dataesr/scientific-paragraphs-categorization huggingface dataset:
Nature, 415:  626-630. DOI: https://doi. org/10. 1038/415626aGurney ,  KR, Razlivanov, I, Song, Y, Zhou, Y, Benes, B, Abdul-Massih , M 2012 Quantification of fossil fuel CO 2 emissions at the building/street scale for a large  US city. Environmental Science & Technology, 120815073657007. DOI: https://doi. org/10. 1021/es3011282 Homer,  CG, Dewitz, JA, Yang, L, Jin, S, Danielson, P, Xian, G, Coulston, J, Herold, ND, Wickham, JD and Megown , K 2015 Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and  Remote Sensing ,  81 (5):  345-354. Kaimal,  JC, Wyngaard, JC, Haugen, DA, Caughey , SJ and Readings, CJ 1976 Turbulence structure in the convective boundary layer. J.
What are blockchains and how are they relevant to governance in the global political economy? Malcolm  Campbell-Verduyn  Imagine having almost instantaneous access to a permanent record of all digital transactions undertaken across the world. Without revealing precisely who and what is involved in these transactions, this digital database grants you nearly real-time overviews of peer-to-peer exchange within and across national borders. Such unprecedented capacity to monitor direct Internet-based interaction between quasi-anonymous individuals who undertake, verify, and publish records of their digital transactions is at the core of promises and fears surrounding blockchains. This volume explores governance implications for the actors and processes involved in ordering, managing, and organising an increasingly digital global political economy arising from growing applications of this set of emergent technologies to Bitcoin and beyond.
At their essence, blockchains are digital sequences of numbers coded into computer software that permit the secure exchange, recording, and broadcasting of transactions between individual users operating anywhere in the world with Internet access. Like most technological changes, the development of blockchains drew on and combined several existing technologies. Blockchains incorporate digital encryption technologies that mask, to varying degrees, the specific content exchanged as well as the identities of individual users. Algorithms, precoded series of step-by-step instructions, are also mobilised in solving complex mathematical equations and arriving at a consensus on the validity of transactions within networks of users. Time-stamping technologies then periodically bundle verified transactions into datasets, or 'blocks'.
Linked together sequentially, these 'blocks' form 'chains' that make up larger 'blockchain' databases of transactions that broadcast a permanent record of transactions whilst maintaining the anonymity of users and specific content exchanged. Blockchains are intended to be maintained by all users in manners meant to be immutable, unless users arrive at a clear consensus to undertake changes. Ledgers of user-verified transactions were envisioned by the science fiction writer H. G. Wells (2005)  in the 1930s and advocated by 'cypherpunk' computer hackers seeking to ensure digital privacy as the Internet began evolving later in the twentieth century  (Jeong, 2013). The technical blueprint for developing blockchain technology was originally proposed in a white paper published by one Satoshi Nakamoto in 2008.
In sum, there are three governance design features at the blockchain level associated with more centralization: the use of PoS, the use of pre-mining, and the use of nomination. The protocol level. Developers who specialize in programming blockchain applications constitute the key stakeholder at the protocol level as they are the people who "write the rulebook"  (Narayanan et al. , 2016: 173). For most cryptocurrencies including Bitcoin, developers work on a voluntary basis and are not hired or funded by any centralized organization. The code that they work on is typically open source, meaning that any developer can contribute to the code using online repositories such as Github. com (which acts as the Wikipedia of software development). Still, a small group of very dedicated "core" developers can be formed and governance decisions may thus become more centralized.
Sampling We collected data on Bitcoin (BTC), Litecoin (LTC), Peercoin (PPC), Ripple (XRP), and Stellar (XLM). These cryptocurrencies vary in terms of their technology, applications, and most importantly, degree of decentralization. Bitcoin is included in our sample because it represents the first and most established cryptocurrency. At the organizational level, Bitcoin is not centrally funded, and its core developers are not employed by the Bitcoin Foundation or other centralized entities associated with Bitcoin. Building on proof-of-work (PoW) consensus mechanism, Bitcoin distributes governance among miners who allocate external resource to mining  (Narayanan et al. , 2016: ch7. 3). At the protocol level, miners can also have influence over code changes through formal voting mechanisms such as the Bitcoin Improvement Proposals (BIPs), wherein proposals for code improvement are being reviewed, published, and voted for (or against) by miners (for more details, see https://github.
