dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability. All data and metadata are available at the French INSU/CNRS LEFE CYBER database (scientific coordinator: Herveéé Claustre; data manager and webmaster: Catherine Schmechtig) at the following web address: http://www. obs-vlfr. fr/proof/php/outpace/outpace. php (last access: 20 August 2018) (INSU/CNRS LEFE CYBER, 2017).
Research output -The output is the product resulting from conducting research or from interactions of researchers with the actors of the project(s). It can take the form of scientific or non-scientific knowledge (publication, report, database, method, etc. ), professional or academic training, expertise, technology, network or other forms of products. Research outputs can contribute to the emergence of innovation when they are appropriated by the actors of society. In the method described in this guide, the research outputs (knowledge, prototypes, etc. ) developed before the start of the case study are considered inputs while those developed during the case study period are considered outputs. Similarly, it is not always easy to distinguish outcomes from impacts, because an impact at the level of an actor can become an outcome that will generate an impact for another actor who interacts with the former.
Phase 3: Construction of the narrative of the innovation and of the impact pathway In this phase, the aim is to gather and summarize all the data pertaining to the innovation process being studied. It allows to complete the narrative of the innovation and the impact pathway. The objectives of this phase are to: --systematically document information that is useful for revising the narrative of the innovation and for orienting the analysis of the impact pathway (inputs, outputs, outcomes and impacts) to establish cause-and-effect links. In the course of this work, actors formulate impact descriptors and often impact indicators; --identify situations and learning processes that have built up the human and social capital, and characterize the potential contribution of research; --identify key moments and mechanisms of interaction with public policies that have been beneficial to the innovation.
The research community mainly intervenes in the form of projects in developing countries. The demarcation of the case study's scope makes it possible to identify projects undertaken by the research community or by its partners (actors of development) that actually contribute to the innovation. Outcomes of some research projects may be considered as prior learning on the start date of the innovation (accumulation of knowledge, development of methods, etc. ). They are part of the conditions that precede the innovation and create a context that is propitious to the start of the innovation process. This prior learning is considered an input. Table  1  provides information on projects that are considered important for the case study. It is not integrated into the ImpresS database, but allows you to better define the scope of the case study.
Information is collected using a system of surveys that should be carefully designed. The aim is to gather information useful in documenting the narrative of the innovation, identifying impacts, constructing the impact pathway and measuring impacts. This information can be obtained from existing secondary data, through semi-structured interviews, in the course of meetings of focus groups, during workshops involving a larger number of actors, and through surveys of samples of individuals. The same activity can be used to gather information for different aspects of the evaluation. For example, a semi-structured interview of a manager of a producer organization -which allows her/ him to express her or himself about her/his activities, about how s/he experienced a particular situation, and about her/his perceptions and opinions of it -can be used to gather a set of data necessary for different analyses and measurements that form part of the ImpresS method.
Actors are represented by nodes in the sociogram and links are drawn between the nodes to symbolize the relationships between actors. Actors can be specific individuals acting within an organization or organizations themselves. It is possible to represent one or more types of links between the actors, and to illustrate their intensity by varying the thickness of the arrow. It is also possible to represent these links over time to show how they Key tools to characterize the contribution of research to impact become deeper during the innovation process. The links can symbolize exchanges of information, knowledge, material, or funding. In addition, these links can represent different interactions of collaboration or rivalry of various intensities (cooperation, competition, conflict, etc. ). Open source software, such as Pajek 8 , can be used to depict networks of actors graphically.
Identification of inputs mobilized by the research community We refer here only briefly to the inputs mobilized by the research community, since it is difficult to attribute inputs to the innovation (the inputs could be shared by different research activities or by different partners). The inputs will be entered in the ImpresS database as shown in Table  5. The input categories identified from the analysis of the 13 case studies conducted in 2015-2016 are listed in Table  6. Box 9: Origin of the impact pathway The concept of the "impact pathway" comes from ex ante logical frameworks of evaluation used to improve the effectiveness of development or research projects, especially those undertaken by CGIAR. The logical framework has its methodological origins in the management sciences and concerns issues of planning, and more concretely, in guides developed by GTZ (one of the main funders of CGIAR centers) for an improved assessment of organizational conditions that structure the impacts of research projects  (Douthwaite et al.
