dataesr/scientific-paragraphs-categorization huggingface dataset:
HIGH CATEGORY IMPACT --replace -MID3 approach provides inference which informs internal decisions without requiring additional experimental or trial data to be generated MID3 inference only can effectively address strategic questions. MID3 inference is a vital component at the next decision point and may not be verified by evidence generated by future experimental or trial data. Applications include: strategic decisions at the portfolio level (e. g. , starting, stopping, delaying, and accelerating compounds or mechanisms), replication of effectiveness providing evidence of efficacy/safety in lieu of clinical data for regulatory submissions and labelling. MID3, model-informed drug discovery and development; PK, pharmacokinetic; PK/PD, pharmacokinetic/pharmacodynamic. according to its revised intended purpose.
For that reason, irrespective of the formats selected, we recommend that links are established between data, data transformations and manipulation, final model/simulation code, and conclusions in order to facilitate traceability. For MID3 analyses which are either intended to be used for interaction with regulators or submitted for registration and are not reported as part of the standard clinical study report, it is proposed that more structured MID3 documentation proposals are adopted. There are three principal components of MID3 documentation: analysis plan, simulation plan, and report. These documents have a high-level structure shown in Table  4. Additional specific considerations relevant for each component are provided in sections 5. 3, 5. 4, and 5. 5, respectively. The report should provide sufficient detail to enable an independent reviewer to assess precisely what activities have been completed and the appropriateness of the results/conclusions.
Quality assurance, control, and verification There are currently no broadly adopted good practices with respect to Quality assurance, control and verification. 82 The following aims to ensure that the analyses appropriately address the scientific question, are technically correct; and the data, analysis code, and outputs are stored and documented in a manner that allows reproduction by a third party: 1. Quality assurance: to assure the integrity of the data, processes and/or technical solutions should be in place that allow tracing back from the results in the final documentation to the original data/database used (technical audit trail or process description). The person(s) performing the analysis should have been trained in the use of the software(s) utilized. The software used should be ''qualified'' for its intended purpose.
2. Scientific review: to further assure the quality of the analysis, an independent peer review (competent person not involved in the analysis) is recommended. Here, the focus is to critically evaluate the appropriateness of the analysis, and its results, with regard to the stated objectives. 3. QC/verification: these are measures that should be performed for each analysis with regard to generation of the initial and final input dataset, model description, model code, and key simulation code. Some activities may not require QC/verification (e. g. , for low category impact activities, or an analysis for internal decision-making). To assist in this determination, a risk-based approach is suggested: • Probability (low, medium, and high) of a possible error during creation. • Probability (low, medium, and high) of errors remaining undetected.
Data formatting This covers data transformation with respect to units, definition of derived variables, rules around time order of events, and determination of time windows and handling of missing data, including data-imputation and exclusion-based (whenever possible) on prespecified criteria, identification, and handling of outliers. The detailed description of the data specification can be provided in a separate document and only a brief description provided in the analysis plan. ! No dose adjustment necessary for PK reasons. Disease assumption: Linear progression of disease with a slope of X/year. Cannot be estimated directly from the dataset, but supported by literature review.
Data exploration Descriptive and graphical data exploration is an essential initial step. There are generally three aims for this activity: (1) assessment of the quality of the data as part of the quality assurance, control, and verification process (section 5. 2); (2) identification of similarities or gaps with respect to prior knowledge/historical data and the objectives of the analysis; and (3) investigation of patterns and relationships in order to guide model development. Typical outputs would consist of graphical and tabular summaries and descriptive statistics. Although data exploration is essentially (analysis) dataset driven, it is recommended that a high-level description of the plan based on previous knowledge and the objectives of the analysis is provided in the analysis plan.
Methods All methods for model building, selection,  90, 91  identifiability,  92, 93  evaluation, and model qualification  [94] [95] [96] [97]  should be described. The methods utilized will vary based on the available data, the objectives of the analysis. If the analysis is complex, we recommended outlining the MID3 analysis workflow with a visual representation (e. g. , the relationship between various methods and data components used to address the proposed questions). Model evaluation, as part of the learning and knowledge creation step (also described in section 4. 2. 3), should explore how well the model represents the system under consideration, through comparison of simulations and predictions to the available (integrated) data. The more formal qualification procedures can utilize precise criteria to assess the quality of the model with respect to its intended purpose.