Data We acquired panel data from CoinGecko. com, a leading scholarly project that collects daily and weekly data at both the market and community level  (Wang and Vergne, 2017; Ong et al. , 2015). CoinGecko. com is an independent data source compiled by two cryptocurrency academic researchers, Bobby Ong and Teik Ming Lee, at Singapore Management University's Sim Kee Boon Institute for Financial Economics. The CoinGecko database consists of information extracted and scraped from cryptocurrency specialist websites (such as Coinmarketcap. com), code repositories (e. g. , Github. com), community forums (such as Reddit. com), social media (e. g. , Twitter and Facebook), and search engines (e. g. , Bing or Alexa). In addition to information on price, volume, and market capitalization, CoinGecko.
com also contains indexes measuring liquidity, protocol development, community activities, and public interests using composite scores. This database has been drawn upon in other scholarly analyses, including the book chapter co-authored by the two founders, "Evaluating the potential of alternative cryptocurrencies"  (Ong et al. , 2015)  in The Handbook of Digital Currency  (Lee, 2015). We complemented CoinGecko data with weekly data from Factiva for media coverage, and manually coded several governance indicators such as the association with fraud, hacks, and Ponzi schemes. We did not make a distinction between the financial and the generalist media. Instead, we focused on mainstream media that follows closely the blockchain industry, such as the Financial Times, the Wall Street Journal, and The Economist as they are generally more objective and neutral in commenting on the disruptive nature of cryptocurrencies and blockchains than pro-industry sources, who tend to act as cheerleaders.
Observation period We study the five cryptocurrencies on a weekly basis between September 2014 and August 2015 (when we first began to collect the data). This is a period when the cryptocurrency market has become more established and stabilized in contrast to the earlier period in which only a handful of cryptocurrencies existed. More variations in cryptocurrencies were present in this period, with  Source for graphs of permission-less vs. permissioned blockchains: www. linkedin. com/pulse/ making-blockchain-safe-government-merged-mining-chains-tori-adams. the introduction of Stellar at the start of our observation period. All cryptocurrencies in our sample existed throughout the period, forming a balanced panel data for analysis. The final dataset contains weekly data for 255 observations.
Blockchain technologies promise solutions to the growing problems of public distrust with traditional financial institutions and transactions. In the blockchain-empowered system, where the traditional coupling of fiat with digital forms of bank credits and debits has been replaced by cryptocurrency, the database of transactions is shared by a network of autonomous computers and enables a novel and transparent approach to recording transactions and verifying information -a "trustless" and decentralized transaction system. Historical changes in trust-bearing and ledger-keeping institutions -from barter to digital finance -have shown us how systems of trust redefine tokens of money as mediums of exchange as well as stores of value  (Casey and Dahan, 2016). 13 The novel decentralized trust protocols bring about changes not only in ledgers and record-keeping, but also in contract -the two "central legal devices of the modernity" (DuPont and  Maurer, 2015: 4).
15 www. orrick. com/Insights/2016/12/National-Bank-Charters-for-Fintech-Companies 16 Please see the chapter by Campbell-Verduyn and Goguen in this volume for more details about the impact of the global anti-money-laundering regime on blockchain applications. 17 www. coindesk. com/philippines-just-released-new-rules-bitcoin-exchanges/ 18 https://medium. com/cryptonight/bitcoin-doesn-t-make-remittances-cheaper-eb5f437 849fe#. shqxjn4d3 19 www. cash2vn. com/ 20 http://asia. nikkei. com/Politics-Economy/Economy/Asia-has-all-the-ingredients-for-afintech-revolution 21 http://fintechnews. sg/5978/philippines/top-24-fintech-startups-forbes-philippines/ 22 The World Bank Send Money Africa database. 23 www. forbes. com/sites/laurashin/2016/06/15/elizabeth-rossiello-describes-how-bitpesaslashes-international-payment-fees/#105259c6475d 24 www.
