dataesr/scientific-paragraphs-categorization huggingface dataset:
At a national or regional level, multiple works are ongoing. For example, for Catalonia,  Rovira and Labastida (2018)  set up a methodology using the Wos and Scopus to get the publications and then Unpaywall to get the OA status. Countries like the Netherlands, Denmark, and Germany also work on national Open Access monitoring. Some of them rely on a bottom-up data flow. In Denmark for example, publication metadata is collected from each of the local research databases of the 8 Danish universities. 10 None of the existing approaches allowed to track the OA rate at a national level without a bottom-up data production system or the use of an existing database that already has geographical metadata. Our methodology instead relies on huge open databases and referential and uses simple (for matching) and more complex algorithms to enrich and filter the data.
Method First step in the method: Identify the publications with a French affiliation 12 In this work, we decided to focus only publications with a DOI (Digital Object Identifier). We use data from Unpaywall and HAL (open repository) to list all the publications published during the period year. There are about 4. 5 million DOIs per year. These sources provide also a few metadata for the publications: title, authors first name and last name, journal name, and International Standard Serial Number (ISSN). However, the affiliations metadata are generally missing (especially from Unpaywall, which is the main source we use). 13 As a consequence, to decide whether a given DOI has a French affiliation, we first check if it is available on HAL with a French affiliation. If not, we scrap the DOI redirect page, i.
This can be measured manually, with a check of a random sample. • False negative (recall), i. e a DOI is not detected as having a French affiliation, but it actually has. This is much more difficult to measure as it is the 'invisible' part. Second step in the method: Enrich the metadata of the identified publications Once the perimeter of the publications is defined after the first step, it is essential to describe them, that is to say, being able to answer these questions: who are the authors of these publications? in which structures do they work? to which scientific field do they belong? For authors and affiliations, that means improving data interoperability by adding external identifiers from national and international databases and referentials like ORCID, IDRef, HAL for authors, and grid (Global Research Identifier Database), RNSR (r épertoire national des structures de recherche, which is the referential for French research structure maintained by the French Ministry of Higher Education and Research) and SIRENE (Système national d'identification et du répertoire des entreprises et de leurs établissements, referential for French corporations maintained by INSEE) for affiliations.
Scientific field classification We want to estimate the Open Access prevalence by discipline. The task of estimating what is the scientific discipline of a given publication could leverage on the content of the publication (title, keywords, summary and full text), the journal in which it is published, and also on the authors themselves. In this work, we use only "content data", trying to infer information from the title of the publication. We focus on the title as it is the only content data available at a large-scale. Clustering a set of documents by topic is a difficult Natural Language Processing (NLP) task (unsupervised learning). Instead, we chose to use a supervised learning method, but we needed a training dataset. We used the "Pascal-Francis" open database from INIST, CNRS, with 17M+ publications.
This training dataset labels each of the 17 million entries with tags from a hierarchical classification of a few thousand tags. We then train a model to predict, from a title, the Pascal-Francis classification tags. To do so, we used the approach developed by Joulin, Grave et al (  2017 ) called FastText classification. It leverages on word embeddings, including bi-grams. The model was evaluated with a train-test split of 80% -20% and then trained on the whole dataset before being applied to predict scientific field on new data. Once this model built, we then cluster the Pascal-Francis tags into 10 macro-disciplines, namely: Biology, Engineering, Social sciences, Mathematics, Computer and information sciences, Chemistry, Medical research, Physical sciences / Astronomy, Earth / Ecology / Energy / Applied biology, Humanities.
We add an extra step in the scientific field classification when the associated probability given by the model is too low. That means the FastText classifier is really unsure of the predicted tags. That can be the case for example when the title is too short. There, scientific discipline is consolidated at the journal level. So publications that have no scientific discipline get the scientific discipline that is the most common in the same journal. Third step in the method: Open Access detection Eventually, we detect if a publication is OA using data from Unpaywall and HAL. We fetch not only the OA status but also information about licensing and hosting. The main point in this third step is actually the dynamics. That is to say that the OA status of a publication evolves over time.
Two publishers specialized in social sciences and humanities, mainly in French language (CAIRN and OpenEdition) represent put together 5. 5% of the total. What is the split of the identified DOIs per discipline? The scientific discipline of a publication is not part of its available metadata. So we built a machine-learning classifier that has first to be evaluated. We evaluate the performance of the classification model with two metrics, precision and recall. The precision is the number of correct labels among the labels predicted by the model. Conversely, the recall is the number of labels that successfully were predicted, among all the real labels. The real labels in our case are the Pascal-Francis labels in the training set. As an entry can have multiple labels in the training dataset, we evaluated the model in two contexts, with only the first label predicted (Precision@1 and Recall@1), and with the first five labels predicted (Precision@5 and Recall@5).
e publications indicated as closed whereas they actually are open access. Also,  Mikki, Susanne, et al. (2018)  show that, for the Norwegian case, the national OA rate measured with oaDOI (previous version of Unpaywall) is 31% when they state it is actually at 70%. We manually check a random sample of publications to assess their open-access status. Given a publication marked as closed in Unpaywall snapshot we used, three cases can happen: • It is still closed access as of the time of the manual check. • It is open access, and it is also seen as open access in the Unpaywall feed data (data has evolved since the snapshot we used) • It is open access, but still seen as closed in the Unpaywall feed data. With two random samples of publications (of size 100 each), one for the year 2017, and one for the year 2013, we get the following results: These results show that the OA status is hard to get right, and moving in time.