Data This is a selected database of relevant data associated with and generated from the system of interest. It is proposed as good practice that Compound, Mechanism, and Disease level data should be assimilated. The determination of the possible pertinent data is the first step in development of the data plan component of an analysis plan. Knowledge This is the outcome from cycles of Model Development and Model Evaluation. The parameter estimates, associated uncertainty, and their interrelationship represent the gain in understanding of the underlying pharmacological, physiological, and pathological properties that describe the system of interest.
QC/verification of final data This is the process of checking the manipulations used in creating the final dataset from the source data are consistently and correctly applied and documented. The nature (e. g. , clinical trial database) and status (e. g. , final and locked) of the source data should also be appropriately captured. QC/verification of model code This is the process of confirming that the algebra is consistent with the model description (e. g. , two compartment models with zero order absorption and mixed linear and nonlinear elimination from the central compartment) and that it is translated correctly into the software code. Similar QC/verification would apply to the ensuring used software settings (estimation approach, priors, stating estimates, and termination criteria/messages are correctly described in the documentation).
ACKNOWLEDGMENTS The EFPIA MID3 work group acknowledges the personal contribution of Efthymios Manolis and Terry Shepard for their ongoing support in the development of this guideline including, but not limited to, their insightful comments and advice during face-to-face planning meetings and regular review meetings. We also acknowledge the personal contribution of the EMA Modeling and Simulation Working Group colleagues and many other company colleagues who have provided review comments and assisted with the compilation of the application examples via their EFPIA MID3 workgroup representatives. Based on a detailed scientific draft provided by the authors, Gardiner-Caldwell Communications provided medical writing support with respect to formatting, styling, and document administration. We would also like to thank Julia Bray for administrative and document support.
What is the level of target Develop a database of liter- inhibition/modulation ature for known com- needed to demonstrate pounds with similar or efficacy? How is duration related mechanisms used and intensity of the inhibi-in the disease/indication tion/activation related to of interest. dosing interval to achieve Develop a mechanistic or adequate efficacy? semimechanistic PK/PD model using biomarkers and known physiology. Compound level Relates to questions and activities that focus on the data associated with a compound of interest. Example questions or Examples of proposed required knowledge? activities What is our confidence in Deterministic and stochastic this compound at this simulation utilizing the stage in development vs. efficacy and safety mod- quantitative product els (including competitor concept? data) to determine proba- bility of meeting product profile based on associ- ated target values.
Define the minimum improvements (target values over SOC) required to meet medical needs What gains in the risk-ben-Develop HTA model that efit profile would be integrates trial outcome required to drive cost-with real-life data and effectiveness given conduct sensitivity analy- expected treatment costs sis to determine likely vs standard of care? improvements required to drive a gain in QALYS vs. expected cost. What is the dose (expo-Develop a database of sure)-response relation-literature for known com- ship for compounds used pounds/placebo used in to treat this disease? the disease/indication of interest. Undertake explo- ration and MBMA analy- sis. What is the dropout rate in Include information on drop- the target population for out and imputation in the known compounds/pla-meta-analysis database cebo and how does it and utilize as covariate in impact efficacy? efficacy analysis.
Based on current knowl-Utilize systems pharmacol- edge of the pharmacolog-ogy model to investigate ical target, can we expect impact of known polymor- genetic variation in phisms leading to response changes in expression of (nonresponders)? key proteins in the path- way of interest. Safety/tolerability What was the onset and Develop a database of What is the exposure-Exposure-response model-Would dose titration Development of mechanistic time course of this literature for known com-response relationship for ing. improve the toleration or semimechanistic PK/ emerging AE profile for pounds/placebo with the emerging safety Exploratory and formal profile? PD model using safety previous compounds/pla-safety or toleration sig-signal? covariate analyses. biomarkers. cebo in this indication? nals of interest.
e. illustrative plots for clinical interpretation or to support a label claim) Discussion Explanation of the relevance of the All readers Place results in technical and clinical context results Discuss assumptions of the model (see Table 5) and limitations of the data Discuss clinical relevance of the analysis Conclusions Present main findings focused on clin- All readers Preferably presented as a bullet point list ical relevance Appendix Documents/scripts and code that Technical Analysis plan allow reproduction of the analysis Dataset specification (including missing data rules).
For instance, the use of metrics like GWP20 or GWP100 can provide information about the time-integrated contributions to global warming in a short/medium term, whereas GTP100 provides information about the instantaneous contributions to global warming on a longer timeframe. If GWP aligns well with the LCA ambition to prefer integrated impacts, GTP provides the possibility to explicitly link global warming contributions to a climate target, based on planetary boundary and/or policy considerations. In general, the utilization of multiple metrics provides complementary information on the implications of mitigating gases with varying lifetimes, and shows the extent to which results are sensitive to the choice of metric or robust across a range of choices. The inclusion in existing LCA databases and impact assessment methods of the spectrum of the metrics available in the latest IPCC assessment report will facilitate their application by LCA practitioners.