Thisstudyreliesonaqualitativeexplorationofacorpusofpressarticlesand expert contributions, having Bitcoin as their core subject from its birth to the present day. For each of the cases, we "unwrap" and decode several (n=6-8) writtenpieces-mainlyspecializedpressandwebarticles,butalsotechnicaldocuments and forum excerpts -concerning the controversial episodes. Through the chapter, we attempt to highlight how these different data and the particular ways in whichcontroversiesarenarratedandunderstoodcontributetodefiningtheworld they address and, thereby, propose specific forms of governance. In conclusion, thechapterwillproposeageneralreflectionontheissueofblockchaintechnology governance,thankstothe"already-historical"experienceofBitcoin.
Albeitsuccinctand impersonal,theformerclarifiesthatthecauseoftheproblemwasthecreation, mining, and broadcasting of a "large block […] that is incompatible with earlierversionsofBitcoin. "Meanwhilethelatterarguesthattheissuewiththe block being was that it displayed a "larger number of total transaction inputs thanpreviouslyseen. "Inasubsequentanalysisoftheincident,VitalikButerin 5 furtherclarifiesthattheproblemwaslinkedtothereleaseofthemostrecent version of "bitcoind," the most popular implementation tool/interface for running Bitcoin used by miners. Developers of this release, labeled 0. 8, switched the database that bitcoind used to store blocks and transactions from one called BerkeleyDB to another called LevelDB, considered as more efficientandmoresuitabletoreduceblockchainsynchronizationtime.
However, Buterinexplains: what the developers did not realize at the time was that by doing so they alsoaccidentallyintroducedachangetotherulesoftheBitcoinprotocol. In order to make an update to the database, the database process must make a 'lock' on the part of the database which stores that particular item of information, a mechanism implemented to prevent two changes from occurringsimultaneously. LevelDBhadnorestrictionsonthepossiblenumberoflocks,butBerkeleyDB had,andfailedwhenasingleblockrequiredmorelocksthanthislimit. NodesrunningthemostrecentversionoftheBitcoinsoftware,0. 8,wereable to manage this large block, but some nodes running previous versions of the softwarerejectedit. Theunintendedforkoftheblockchain,welearn,wasdueto thefactthatatthetimeoftheincident,thenodesrunningpre-0. 8softwareversions had more sheer computing power than those running the newest version, thusautomaticallygainingpre-eminence: Thepre-0.
" Third and last, this event underlines the materiality of Bitcoin's infrastructure asagovernancetool. Withitscomplexecosystemofintermediaries,mediations, and control points, this "governance by infrastructure" is evident in both the cause of the incident (a change in the type of database used to store results has unintendedconsequencesfortheorganizationofthecommunityandthevalueof the currency) and its solution (an actor that is "powerful" in terms of computing power is capable of single-handedly bringing the controversy to a stabilized point,ifnottoanend).
Acknowledgments We would like to thank  Malcolm Campbell-Verduyn  for his insightful comments and his invaluable work putting this volume together. We would also like to thank the participants of the " Bitcoin and Beyond " workshop at the  Balsillie School of International Affairs  for their comments and discussion on an earlier draft. This chapter benefited from a  EURIAS fellowship  at the  Paris Institute for Advanced Studies (France) , co-funded by  Marie Skłodowska-Curie Actions , under the European Union's  7th Framework Programme  for research, and from funding from the  French State  managed by the  Agence Nationale de la Recherche ,  programme "Investissements d'avenir"  ( ANR-11-LABX-0027-01 Labex RFIEA+ ).
Acknowledgments The authors of this chapter would like to thank the volume editor  Malcolm Campbell-Verduyn  for his valuable advice and insights, and the participants of the " Bitcoin and Beyond" workshop (Balsillie School of International Affairs , February 27, 2017) for providing a stimulating venue for discussions that inspired the revisions of this chapter. We also thank participants of the panel discussion on Digital Remittances in Crisis-Affected Contexts at the International Monetary Fund in October 2015, where earlier versions of the chapter material were presented.
Notes Acknowledgments From 2010 to 2014, all three authors were supported by the  French National Agency for Research (Agence Nationale de la Recherche, ANR) , CONTINTprogramme , ANR-10-CORD-004 -ADAM-Architectures distribuées et applica tions multimédias. Francesca Musiani  is currently supported by the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  ( H2020- ICT-2015 ,  ICT-10-2015 ) under grant agreement number  688722 -NEXTLEAP.