Key tools to characterize the contribution of research to impact For each of these outcomes, we must: -describe the outcome rigorously, specifying the research outputs that contributed to it (by establishing the causal link(s)); -identify the actors concerned (those who appropriate research outputs, who change their practices, etc. ); -specify the period concerned (in reference to the phases presented in the narrative of the innovation); -note the geographical extent (e. g. local, regional, national, supply chain segment); -describe any change concerning the actors involved (change of vision, practice, organization, knowledge, capacity) that will lead to 1 st level impacts by establishing the cause-and-effect link. Information on outcomes, as presented in Table  9 , will be integrated into the ImpresS database.
). An indicator can be quantitative or qualitative. We must give priority to the indicators that have been mentioned by the actors and, as far as possible, those that can be assigned values via databases, statistics, etc. It is recommended to define two or three intensity indicators and two or three magnitude indicators for each impact. Table  17  shows how impacts can be measured. In the fictitious example mentioned above, following the participatory workshop, interviews and focus groups, three impacts were identified, with several indicators per impact (intensity indicators, magnitude indicators). It is the farmers who are impacted by the innovation (dissemination of a conservation agriculture cropping system) and the NGOs who have supported the farmers (training, advice). Box 14: What is an impact indicator? An impact indicator is defined as data that reports or quantifies an impact.
It only makes sense to measure the impact indicator in relation to a reference situation and to an actor's objectives in a given context. It can be measured for one or more categories of actors. A good indicator is one that is: -specific: the indicator must come as close as possible to the impact it characterizes. For example, for the "income improvement" impact, a quantitative indicator can be "change in farm production" or "change in yield" and a qualitative indicator can be "families' perceptions of the improvement in children's well-being;" -measurable (and robust): the indicator must be quantified or qualified, and measured in the same way in different situations. The indicator is considered all the more robust when its value is obtained from an existing reliable database; -acceptable: the indicator should be easy to understand, share, and must be interpretable in the same way by different observers; -relevant: the indicator must be realistic and relevant to the impact; -temporally defined: the indicator has to be part of a clearly defined temporal dimension, i.
Assigning values to indicators for 1 st and 2 nd level impacts After indicators of intensity and magnitude of impacts have been identified, the data to measure them must be collected. Several solutions exist to assign values to the indicators: -the indicator is quantitative and data are available from databases, statistics (permanent agricultural surveys, surveys on household living conditions or consumption budgets, market price data from information systems, seed supply data from an agricultural research center, etc. ), or reports. The preferred option is to assign values to indicators from these sources; -the indicator is qualitative, or quantitative but no reliable database or statistics are available (which is often the case):. the best option -if time and funding are available -is for the team to undertake a survey to assign a value to the indicator for a sample (different sampling techniques exist, to be chosen according to the team's resources and the context) which reflects the diversity of the impacted actors (producers, companies, etc.
In other cases, such a process might even lead their responses to diverge. In such a case, the diversity of the responses can, if deemed necessary, be presented according to the type of actors. 9 Table  18 , constructed for the previous fictitious example, illustrates the type of data that must be obtained for each case studied before the impacts can be discussed with the actors. 9. For more information on the methodology applied, see  Sackman (1975)  and  Listone and Turoff (2002). Box 15: Examples of data collection in order to assign values to indicators In the case study of the innovative management of organic manure in agro-pastoral systems in western Burkina Faso, a specific survey of a sample of farms was undertaken to measure indicators such as the rate of adoption of certain farming practices, changes in yields or changes in incomes.
