZ9/00202. MMe: this work has been supported by the  French Government , through the  UCAJEDI Investments  in the  Future  project managed by the  National Research Agency (ANR)  with the reference number  ANR-15-IDEX-01. ACKNOWLEDGMENTS This publication is based upon work from  COST Action   CA18238  ( Ocean4Biotech ), supported by  COST (European Cooperation in Science and Technology) program.
europa. eu/ Programme info/horizon-europe- (Horizon H2020, next-research-and- Horizon Europe) innovation-framework- programme_en Funding Bio-based EU https: Industries Joint //www. bbi-europe. eu/ Undertaking (BBI-JU) Funding LIFE (L'Instrument EU https://ec. europa. eu/ Financier pour easme/en/life l'Environnement) Funding ERA-Net Cofound Limited country https://ec. europa. eu/ participation programmes/ horizon2020/en/h2020- section/era-net Funding Interreg, Interreg Regional https://interreg. eu/; Med, Interreg https://interreg-med. eu/; Baltic, Interreg https://www. interreg- Atlantic, Interreg baltic. eu/home. html; Mac https: //www. atlanticarea. eu/; https: //www. mac-interreg. org/ Funding Joint Programming Limited country https: Initiative Healthy participation //www. jpi-oceans. eu/ and Productive Seas and Oceans (JPI Oceans) Funding National, Bilateral Limited to single country or bordering countries.
Experimental Section Human Pituitary Tissue Samples: Pituitary tissue samples were obtained from the University Hospital Grenoble, Department of Neurosurgery. The human biopsies were residues from other research projects. As such, the French Public Health Code does not require compulsory formality, but recommend subjects information and consent. Informed written consent was obtained from the patients or their families. A total of eight pituitary tissue samples were collected from the operating room during trans-sphenoidal surgery of six patients and were immediately frozen at À80 °C. Pathologic diagnoses rendered based on routine histology, hormone immunostaining are shown in Table  1. IT-AFM and Data Analysis: Frozen surgical specimen were cut in thick slices (150 μm in thickness) using a cryostat.
The indentation points were organized in maps of 90 Â 90 μm 2. About 2-9 maps randomly distributed on the surface of the tissue section were acquired for each specimen. The fitted Young's moduli showed unimodal peaked distributions. We characterized the distribution on every specimen by calculating the median Young's modulus and the dispersion around the median. Dispersion around the median value was calculated from the 0. 17 and 0. 83 percentiles, which enclose 66% of the values. This interval was considered as a relevant indicator of the dispersion of the data for these non-normal distributions as it encloses a similar amount of data as the standard deviation in normal distributions. [29] Histograms of the distribution of the Young's moduli were fitted with log normal probability density function to guide the eye on the figures.
The existence of diagnostic features in the visible and infrared regions makes it possible to use reflectance spectra not only to identify mineral assemblages but also for calibration and classification of satellite images, considering lithological and/or mineral mapping. For this purpose, a consistent spectral library with the target spectra of minerals and rocks is needed. Currently, there is big market pressure for raw materials including lithium (Li) that has driven new satellite image applications for Li exploration. However, there are no reference spectra for petalite (a Li mineral) in large, open spectral datasets. In this work, a spectral library was built exclusively dedicated to Li minerals and Li pegmatite exploration through satellite remote sensing. The database includes field and laboratory spectra collected in the Fregeneda-Almendra region (Spain-Portugal) from (i) distinct Li minerals (spodumene, petalite, lepidolite); (ii) several Li pegmatites and other outcropping lithologies to allow satellite-based lithological mapping; (iii) areas previously misclassified as Li pegmatites using machine learning algorithms to allow comparisons between these regions and the target areas.
