The DMAIC approach used in Quality Management stands for:

Define, Measure, Analyze, Improve, and Control

Design, Measure, Analyze, Implement, and Cost

Design, Monitor, Assess, Improve, and Compare

Define, Modify, Apply, Increment, and Close

What is Quality Control

Detection of defects in a product

Concerned with the integration of all the efforts in organization

Process of recognition of entire manufacturing process

Quality Check list are used to

Ensure that quality assurance steps are followed

Inform management where errors occur

Which of the following must not be a characteristic of a quality system?

Products or services actually do satisfy customer expectations

QUALITY MANAGEMENT PRINCIPLES AND TOOLS

Chapter 7 - Review Data and Decision Making *Glow bus due at midnight, name and student number: answer questions using content in class People have created wonderful things for centuries, and management Management can be traced as far back as 500 bc when the ancient Sumerians used written records to improve government and business activities Why is it important to lean from the past Not to repeat our mistakes Classical management approaches Scientific management Administrative Principles Bureaucratic organisation Behavioural Management Approaches Follett’s Organizations as communities The Hawthorne studies Maslow’s theory of human needs Mcgregor’s Theory x and Theory Y Argyris Personality and organisation Modern Management foundations Organises as systems Contingency thinking Quality management Quantitative and analysis and tools Evidence-based management Contributions Frederick Taylor - Father of Scientific management He noticed that workers often did their jobs with wasted motions and without a constant approach. His resulted in inefficiency and low performance He believed the problem could be fixed if workers were taught to do their jobs in the best ways and ten were helped and guided by supervisors Four guiding principles of scientific management Rules of motion, standardized work and proper working conditions Select workers with the right abilities Train workers and give them incentives Support workers by planning and smoothing the way as they do their work Frank and Lillian Gilbreth Pioneered use of motitono studies as a management tool In one famous case, the gilbreaths cut down the number of motions used by bricklayers adn tripled their productivity Contributions from scientific management Make results-based compensation a performance incentive Carefully design jobs with efficient work methods Carefully select workers with the ability to perform the job Trian workers to execute activities to the best of their abilities Train supervisors to support workers so they can perform jobs to the best of their abilities Classical Management Adiminstative principle (Henro Fayol) 1919, after a career in French industry, Henri F published “adminisration Industrielle et Generale” (General and industrial management) in which we out like his views on the management of organiztion and workers Rules and duties in management Foresight - to complete a plan of action for the future Organization - To provide and mobilize resources to implement the plan Common- to lead, select and evaluate workers to get the best work toward the plan Coordination- to fit diverse efforts together and ensure information is shared and problems solved Control- to make sure things happen according to plan and to take necessary corrective action Classical management Bureacratic organiztion (Max Weber) Max weber (Bureaucrativ organization) - late 19th century German political economist who had a major impact in the fields of management and sociology Bureaucratic Organization An ideal, intentionally rational adn very efficient form of organization Based on the principles of logic, order and legitimate authority Characteristics of BO Clear division of labour Clear hierarchy of authority Formal rules and procedure Impersonality Careers based on merit What are some disadvantages of bureaucracy Takes a long time for problems to become solved bec there are procedures and there is a chain of people in command Having the power Rules have to follow Excessive paperwork or “red tape” Slowness in handling problems Rigidity in the face of shifting needs Resistance to change Employee apathy Behavioural Management Approaches (focus on understanding the elements that affect human behaviour in organisations) Follett’s Organizations as communites Mary park follett contributed to the transition from classical thinking inot behavioural management Groups and human cooperation Groups allow individuales too combine their talents for a greater good Organizations are cooperating “communites” of managers adn workers Managers job is to help people copperate and achive an integration of goals and intrests Forward-looking managment insight: Making every emploee an owner creates a sense of collective responsibility Prescursor of employrr ownership, profit sharing and gain sharing Buniess problems invovle a varity of inter realted factors Prescursor of systems thinking Private profits realtive to public good Precursor of managerial ethics and social respinsibility Hawthorne studies Took place at western electric chicago plan, a tran led by Harvards Elton Mayo set out to learn how econmic incentives and workplace conditions affected workers output Maing objective Intial study examined how ecomoin incentives adn physical conditions affected worker output (productivity) No consistent relationship found During experientmetn they had 2 groups The expertiant groups (impoved wokring ocnditions ) The control group ( no changes to original working conidtions) No consitant relationship found, perfomance in both groups increased even after removing incentives Social setting and human relations Concluded New “social setting” led workers to do good job Good “Human relations” = higher productivity The contect - The Great Depression (1929-1940) Employee attitudes and groups processes Osme thinsf satisifed some workers but not others People resticited output to adhere to groups norms (Avoid layoffs) Lessons from he hawthrone stufirs Social and human concerns are keys to prductivity Hawthrone effect - People who are singled out for special attention perform as expected Maslow’s Theory of human needs Human needs The work of psychologist Abraham Maslow in the area if human “needs,” also has had a major impact in the behavioual apporach to management Maslow’s hierarchy of human needs Self actualization needs Higherst level: need foe self fulfillment to grow and use abilites to fullest and most creative extent Esteem needs Needs fro esteem in eyes of others need for respect, prestige, recognition; need for self esteem, personal sense of competence, mastery Social needs Need for love, affection, sense of belongingness in ones relationship either other people Safett needs Need for security, protection and stability in teh events of day to day life Physiological needs Most basic of all human needs: need for biological maintence; food, water and phydical well being Principles Defict principle: A satidifed need is not a motivator of behaviour Progress principles: A need becomes a motivator once the preceding lower-level need is satisfied Both principles cease to operate at self actulilzation level McGregor’s Theories Thepry x assumes that workers; Dislike work Lack ambition Are irresponsible Resist change Prefer to be led Theoyry y assumes that workers are Willing to work Willing to accept responsibility Capable of self control Capable of self direction Imaginative and creative According to McGregor, Managers create: Self fulfilling prophecies Implications of Theory x and y Theory x managers: Create situations where workers become dependent, passive and reluctant Theory y managers create situations where workers respond with initiative and high performance Central to notions of empowerment and self management Argyris’s theory of adult personality Classical management principles and practices inhibit worker maturation and are inconsistent with the mature adult personality Management practices should accommodate the mature personality: Increasing task responsibility Increasing task variety Using participative decision making Modern Management Foundation Quantitative analysis and Tools Analytics: the use of large data bases and mathematics to solve problems and make informed decision using systematic analysis Organization as systems System Collection of interrelated parts that function together to achieve a common purpose Subsystem A smaller component of a larger system Open systems Organisations that interact with their environment Contingency thinking Tires to maths managerial responses with problem (situation) No “one best way” to manage The “appropriate way to to manage depends on the situations Quality management Qality anc competitive advantafe are linked Total quality managment (TQM) Comprehensive approach to contiou impovment on teh entire organization ISO certification Gloval quality management standards Refine and upgrade quality to meet ISO requirments Evidednce Based Managment Making management decision on “hard facts” about what really works