Constructing the database Feeding the data collected in a given case study in a (generic) database can be useful for a number of reasons, including for facilitating the triangulation of information collected through various tools, for moving away from locally formulated categories (of actors and their relationships, of impacts) and towards more generic formulations, or for comparing one's results with results obtained in other case studies. CIRAD did build such a database for its own learning and analysis purposes 13. This database is a semi-quantitative generic database constructed in Access; Its aims include (i) archiving and harmonizing the data collected from case studies conducted by CIRAD researchers and their partners, (ii) correlate the information collected by the various tools, (iii) allow a comparison between case studies implemented using the ImpresS approach and conduct cross-analyses, and (iv) ultimately build a record of the contribution of research to innovations.
The CIRAD database is divided into five parts: case ID, impact pathway, actor mapping, capacity building and research activities. The case ID provides information on the name of the case, its status (ex post, in itinere), the department and the research unit responsible for the case study, the start date of the case study, etc. For the "impact pathway" part of the database, each box (input, output, outcome, impact 1 and impact 2) of the graph is entered into the database in the form of a table. In the "actor mapping" part, the corresponding table includes information on all the actors of the innovation process: type of actor, category of actor, type of participation in the innovation process. Each actor has a role, with roles defined in a separate "actor role" table, and linked to the different phases of the innovation process of each case study.
Research output: It is the product resulting from the research or from interactions of the research community with the actors of the project(s). It can take the form of scientific or non-scientific knowledge (publication, report, database, method, etc. ), professional or academic training, expertise, technology, network or other forms of products. Research outputs can contribute to the emergence of innovation when they are appropriated by the actors of society. In the method described in this guide, the research outputs (knowledge, prototypes, etc. ) developed before the start of the case study are considered inputs while those developed during the case study period are considered outputs. Scaling (or change of scale): Geographic extension of an innovation or increase in the number of its adopters (scaling out) or increase in the number of types of actors or arrangements between actors related to the deployment of an innovation (scaling up).
Data extraction and coding We extracted data related to MAs and trials by using two standardized and pilot-tested spreadsheets. The items we extracted are presented in Supplementary Table  1 , available as Supplementary data at IJE online. Data were collected from the systematic reviews, except when otherwise specified. Data extraction was performed independently by two of us (C. L. , B. G. ) and any discrepancy was adjudicated, referring to a third opinion (A. C. ) when necessary. If the number of patients per group, number of events, means and standard deviations were not reported in the systematic review, we collected them from the trial reports, or in case of doubt, we contacted the authors of the systematic reviews (which happened for nine MAs).
In the second stage, the ratio or difference in intervention effects was combined across MAs using random effects MAs. The heterogeneity between MAs was quantified with the I 2 , Cochran Q chi-squared test, and between MAs variance s 2 17 using a REML estimation. 18 Analyses involved use of SAS 9. 4 and R 3. 2. 0 with the package metafor. All the statistical tests were done at a 5% significance level.
Characteristics of selected MAs Considering Cochrane reviews published over a period of five full years, we identified 1301 systematic reviews by the electronic search. We selected 121 MAs (full references in in the Supplementary Material 1a, 1b and 2, available as Supplementary data at IJE online), corresponding to 1458 trials (Figure  1 ): 76 MAs (917 trials) had binary outcomes and 45 (541 trials) had continuous outcomes. MAs concerned very different medical and educational fields and interventions (Supplementary Material 1a and 1b, available as Supplementary data at IJE online). Table  1  shows that pharmacological interventions were investigated in 25 (32. 5%) MAs with a binary outcome but in only six (13. 3%) of those with a continuous outcome. Less than one-third of the MAs had active controls, both for binary and continuous outcomes.
a b s t r a c t 44 325 measured and assigned transitions of H 2 32 S, the parent isotopologue of the hydrogen sulfide molecule, are collated from 33 publications into a single database and reviewed critically. Based on this information, rotation-vibration energy levels are determined for the ground electronic state using the Measured Active Rotational-Vibrational Energy Levels ( Marvel ) technique. The ortho and para principal components of the measured spectroscopic network of H 2 32 S are considered separately. The verified set of 25 293 ortho-and 18 778 para-H 2 32 S transitions determine 3969 ortho and 3467 para energy levels. The Marvel results are compared with alternative data compilations, including a theoretical variational linelist.