Reference spectra for some of the most important Li minerals, such as spodumene and lepidolite, can be found in the United States Geological Survey (USGS)  [15]  and ECOSTRESS  [16]  spectral libraries. Nonetheless, there are no reference spectra for petalite in these open domain spectral libraries. The Geological Survey of Brazil (CPRM) has been trying to address this issue by compiling a spectral database with a Li minerals' dedicated section, but so far the petalite diagnostic features have not been identified  [17]. To fill in this gap and to complement the development of image classification algorithms for Li exploration, a spectral database was built in this work based on samples collected in the Fregeneda-Almendra aplite-pegmatite field (Figure  1 ). In this region, spodumene, petalite, and lepidolite minerals occur in evolved pegmatites  [18, 19]  that intruded metasedimentary rocks belonging to the "Complexo Xisto-Grauváquico" (CXG)  [20].
Complementary data such as (i) the sample location and coordinates (when available), (ii) degree of alteration of the sample, (iii) sample color, (iv) type of face measured, The spectral library is composed of field and laboratory spectra not only of Li minerals (spodumene, petalite, lepidolite) but also from the main outcropping lithologies of the Fregeneda-Almendra area (granitoid rocks, CXG metasediments, Li pegmatite). These rock spectra were mainly acquired in areas of good exposition so they could be used as training areas for satellite-based lithological mapping. Additionally, to allow further investigation on the ability to discriminate Li minerals and Li pegmatites from other lithologies, the spectra from areas misclassified as Li pegmatites using machine learning algorithms  [13]  are also provided.
The comparison of the aforementioned data can allow users to evaluate the degree of spectral similarity between the target minerals/rocks and the remaining within-scene elements. Complementary data such as (i) the sample location and coordinates (when available), (ii) degree of alteration of the sample, (iii) sample color, (iv) type of face measured, and (v) equipment used, are provided for each spectrum. Spectra acquisition and curation are described thoroughly. For the laboratory spectra, also available are (i) sample photographs, Data 2021, 6, 33 3 of 10 (ii) respective continuum removed spectra files, and (iii) details on the main absorption features automatically extracted. This spectral database was established in the ambit of the "Lightweight Integrated Ground and Airborne Hyperspectral Topological Solution" (LIGHTS) project, whose goal is to develop a tool that combines remote sensing data acquired at different scales with geological and geochemical data to rapidly identify target areas for Li exploration  [21, 22].
However, such a database could be useful for other ongoing research projects, namely: (i) the fiber laser plasma spectroscopy system for real-time element analysis (FLaPsys) project, which aims at developing an advanced spectroscopy system capable of real-time element identification and quantification mainly applied to Li mineralizations  [23] , and in which laser-induced breakdown spectroscopy (LIBS) will be correlated with the visible and infrared data acquired; (ii) new exploration tools for a European pegmatite greentech resources (GREENPEG) project which aims at improving responsible exploration in Europe for pegmatites through the development of integrated, multi-method exploration toolsets that include satellite image processing and airborne and ground-based geophysics and geochemical approaches  [24].
Part of this spectral database was already the basis for some publications  [25, 26] , but more works are expected in the future since there is great potential in this dataset whose main advantages include: (i) that the data are provided in a universal text file format that is not dependent on software; (ii) the ability to compare field and laboratory spectra for 52 coincident spots; (iii) finally, the details and statistics of the extracted features provided allow the user to compare the shape, asymmetry, and depth of the absorption features of the distinct Li minerals, including petalite.
Data Description The spectral database is divided into two subsets; the first concerning the spectra collected in the laboratory and the second corresponding to the spectra acquired in the field. The spectra were organized into categories within each subset according to their application purpose (Table  1 ). To allow a rapid and easy identification of the spectra, the spectrum naming was made considering logical codes embedded in the spectrum title (Table  1 ). Consequently, information such as the location of the sample and analyzed lithology/mineralogy can be readily extracted based just on the spectrum name. Besides the codes of Table  1 , each spectrum has its associated measurement number. All spectra are provided in a universal UTF-8 text file format that can be read in any proprietary or open-source software, representing measurements made in the visible and near-infrared (VNIR) and shortwave infrared (SWIR) regions.