The consideration of NTCFs in LCA presents challenges at an inventory and characterization level. Most of the NTCFs are already tracked by the majority of the life-cycle inventory databases, as they contribute to other environmental impact categories, except for BC and OC emissions. Although they can be indirectly quantified from emissions of particulate matter, their explicit inclusion in emission inventories is desirable to facilitate applications. Characterization of their impacts on climate should consider the higher level of uncertainties associated with metrics for NTCFs, and ideally consider the range of possible metric values summarized in the latest IPCC report. The LCA community should closely follow updates on quantification of impacts from NTCFs as the climate science community is continuously improving the robustness of characterization factors for NTCFs.
Regional climate change categories can also be formulated in the future when robust estimates of metric values for regional responses to NTCFs become available. Inventory databases should already be adapted by elaborating spatial-explicit emission inventories that keep track of emission regions. The time is ripe for the LCA community to consider the complexity of climate science and gain insights on the implications associated with the selection of emission metrics for the intended goal of the analysis. Rather than using a single default metric for WMGHGs in all applications, analysts should acknowledge the various forcing agents and the caveats associated with the aggregation of species with different lifetimes to common units. The sensitivity of the results to the type of metric used should be explored.
Data The sample consists of the CAC 40, CAC MID 60 and CAC SMALL index over the period January 21, 2005 to March 11, 2016, a total of 2,852 observations. The one-month T-bill rate is used to proxy for the free-risk rate which is converted into daily equivalent so as to have a similar frequency with the index returns. The SBF 120 index is used to proxy for the market portfolio. The data required is the returns on the CAC index return series. In order to calculate these returns, the author uses data on the daily price. The requisite data is obtained from the Factset database. The price taken as the daily price is the last trading price of the day. The summary statistics for the CAC 40, CAC Mid 60 and CAC Small index (Table  A1  in the Appendix) reveals a positive skewness, and a negative kurtosis.
En tout cas, le présent travail est d'un intérêt certain aussi bien sur le plan académique, que professionnel. Cependant, de tels apports ne doivent pas occulter les limites inhérentes, voire propres à toute contribution qui se veut scientifique. Au premier rang desquelles nous citons l'indisponibilité de certaines informations (absence de bases de données en accès libre sur ce sujet) et la nature confidentielle qui caractérise les données dont nous avions besoin (accès aux bilans d'entreprises), ce qui explique le refus de certains établissements à nous donner accès à leur documentation. À cela s'ajoute la taille limitée de notre échantillon, surtout par secteur, qui aurait visiblement joué sur les tests statistiques menés. Raisons pour lesquelles nous considérons que ces limites sont remédiables en proposant notamment quelques voies de recherche future sur ce sujet.
Remerciements L'auteur tient à remercier les experts-comptables et les commissaires aux comptes qu'il a sollicités pour le recueil des données et qui n'ont épargné aucun effort pour mettre à sa disposition la documentation et les informations nécessaires. Ses remerciements vont également au Professeur Khalifa AHSINA ainsi qu'aux évaluateurs de la revue pour leurs remarques pertinentes et constructives.
Ensuite, le logiciel SPSS retire en passant d'une étape à une autre les variables qui ne contribuent pas à l'amélioration de la prédiction. Au final, le logiciel génère un dernier modèle qui contient les variables les plus significatives et qui constituera la base de nos conclusions. Dans le cas de SPSS, l'élimination d'une ou plusieurs variables se fait sur la base du rapport de vraisemblance, de la statistique conditionnelle et sur la base de la statistique de Wald. En se basant sur le rapport de vraisemblance comme critère d'élimination, les résultats obtenus se présentent ainsi :.
Data collection and treatment: the challenge of measuring the location of logistics establishments and distinguishing the parcel industry In order to measure the location of logistics activities, we used a database containing all the establishments of the Paris administrative region, Ile-de-France. The source for the list of establishments is the Altares database, which uses a data retrieval from the French Register of establishments (called SIRENE), published every year by the French Statistics Institute (INSEE). The database is only available for 2013, so a diachronic analysis was impossible. This database contains key information for our study: the name of the company owning the establishment; the type of activity of an establishment, in the form of a NAF code (equivalent to the NACE code for the European Union, or NAICS code for the United States), a classification of activities into 732 subclasses; the number of employees; the address of the establishment.