Even for many-year-old publications (e. g from 2013), we estimate the false negative rate at 3%. This figure can be decomposed in two parts: 2% that were marked as closed in the Unpaywall data we used (from June 18) but now marked as open in the updates; and 1% still marked as closed. Two hypotheses could explain the first part: either the OA status has actually changed, more than 6 years after publication; either Unpaywall engine got improved in the meantime and now catches DOI that it did not catch a few months before. We do not have enough evidence to decide what is the right explanation. This phenomenon gets amplified for more recent publications. Their OA status actually moves for sure, because of the embargo and moving barriers. We remark that most of the false negative cases are publisher-hosted open access.
What is the overall level of Open Access on the period 2013-2017? We observe in Figure  3  a gentle increase from 39. 6% in 2013 to 42. 7% in 2016 of estimated OA rate for detected French publications. In 2017, we see a small decrease to 40. 5%. However, we analyze this decrease as a consequence of the difficulty to catch the OA status dynamics underlined above, and not as a real decreasing trend. Moreover, this decrease is backed only by publisher hosted OA, which is in line with our previous observation that most of the false negative cases are publisher hosted. These OA rate figures should then be seen as a snapshot of the situation as of June 2018 (date of the Unpaywall snapshot). As a consequence, the database we build with our method should be considered as a living database, that keeps updating from new pieces of information (new DOIs, new OA status), even for objects whose publication date is several years back in the past.
But there is still much room for improvement for green OA. Indeed, with the evolution of the French law (Digital Republic Act) in October 2016, authors whose research has been publicly funded at more than 50% have the right (not the obligation) to post their work on a repository, after an embargo period of maximum 12 months (depending on the disciplines). So we expect the share of repository hosted OA to grow faster in the next years. This work also presents several limits. First, we included in the analysis only the publications with a DOI. That excludes from the analysis a share of the production, in particular, a part of books. Moreover, to compensate for the metadata poverty, we add to enrich them: French affiliation detection, author identification, discipline classification. Each of these enrichments is automatic, based on rules (regex like or heuristics) and machine learning.
So there are errors, that we tried to estimate. Another limit of the analysis is the difficulty to get the OA status right. In particular because of its dynamics, and also because we rely on the Unpaywall data that is itself containing errors, in particular, false negative. The design and the implementation of this methodology is transparent and independent from commercial databases. Some bits of the method are specific to the French case. It concerns the alignment with French referential, like IDref for authors. ORCID is a good international identifier, but in our case, we rely on the IDRef producer (ABES) that already automatically match ORCID and IDRef once a year. But parts of it are totally generic: identifying the publication for a given perimeter (country, region…) is based only on rules that can be changed; the discipline classification is generic (but trained on the Pascal-Francis database); the OA status comes from Unpaywall which is a large-scale database.
Further work This work is only a first step to build a large-scale, open, database for Open Access monitoring. Upcoming work is about authors and affiliations identification in several external referential, especially IdRef, ORCID, GRID, RNSR, SIRENE, HAL to improve data interoperability. That means handling the disambiguation for authors and affiliations, and use existing, external identifiers for these objects. Improving the detection of the publications with a French affiliation is also part of the next steps. Especially how we could catch publications without DOI. Besides, a large piece of work concerns the measure and the understanding of the OA status dynamics. At what pace does the OA status move? Next steps will also include the addition of new data sources, namely OpenAPC for Article Processing Charges (APC) monitoring and OpenCitations to add references and citations.
Piwowar, Priem et al (2018)e embargo period for open repositories and moving barriers for journals. In this work, we have used cold data from the June 2018 snapshot of the Unpaywall database. However, we plan in future work to keep updating the data with warm data coming from Unpaywall data feed. A shortfall here of the Unpaywall data is that it does not contain the date of the first time a publication was detected as OA, but instead the last time. Memorizing what is the first time the publication was OA would enable to analyze how long it takes, after the publication date, for a publication to become OA. This will be part of our further work. Also, different types of OA exists. Piwowar, Priem et al (2018)differentiate several types: gold, green, hybrid and bronze. In this study, for a matter of simplicity, we only differentiate publisher hosted OA and repository hosted OA.