New Trends in Agriculture Extension approaches Extension has been, and still is, under attack from a wide spectrum of politicians and economists over its cost and financing. As a result, Extension Systems have had to make changes, by restating the system’s mission, developing a new vision for the future, and formulating plans for the necessary transition to achieve the desired change. 1. Privatization of Agricultural Extension Service Privatization: Process of funding and delivering the extension services by private individual or organization is called Private Extension. Concept: Privatization of extension refers to services rendered in rural area & allied aspects of extension personnel working in private agencies or organization for which farmers are expected to pay a fee & it can be viewed as supplementary or alternative to public extension services (Sarvanan & Shivalinge 1980). Privatization approaches ➢ Share cropping system ➢ Village extension contract system ➢ Public extension through private delivery ➢ Service for vouchers Strengths of Private Extension System ➢ More demand - driven rather than supply – driven ➢ High quality of services in terms of satisfying information needs of clientele, trained manpower, sustained finances and resource allocation ➢ Provides for an information mix and choices available to farmers ➢ Enhanced efficiency of staff ➢ Assure continuous supply and quality agricultural products ➢ More effective because farmer can select an adviser who is the best able to help ➢ Healthy competition among service provider will lead to better quality and lower costs for service Weakness of Private Extension System ➢ Concentrate on area having favorable physical environment ➢ More face-to-face contacts (person oriented) ➢ Increased dependence of farmers and hence exploitation ➢ No education role ➢ Deprivation of small farmers ➢ Hamper the free flow of information 2. Cyber Extension or e-extension Concepts Cyber space: it is the imaginary or virtual space of computers connected with each other on Networks, across the Globe. Cyber extension: it means 'using the power of online networks, computer communications and digital interactive multimedia to facilitate dissemination of agriculture technology. Cyber Extension thus can be defined as the extension over cyber space. Important tools of cyber extension E-Mail, Telnet, File Transfer Protocol (FTP), Gopher, Archie and World Wide Web (WWW) Strengths of Cyber Extension ➢ Access to the astounding information and continuously available ➢ Information rich and instantaneously available of information ➢ Interactive communication ➢ The information is available from any point on the globe ➢ Communication is dynamic ➢ Cut steps from traditional process ➢ Save money, time and effort ➢ Multiplicity of purpose Issues and Concerns of Cyber Extension ➢ Lack of Reliable Telecom Infrastructure in Rural Areas ➢ Erratic or no Power Supply ➢ Lack of ICT Trained manpower (willing to serve) in Rural Areas ➢ Lack of content (locally relevant and in local languages) ➢ Lack of Information Services to Rural Clientele ➢ Low Purchasing power of the Rural communities ➢ Lack of Holistic Approaches ➢ Issues of Sustainability Application of cyber extension ➢ Village information shops Dr. M.S. SwaminathanResearch Foundation, Chennai ➢ Information villagers MANAGE in Ranga Reddy District in Andhra pradesh ➢ Gyandoot net initiative of District Dhar, Madhya Pradesh. ➢ Warna wired village of National Informatics Center (NIC) in Kolhapur- Sangli Districts of Maharashtra 3. Market-Led-Extension (MLE) Concepts Market: A congregation of prospective buyers & sellers with a common motive of trading a particular commodity. Extension: It is the spreading/reaching out to the mass Market-led-extension: Agriculture & economics coupled with extension is the perfect blend for reaching at the door steps of common man with the help of technology. Dimensions of market-led extension ➢ Marketing mix: A planned mix of the controllable elements of a product's marketing plan commonly termed as 4Ps: product, price, place, and promotion. These four elements are adjusted until the right combination is found that serves the needs of the product's customers, while generating optimum income. ➢ Marketing plan: A marketing plan is a comprehensive document that outlines a business and marketing efforts for the coming year. It describes business activities involved in accomplishing specific marketing objectives within a set time frame. A marketing plan also includes a description of the current marketing position of a business, a discussion of the target market and a description of the marketing mix that a business will use to achieve their marketing goals. ➢ Market Intelligence: It is the information relevant to a company’s markets, gathered and analyzed specifically for the purpose of accurate and confident decision making. Market intelligence includes the process of gathering data from the company’s external environment, whereas the business intelligence process is primarily based on internal recorded