This allows us to geocode the establishments precisely, using a GIS system. About 3% of establishments couldn't be geocoded at the address level. They have been geocoded at the center of gravity of the municipality. This is not an important problem because the municipalities are very small in France. There are 1,300 municipalities in Ile-de-France alone. Therefore, establishments geocoded at the center of gravity of municipalities will not be very far from their real location. While the use of this database seems very well suited to the purpose of this paper, it requires in reality a lot of work to make it usable. The SIRENE file, from which Altarès originates, contains many flaws, which are obstacles in the way of a precise estimation of the number and location of establishments. For this reason, the final number of logistics establishments, obtained with our methodology, can only be an estimation of the total number of logistics establishments in the Paris Region.
The first step is to extract all of the logistics establishments from the database. In order to do this, we used the NAF codes in table 1 below. The use of the NAF code is an obstacle in our methodology. Upon creation, every economic establishment in France has to administratively declare its NAF code. Also, INSEE (the French Statistics Institute) updates part of its file every year. So there are several problems: first of all, it is not unreasonable to doubt the precision of the initial statement by the establishment (there are 732 classes and some of them overlap). Also, at this level of detail, an establishment can change its activity type and it may not be updated by INSEE. For example, an establishment of "long-distance freight transportation by road" (49. 41A) can become an establishment of "proximity freight transportation by road" (49.
So they are not really logistics establishments. Therefore, a careless use of the Altarès Database invariably leads to skewed results. Some establishments, which are noted as logistics establishments, have no logistics activities whatsoever. And within logistics establishments, it's not unreasonable to doubt whether the NAF code is relevant to inform us as to the exact activity of the establishment (which would allow us to distinguish between the parcel industry and other logistics activities). After we extract the 10,000 logistics establishments, using the NAF codes in table 1, a number of filters have to be put in place to estimate their number more precisely. For establishments noted as having logistics activities, but that in reality do not (such as company headquarters, or passenger transportation establishments), several filters are put in place to eliminate them from the database.
Firstly, a detailed overview of the names of the establishments allows us to eliminate many of them: this works particularly well for passenger transportation activities (their name often contains reference to taxis, air travel or public transports). The most important work is to gather information through the observation of satellite photographs of the addresses noted in the Altarès database. Sometimes, observation of street photographs is also useful in order to validate or invalidate observations from satellite photographs. † According to us, this methodology is the simplest way of determining which establishments contain logistics activities. In total, about 7,500 establishments are removed from the Altarès database, either on the basis of their name or their address. The other problem is to be able to determine which logistics establishments are a part of the parcel industry, since, as we said before, the NAF codes don't necessarily reflect the exact nature of an establishment.
Not too long after this report was released, the organization took charge of the agri-environmental scheme in a catchment basin in the South of the Seine-et-Marne département covering 12 municipalities, and hired a computer engineer to take care of the agricultural practice database, a public relations manager and a second facilitator specialized in agricultural practices. With the development of this organization's activities surrounding agricultural issues, the local Chamber of Agriculture began to get more involved in the issue of water pollution. Moreover, in 2006, the government published a "plan for water for the département", which among other things proposed subsidies for equipment to prevent accidental pollution on farms and offered the possibility of joining a new territorial agri-environmental scheme to protect water resources by reducing the use of pesticides and herbicides.
Acknowledgements The cases in the Drôme valley were studied in the framework of  Sibylle Bui 's PhD thesis (2015) funded by  INRA  and the  Rhône-Alpes region , and also benefited from financial support in the frame of the  ANR Dynrurabio and Core-Organic HealthyGrowth  projects. The cases in the Paris Basin were studied in the framework of  Aurélie Cardona 's PhD thesis (2012) funded by the  Ile-de-France region , and also benefited from financial support from the  ANR   POPSY  project and  INRA. We also wish to thank two anonymous reviewers who gave a variety of very useful comments on an earlier version of this paper.
Boufateh et al. [26]  applied LCA to compare different products in the textile industry. They checked the applicability of several MCDM (multi-criteria decision making) methods to ease the interpretation of the LCA results and highlighted the relevance of such methods. den Boer et al. [27]  presented a decision support tool for assessing municipal waste management strategies from environmental, economic and social perspectives. The environmental perspective is particularly consistent with the LCA philosophy. The assessment spans over the phases of waste storage, collection, transport and treatment, and final disposal. To simplify the assessment, software tools are used for modelling the subject of the assessment and fed by databases in which various calculation methods such as Ecoinvent are implemented  [28].