Comments on the experimental sources of Table 4 (4a) 72HeCoLu  [34]  contains 2 lines which have been cited as taken from other experimental sources, for which the original data are already in our dataset. These duplicates were removed. (4b) 95BeYaWiPo  [35]  contains 28 lines which have been cited as from other experimental sources, for which the original data are already in our dataset. These duplicates were removed. (4c) 13AzYuTeMa  [42]  gives data for rotational lines recorded at room temperature. The equipment used provides spectra with very high sensitivity and this enabled transitions between highly rotationally excited states to be recorded, even at room temperature. These high rotational excitations may not be so accurately assigned as those of lower energy, as there are no data from other sources in the same region to confirm these measurements.
The supplementary data from the original paper does not contain the original experimental transitions; this was provided by the corresponding author. There were 19 lines which could not be validated against other data and so were removed from our dataset (see Section 3. 2 ). (4d) 83FlCaJo  [43]  contains 39 lines which have been cited as from other sources which are already present in our dataset. The duplicates were removed. (4e) 82LaEdGiBo  [45]  contains one line which could not be validated and so has been removed from our dataset. (4f) 84LeFlCaJo  [50]  contains 2 lines which could not be validated against other more recent data and were removed from our dataset. All data from this source were recalibrated; the calibration factor employed was 0. 999 999 746 below 2450 cm -1 and 1. 0 0 0 0 0 0 0714 above 2450 cm -1.
(4g) 81GiEd  [51]  contains 11 lines which could not be validated against more recent data and thus were removed from our dataset. (4h) 97BrCrCrNa  [54]  contains 2 lines with no corresponding energy in the AYT2 linelist (see Section 5 ) and so were removed from our dataset. (4i) 18Liu  [55]  contains 2 lines with no corresponding energy in the AYT2 linelist and so were removed from our dataset. (4j) 04BrNaPoSi_a  [57]  contains 2 lines with no corresponding energy in the AYT2 linelist and so were removed from our dataset. (4k) 04BrNaPoSi_b  [60]  contains 4 lines which could not be validated and were removed from our dataset. (4l) 03DiNaHuZh  [61]  contains one blended line which has been removed from our dataset. A further 7 lines were found to include Energy Levels 01TyTaSc  [82]  Vibrational energy Levels 69MiLeHa  [83]  Could not be validated with the other sources to a reasonable accuracy (see Section 3.
2 ) 56AlPl  [31]  Could not be validated with the other sources to a reasonable accuracy (see Section 3. 2 ) 69SnEd  [84]  Could not be validated with the other sources to a reasonable accuracy (see Section 3. 2 ) a level with no corresponding energy in the AYT2 linelist and so were removed from our dataset. (4m) 01NaCa_a  [62]  contains 8 transitions which were found to have no corresponding levels in the AYT2 linelist and so were removed from our dataset. (4n) 94GrRaStDe  [63]  tentatively assigned the vibrational band in their data as ( v 1 , v 2 , v 3 ) = (2 , 2 , 2) ; however, in 95FlGrRaSt  [79]  , a paper published later with the same authors, the revised assign- ment is ( v 1 , v 2 , v 3 ) = (3 , 0 , 2). We adopt the latter vibrational label in our dataset. No other data sources probe this vibrational band.
The frequency is only given to 3 decimal places, so the uncertainty was altered to match. The original dataset has 82 transitions either labelled with an asterisk to indicate lines from the H 34  2 S isotopologue, or labelled with a dagger to indicate lines which have been perturbed due to those from H 34  2 S  [63]. We have commented these out by adding a minus sign to the wavenumber and added the label _ pt to the end. (4o) 97VaBiCaFl  [64]  contains 4 lines which could not be validated and so were removed from our dataset. A further 8 lines were found to contain a level with no corresponding energy in the AYT2 linelist and so were removed from our dataset. (4p) The assignment for 99CaFl_185_na_ct from 99CaFl  [65]  leads to different spin states ( ortho / para ) for the upper and lower state, which is forbidden.