events – such as sales, shipments and purchases. ➢ Market oriented production ➢ Use of Technology Strengths of market-led extension ➢ SWOT analysis of the market ➢ Organization of Farmers’ Interest Groups (FIGs) ➢ Enhancing the interactive and communication skills of the farmers ➢ Establishing marketing and agro-processing linkages ➢ Advice on product planning ➢ Educating the farming community ➢ Direct marketing ➢ Acquiring complete market intelligence ➢ Publication of agricultural market information Production of video films of success stories ➢ Challenges to market-led extension ➢ Gigantic size of extension system ➢ Information technology Diverse conditions ➢ Market intelligence ➢ Reforms in agricultural extension system Government Initiatives ➢ Central warehousing Corporation-1965 ➢ MSP by Commission for Agricultural Cost and Price (CACP) ➢ Food Corporation of India ➢ Then some others as: Cotton Corporation of India (CCI), Jute Corporation of India (JCI), National Dairy Development Board (NDDB), Agriculture and Processed food Export Development Authority (APEDA) etc. 4. Farmer--Led-Extension (FLE) Farmer--led-extension is defined as 'the provision of training by farmers to farmers, often through the creation of a structure of farmer promoters and farmer trainers' (Scarborough et al., 1997). Philosophy and principles ➢ Farmers and local institutions (e.g. producer organizations or village leaders) should play a key role in selecting farmer-trainers and monitoring and evaluating them. This helps make the programmes more accountable to the community or groups that they serve. ➢ Farmer-trainers are ‘of the community’; they communicate in local languages and are more sensitive to local cultures, mannerisms, farming practices, and farmers’ needs. ➢ Farmer-trainers should be selected on the basis of their skills and interest in sharing information, not just on their farming expertise. ➢ Farmer-trainers need strong linkages with and support from development agents (whether government, non-government organization (NGO), or private), the people who train and backstop them. Farmer-trainers generally serve as a complement to existing extension systems, rather than being a substitute for them. ➢ Facilitating organizations and local institutions need to be proactive in ensuring that women as well as men become farmer-trainers. ➢ Simple and appropriate reference materials should be made available to the farmer trainers. Essential Elements of Farmer--led-extension ➢ The group ➢ The Field ➢ The Facilitator ➢ The curriculum ➢ Programme leader ➢ Financing Special features of Farmer--led-extension ➢ All learning is field based & it is primary venue for learning ➢ FLE group learning constantly over the experimentation period ➢ FLE promotes healthy decisions & quality decisions ➢ Farmers conduct their own field studies with comparisons or treatments ➢ Facilitates Farmer-to-Farmer communication ➢ Field staff serve as facilitators ➢ FLE is a unique way to educate farmers ➢ It is an effective platform for sharing of experiences and collectively solving agriculture related problems. 5. Expert system Expert system is an intelligent computer program that uses knowledge and inferences procedures to solve problems (Daniel Hunt, 1986). Objectives of developing expert system ➢ To enhance the performance of agricultural extension personnel and farmer ➢ To make farming more efficient and profitable ➢ To reduce the time required in solving the problems ➢ To maintain the expert system by continuously upgrading the database Advantages of expert system ➢ Solves critical problems by making logical deductions without taking much time ➢ It combines experimental and conventional knowledge with the reasoning skills of specialists ➢ To enhance the performance of average worker to the level of an expert Limitations of expert system ➢ Expensive computer program ➢ Mostly developed not in regional languages ➢ Requires AC power and internet connection all the time ➢ Complex software requires computer skilled personnel Modules of expert system in agriculture ➢ COMAX: Integrated crop management in cotton ➢ SOYEX: Soybean oil extraction expert system ➢ PLANT/ds: Diagnosis of soybean diseases ➢ MAIZE: Maize expert system for field crop management ➢ SEMAGI: Weed control decision making in sunflowers ➢ Rice Crop Doctor: Developed by National Institute of Agricultural Extension Management (MANAGE) Difference between conventional and expert system of extension Conventional Extension ➢ Universal approachability of same information is a problem ➢ Information is given whatever is available without considering needs and resources ➢ No Cost benefit analysis ➢ Information flow depends on availability of agent ➢ Require users to draw their own conclusion from facts Expert System of Extension ➢ Universal approachability of same information is possible ➢ Information is chosen based on their needs and resources ➢ Cost benefit analysis ➢ Information through Cyber Cafe at any place at any time ➢ Conclusion is drawn based on the decision given by the expert