Acknowledgments Funding for this research was provided by the  University College Dublin Centre for Innovation, Technology, and Organization. I hold a small amount of cryptocurrency for research purposes, including Ethereum and (previously) The DAO tokens. Thanks to  Gianluca Miscione ,  Paul Ennis ,  Donncha Kavanagh , and  Bill Maurer  for commentary and guidance. Thanks for Stephan Tual and  Christoph Jentzsch  for commentary on an early draft. Initial qualitative research on The DAO was also conducted by  Melissa K. Wrapp  and shared with me, providing an invaluable aid in the early stages of this research. Malcolm Campbell-Verduyn  provided welcome editorial assistance.
Introduction The large-scale simulation of bioreactors is currently a challenging issue. Such simulations must account for both (i) the (multiphase) hydrodynamics and (ii) the metabolic behaviour of the biological population carried by the fluid. The first can be achieved through the use of widespread CFD softwares which require significant computational power. The second can be addressed with advanced cell models which result from community efforts to integrate genome-scale reconstructions of a strain metabolic network and depict thousands of intracellular reactions and metabolite con-centrations. Examples are the iJO1366 model for Escherichia coli  (Orth et al. , 2011)  and the consensus YEAST model for Saccharomyces cerevisiae  (Heavner et al. , 2012; Heavner et al. , 2013). These models describe state of the art knowledge of a cell metabolism, however their implementations require to solve either cumbersome optimization problems to access a steadystate cell-functioning, or to solve dynamically the metabolite concentrations in a cell that experiences exogeneous perturbations.
Simulation software All following simulations are performed using ADENON, a userfriendly simulation software we developed using the environment provided by MATLAB R2016a. This software is mainly focused on the simulation of bioreactors, by applying our PBM/Metabolic biological models within a hydrodynamic framework (compartment models, plug-flow reactors, batch or fedbatch cultures as well as accelerostat cultures). Population balances can be solved using either class or moment methods, with all core routines -for moment quadrature or distribution reconstruction-built into this software. Following the case configuration provided by the user, this tool formulates the corresponding ODE in terms of mass and volume balances. This set of ODE is then solved using an explicit scheme for time integration, either the Runge-Kutta 2,3 pair of  Bogacki and Shampine (1989)  or a simple first-order Euler scheme.
The templates are generic and could be used to process different matrixes, compound treatments and fluorescent wavelength readings. Step 3: Import normalized values to Graphpad Prism (or similar) for visualization of the growth curves. Step 4: Several statistical tests/procedures were used for an analysis of the growth curves. *Test for normality using the Shapiro-Wilks test. *Non-parametric analyzes were done with the Mann-Whitney-U-Test, parametric with the t-test. Multiple groups were compared using a one-way ANOVA using the Tukey post-hoc test. EXCEL files containing exemplary datasets for 2D and 3D models have been uploaded to Figshare (see data records section). Cell proliferation staining of 2D and 3D matrix embedded cell cultures. Cells were stained with Click-iT EdU Alexa Fluor 647 HCS Assay (2 plate kit, Life Technologies C10356) and Hoechst 33342 (Invitrogen, H3570).
-Spheroid size: Larger spheroids (>100 μm) are subject to light attenuation due to light scattering inside the cell mass, therefore preventing proper imaging of the inner and the deeper parts of the spheroids. Images of the spheroid cultures may be analyzed using software algorithms developed in MATLAB for this purpose  10. The pipeline to analyze Alginate-BR and matrix embedded (co-) cultures is illustrated in Fig. 2. For each of the 3D culture methods an example workflow, images and analysis parameters are provided at Figshare, along with a description and an overview table. 3D Floater cultures. Floater cultures contain a single, relatively large and almost spherical spheroid per well with area sizes of up to 0. 5 mm 2. Signal attenuation within these large spheroids cannot be addressed post-imaging, but solutions to improve light penetration proved unsuccessful in our hands  5.
A pragmatic alternative is to apply 2D projected spheroid size and shape analyzes. However, analysis of EdU positive cells, or full 3D analysis appear to be restricted to smaller spheroids for the time being. 2D size and shape analyzes are carried out in an automated fashion using the algorithm provided on (Data Citation 1) and entitled 'MATLAB script for the image analysis of 3D image stacks of 3D multicellular spheroid cultures'. Step 1: Place plate under microscope. Step 2: Acquire wide-field images *When spheroids are large and analysis of EdU positive cells is not feasible because of signal attenuation, wide-field microscope images are preferred over confocal images. Otherwise, when spheroids are small (below 200 micron), confocal image stacks should be taken, and analysis protocols should be followed as described for the alginate BR.