We have removed this transition from our dataset. 5 other lines could not be validated and so were removed from our dataset. 7 lines were found to have no variational counterparts in the AYT2 linelist and so were removed. (4q) 01NaCa_b  [66]  contains one line which could not be validated and so was removed from our dataset. A further 18 lines were found to contain levels with no corresponding energies in the AYT2 linelist and so were removed from our dataset.
General comments All transitions which were considered but not processed in the final dataset have a minus sign in front of the transition wavenumber (indicating that Marvel will ignore them) in the input files provided as supplementary material and are labelled with a comment at the end of the reference (see the comments of Section 3. 1 ). We used a cut-off of 0. 035 cm -1 as the largest acceptable uncertainty and removed any transitions with an uncertainty greater than this, with a note in Section 3. 1 to indicate how many transitions from a particular data source could not be validated to within this accuracy. The publications 97BrCrCrNa  [54]  , 04BrNaPoSi_c  [56]  , 04BrNa-PoSi_a  [57]  , and 04BrNaPoSi_b  [60]  include just a short description of the assignment and modelling of the Fourier Transform (FT) spectra between 4500 and 8900 cm -1.
Detailed information on the theoretical treatment of these spectra will be published separately in due course. Some of these transitions, between 4400-80 0 0 cm -1 , were previously reported in the HITRAN-2012  [85]  and HITRAN-2016  [86]  databases, while the second decade region, 840 0-890 0 cm -1 , is presented for the first time. It is also worth mentioning that the Marvel dataset includes a considerable number of transitions which include new, unpublished energy levels. As only the upper energy levels of the transitions were reported in 05UlLiBeGr  [53]  , 04UlLiBeGr_b  [58]  , and 04UlLiBeGr_a  [59]  , the corresponding transition wavenumbers were recovered from Liu (18Liu  [55]  ) using the energy levels obtained from 97BrCr-CrNa  [54]  , 04BrNaPoSi_c  [56]  , 04BrNaPoSi_a  [57]  , and 04BrNa-PoSi_b  [60].
For this reason, the transitions in the Marvel dataset which are referred to as from 05UlLiBeGr  [53]  , 04UlLiBeGr_b  [58]  , and 04UlLiBeGr_a  [59]  contain a larger number of assignments than reported in the original publications. The data of the first hexad region between 450 0-560 0 cm -1 were totally assigned based on the energy levels reported in 97BrCrCrNa  [54]. The highly accurate (an accuracy of 0. 0 0 05 cm -1 ) set of H 32  2 S transitions recorded using a FT spectrometer between 2200-4250 cm -1 was provided by Horneman (18Horneman  [49]  ) and assigned using the energy levels reported in 98BrCrCrNa  [48].
Sources from the HITRAN database H 32  2 S has been included in the HITRAN database  [85] [86] [87] [88] [89] [90]  since 1991  [87]. The following are sources for the line positions and energy levels of the H 32  2 S data in the HITRAN database, up to the 2016 release: 96UlMaKoAl  [47]  , 83FlCaJo  [43]  , 98BrCrCrNa  [48]  , 13AzYuTeMa  [42]  , 94YaKl  [40]  , 95BeYaWiPo  [35]  , 05UlLiBeGr  [53]  , 04UlLiBeGr  [58]  , 03DiNaHuZh  [61]  , 01NaCa_b  [66]  , 84LeFlCaJo  [50]  , 82LaEdGiBo  [45]  , 85LaEdGiBo  [80]  , 94ByNaSmSi  [81]. The variational ExoMol AYT2 linelist  [91]  was used to assign transitions in the ν 2 excited vibrational state in the 2012 release  [85].