Principles of TQM, Leadership – Concepts – Quality Trilogy – Four pillars of TQM – PDCA cycle & PDSA cycle. Role of Senior Management – Quality Council, Customer satisfaction – Customer Perception of Quality, Customer Complaints, Employee Involvement – Motivation, Empowerment, Teams, Recognition and Reward, Performance Appraisal, Benefits, Continuous Process Improvement

Administrative jobs involve performing administrative roles that support workers in the agriculture industry. b. Engineering jobs involve using high-level science and math to solve complex problems. Professionals, evaluate, design, test and install agricultural equipment and systems. c. Labor jobs require workers to perform manual tasks such as planting, harvesting, caring for animals and maintaining equipment Sales jobs are performed by professionals who are responsible for selling materials and products to customers. e. Science jobs are those of scientists who work in agriculture and specialize in crops, livestock or food production. Agricultural Jobs: a. Farm workers perform essential manual labor tasks under the supervision of farmers and ranchers. They harvest or inspect crops, assist in watering the plants, applying fertilizer and pesticides to control weeds and insects. b. Growers are responsible for taking care and raising crops that involves proper management of the growing plants and its environment to keep the crops/plants healthy. c. Grain Elevator operators assist in maintaining essential quality standards of grains by properly storing, shipping and purchasing grains. They receive incoming grain deliveries, store the grain safely and they may assist in preparing outgoing shipments, drying grain and blending different grain types. d. Agricultural equipment technicians maintain, install and repair machines and implements. They perform preventive maintenance, which may involve refueling machines, replacing batteries, changing the oil and lubricating moving parts. When they detect a malfunctioning equipment, they perform diagnostic tests and conduct necessary repairs. e. Purchasing agents are responsible for buying agricultural products and raw materials at wholesale for processing and reuse. These professionals often have to meet specific purchasing quotas for processors. They work with several farming clients, who serve as suppliers of grain, milk and other agricultural products. f. Farm warehouse managers are responsible for overseeing all activities related to storing, shipping and receiving agricultural materials. They send and receive shipments, including loading and unloading products and materials Agriculture specialists perform administrative support and clerical tasks that focus on a certain aspect of farming. Some agriculture specialists focus on storage, which requires them to work with farmers to develop high-performing crop and grain storage and inventory systems. h. Sales representatives sell materials and products to businesses and government agencies. They seek out prospective customers by attending trade shows, reviewing customer lists and following leads from existing clients. They determine customers' needs, explain how their products meet clients' needs and create packages that meet customers' budgetary and timeline needs. i. Crop managers oversee the many steps in the crop production process. They supervise seed sourcing, planting processes and scheduling as well as fertilizing, irrigation and harvesting. j. Environmental engineers use science and engineering principles to design and apply solutions to problems that occur on agricultural sites. They assess environmental conditions—including testing soil and analyzing drainage capabilities—and develop improvements. k. Feed mill managers supervise the production and storage of animal feed. They are responsible for monitoring inventory levels, scheduling feed production and inspecting the quality of the grain. These professionals set and maintain quality standards, assess and improve operating procedures and track customer complaints. l. Research scientists who specialize in agriculture often work as food scientists, who research and develop processes for manufacturing, storing and packaging food. They are responsible for developing or improving products, but some specialize in detecting contaminants or administering government regulations