In addition, different ensemble models are also embedded in the FS, combining individual models and allowing for the production of improved forecasting results. Using a job scheduler, every model is automatically executed on the basis of its own regular timetable according to the given requirements, following a Directed Acyclic Graph (DAG) methodology  [15]. According to the latter, there are three main stages during the execution of each model, including the preparation of the input data, the execution of the learning process and the post-treatment of forecasting results. In the preparation subtasks, every model accesses a database where the inputs to be used for training are selected. Then, under the execution subtasks, forecasting models are executed, providing both deterministic and probabilistic predictions for the chosen horizon of prediction.
In this stage, the models are also automatically trained with the latest historical data available, in accordance with the determined time horizon. Finally, during post-treatment, predictions are sent to the database, where they remain stored. On top of that, the FS supports additional tasks, such as the execution of Numerical Weather Prediction (NWP) models  [16] , providing predictions of meteorological variables that are next used in several of the developed models. As a final outcome, by applying certain validation criteria on the forecasting results produced from the pool of both individual and ensemble models, the FS designates the optimum prediction of each variable. Results obtained are eventually transmitted automatically to the MMP, as shown in Fig. 1. Among the main features of the FS, one should also stress the easy and robust connection with the developed EMSs of TILOS project.
Both the Hybrid Power Station EMS (HPS-EMS), developed to maximize the revenues of the Tilos island HPS, and the High-Level EMS (HL-EMS), whose objective is the minimization of system costs -integrating also DSM strategies-are executed with the same job scheduler, allowing for the coordinated management of the FS and EMS tasks. Moreover, the MMP is also able to directly access the different Tilos assets using different, standard communication protocols (such as Modbus TPC, OPC and IEC61850), collecting data for the execution of the forecasting models and transmitting commands in concordance with the results obtained (Forecasting -EMS). This platform has also the ability to communicate all of the data stored in the database to external clients using appropriate asynchronous communication protocols such us AMQP, MQTT or OpenWire.
A User Interface (UI) has been implemented to this end, both for the FS and the entire MMP, allowing visualization of measurements, forecasting and EMS results, and access also to data stored in the databases through different web services. One of these web services allows also for the management of the forecasting and EMS models' execution (Fig. 1 ). The developed forecasting models embedded in the FS are trained and executed with measurements collected by the weather stations and load meters installed on Tilos island (Fig. 2 ). Data collection from Tilos island began in 2015, with the installation of two dedicated weather stations (min-resolution of weather data including wind speed at different heights, solar irradiance at both the horizontal and tilted surface, ambient temperature, atmospheric pressure, relative humidity and PV panel temperature) and two power meters (min-resolution load demand data of the whole island and the village of Livadia in the south part of the island).
This made possible to estimate the membrane resistance ) i from Eq. (  24 ). In the case of the blocking electrode conditions, the influence of the supporting electrolyte and of the electrode material on the resistances were explored through the impedance model ] ^. The parameters = % (related to electrolyte conductivity), = \ (representing the double layer capacity) and ) j present in the electrode impedance could be extracted concomitantly from the fitting. In all cases, the experimental data fitting by the model was performed by a non-linear least square method implemented in Matlab with two different algorithms: trust region reflective and Levenberg-Marquardt  [24].
The term "gross" is used because all energy output is taken into account; on the contrary Net Energy Ratios subtract from the numerator all "self-use" output that is used in the pathway of production of the technology. 5 A related indicator that is sometimes used to compute EROI (as it is already included in many input-output databases) is the Cumulated Energy Demand (CED). I do not use it because  Arvesen & Hertwich (2015)  have shown that it is erroneous to use the CED directly for EROI computations, without making adjustments. In most cases, EROIs (or energy ratios) are defined using quantities of primary energy. However, I adopt a different approach in this paper, and use only secondary energies in my computations. Indeed, as  Arvesen & Hertwich (2015)  put it, "EROI does not need to measure primary energy per se; the crucial point is to measure energy diverted from society in a unit of equivalence".
Data, Sources and Method I apply these formulas to the IOTs (i. e. technology matrices A) and the vectors of unitary energy supply E S from THEMIS  (Gibon et al. , 2015). THEMIS contains hybrid inputoutput tables: precise data on electricity units (the foreground) is completed with data on other sectors that originates from life cycle inventories and national accounts (the background). Gibon et al. (2015)  have compiled various life cycle inventories into the 609 sectors of the foreground, including original and up-to-date life cycle inventories for electricity sectors. Hertwich et al. , 2015  and its Supplementary Information (SI) detail sources and values retained for the evolution of crucial parameters of electricity technologies, such as energy efficiency and market shares of different photovoltaic modules.