The limited imaging depth of the objective, however, restricts image acquisition to approximately half of the spheroid. Image acquisition was always initiated from the bottom of the spheroid. *Image acquisition for proliferation analysis: The 20x magnification objective allowed a visualization of EdU positive cells in the inner parts of the spheroids while still covering whole spheroids in the xy-plane. The z-step is set to 1 μm, to resolve the EdU positive cells. To allow correlation-studies between size, shape and proliferation, all spheroids of the field acquired from the lower magnification (10x) were also imaged with the 20x as sub-images. Step 3: Analysis: Size and 2D shape analysis may be analyzed by the automated analysis algorithm and parameters provided on (Data Citation 1) and entitled 'MATLAB script for the image analysis of 3D image stacks of 3D multi-cellular spheroid cultures'.
If spheroids contain a large percentage of positive cells, objectives or microscopy set-ups with a smaller axial resolution would have to be used. *QC checks: Homogeneous distribution, density of the spheroids in the culture (see also technical validation), no cell growth on the bottom. Morphology of the spheroids and consequences for the analysis algorithms (e. g. , is the assumption that spheroids are round or oval correct?) Shrinkage or folding/tearing of the matrix. Step 3: Analysis: Spheroids may be analyzed using the automated analysis algorithm and parameters provided on (Data Citation 1) and entitled 'MATLAB script for the image analysis of 3D image stacks of 3D multi-cellular spheroid cultures'. *Input parameters for the automated spheroid segmentation have to be adjusted according to the expected spheroid shape-spherical to invasive morphology (see also the images on (Data Citation 1) where example images for each of this types are provided).
Image analysis of fluorescence-labeled 3D cultures The following section describes the 3D analysis procedures used for the 3D analysis of the 3D fluorescent spheroid cultures used in Ref. 5. The main tools used for the analysis are: -FIJI (ImageJ): Used to outline manual ROI's of spheroids (using the RoiManager), -MATLAB: 3D spheroid segmentation, shape analysis and EdU positive cell detection, -CellProfiler: 2D spheroid detection and 2D shape analysis, -R & RStudio: Data analysis of the output of the above two. We also used ZEN 2. 0 LIGHT for the conversion of czi formatted raw image data to tif-stacks. Below we try to provide a workflow for each of the different fluorescent 3D cultures. The raw image(s) have been uploaded to (Data Citation 1), and the metadata corresponding to the file names of the images is documented in the samples overview table 'sample_overview_table.
xlsx' which we will refer further to as the 'samples overview table'. The raw images are either 3D multi-channel stacks taken with a confocal microscope, or 2D (multi-)channel widefield images. The image analysis of the 3D confocal stacks is mostly done using the algorithm provided at https://github. com/mbarbie1/ellipsoids-analysis-paper, while 2D widefield images are analyzed using CellProfiler pipelines. Both are uploaded to (Data Citation 1) as 'ellipsoids-analysis-paper-master. zip' (entitled: 'MATLAB script for the image analysis of 3D image stacks of 3D multi-cellular spheroid cultures') and 'Widefield_bioreactor_breast (lung, prostate)_gfp. cpproj' (entitled: 'Example CellProfiler pipeline for the image analysis of the prostate cancer cell floater cultures'). The data output from the MATLAB script 'ellipsoids-analysis-paper-master.
zip' contains 3 tables: msrSpheroids. csv, msrSpots. csv and summary. csv, and is described in the file 'output_data_ellipsoid_a-nalysis_description. xlsx' which is also uploaded to (Data Citation 1) and entitled: 'MATLAB script: overview of the output'. To use the CellProfiler pipelines, the location of the image files inside the CellProfiler pipeline should be adapted to one's own needs as well as the pattern to extract the image metadata from the files (this can be done in the input modules 'Images' and 'Metadata'). The default output folder in CellProfiler is used to save the output, but this can also be adjusted in the SaveImages and ExportToSpreadSheet modules if desirable. The input images are assumed to be RGB images, from which then the different channels are extracted, if the separate channels are available, it is probably better to use those and to adapt the pipeline accordingly.