The background contains data in physical units for 4,087 sectors from the life cycle inventory ecoinvent and data in monetary units for 203 sectors from the inputoutput database Exiobase  (Wood et al. , 2014). The 44 Exiobase regions are aggregated into 9 macro-regions that coincide with those of the International Energy Agency (IEA), so that the number of rows and columns in each IOT is 9 times the number of sectors: 44,046. Starting from data of the 2010 IOT, the 2030 and 2050 IOTs of THEMIS embed expected technological efficiency improvements of key background sectors, produced by the New Energy Externalities Development for Sustainability project  (NEEDS, 2009). NEEDS' realistic-optimistic scenario was identified as the closest match to the Blue Map and Greenpeace's scenarios assumptions, namely the deployment of best available techniques and reasonable efficiency trends, while the realistic-pessimistic scenario matched the Baseline assumptions.
As the difference is small between these two scenarios, I focus on the 100% renewable one. I describe my methodology for embedding the regional electricity mixes of Greenpeace's scenarios into THEMIS in AppendixA. In the literature, most EROIs estimations follow a bottom-up approach that use data from life cycle inventories. Bottom-up studies describe in details the power facilities and the most direct inputs to the energy technologies, but they do not cover the entire economy: indirect inputs such as clerical work or R&D are often beyond their system boundaries  (Suh, 2004). On the contrary, the input-output method allows to encompass all embodied inputs exhaustively. As a consequence of this more comprehensive account of embodied energy than usual, we expect estimates of EROIs lower than the average of the literature.
6% in a 100% renewable one. The adjustment needed for the cost, around 6%, gives a rough estimate of the upward bias of unadjusted EROI estimates (see section 4. 2 on the relation between price and EROI). Finally, data for Concentrated Solar Panels (CSP) had to be adjusted, because the original data mistakenly contained an energy supplied by unit of solar CSP of 0 in some regions (leading to abnormally low EROIs, around 2). Backed by Thomas Gibon, core developer of THEMIS, I corrected this error by setting the unitary energy supplied for solar CSP in all regions to its value in OECD North America (still letting the value depend on the scenario and the year).
Of course, the quality of inputoutput data is never perfect, and making predictions is notoriously difficult, as was recently proven by the unexpected fall in the price of photovoltaic (PV) modules. However, there are several reasons to be more confident into future EROIs estimates from THEMIS than into past predictions on prices from other sources. First, technical coefficients are more stable than prices. Second, THEMIS accounts for materials and energy efficiency gains for electricity technologies, and uses "fairly favorable assumptions regarding wind conditions, insolation and resulting load factors", which if anything would bias EROIs of renewables upward (see SI of  Hertwich et al. , 2015). Third, THEMIS already includes recent industry road maps in its prospective matrices (see section 3.
Empirical Relation Between EROI and Price Using US data on oil and EROI from  (Cleveland, 2005) , Heun & de Wit (  2012 ) regress p t on the EROI. 9 They obtain a good fit even in their simplest regression (R 2 = 0. 8), and find p oil = β 0 • EROI -1. 4 oil. This result is interesting, and documents a negative relationship between price and EROI, which is close to an inverse one. As the authors do not regress price on the inverse of EROI, one cannot compare whether an inverse specification would provide as good a fit as a log-log one. To undertake this comparison, I run these two regressions using all estimates of EROI computed using THEMIS, one for each combination of scenario, year, region and sector. To obtain the price corresponding to each EROI, which I take before taxes and subsidies on production; I assemble from the columns compensation of employees and operating surplus of the characterization matrix of THEMIS a row vector v of value-added per unit of each sector.
ACKNOWLEDGMENTS General Secretariat for Research and Technology (GSRT)  and  Hellenic Foundation for Research and Innovation (HFRI)  (project code:  508 ) are gratefully acknowledged for the financial support of this research. Also, the  European Commission 's  Seventh Framework Program  ( FP7/2007-2013)  under grant agreement no. 229927  ( FP7-REGPOT-2008-1 , Project  BIO-SOLENUTI ), and the  Special Research Account of the University of Crete.
The carrier gas was argon flowing at 9. 5 mL/min at constant pressure of 0. 4 bars. Injection was performed via a 250-μL gas-tight (Hamilton) syringe previously degassed with CO 2. Conditions allowed detection of both H 2 , O 2 , N 2 , CO, and CO 2. Calibration curves for H 2 and CO were determined separately by injecting known quantities of pure gas. Material characterizations. An X-Ray Photoelectron Spectrometer THERMO-VG ESCALAB 250 (RX source K AI (1486. 6 eV)) was used. X-ray absorption spectra were collected at the LUCIA beamline of SOLEIL with a ring energy of 2. 75 GeV and a current of 490 mA. The energy was monochromatized by means of a Si (111) double crystal monochromator. Data were collected in a primary vacuum chamber as fluorescence spectra with an outgoing angle of 5°using a Bruker silicon drift detector.