Dès le début de vos recherches, vous allez collecter, produire et exploiter des données. La gestion des données (Research Data Management - RDM) fait partie du processus de recherche. Elle concerne l'ensemble des opérations de collecte, description, stockage, traitement, analyse, archivage et mise en accès des données. (extrait de : Passeport pour la Science Ouverte. Guide pratique pour les doctorants ) "La science ouverte est la diffusion sans entrave des publications et des données de la recherche. Elle s’appuie sur l’opportunité que représente la mutation numérique pour développer l’accès ouvert aux publications et – autant que possible – aux données de la recherche. "Les données de la recherche sont la matière première de la connaissance. Les partager, c'est ouvrir de nouvelles perspectives scientifiques" Source : Plan national pour la Science ouverte - Ministère ESR - Juillet 2018 Source image : https://bibliotheques.univ-tlse3.fr/file/composantes-science-ouverte Cette page est une introduction à la gestion des données de recherche. Elle présente quelques concepts et étapes clés pour vous engager dans cette démarche. Consultez les liens pour approfondir vos connaissances. • What are data ? Définition des données de recherche de l’OCDE (2007) « Enregistrements factuels (chiffres, textes, images, sons) utilisés comme source principale pour la recherche scientifique et généralement reconnus par la communauté scientifique comme nécessaires pour valider les résultats de la recherche. Un ensemble de données de recherche constitue une représentation systématique et partielle du sujet faisant l’objet de la recherche ». Exemples • les images d’une ville préhistorique deviennent des données pour un chercheur qui étudie l’histoire de cette ville; • les « données » d’un linguiste peuvent être des écrits ou des discours, des enregistrements de locuteurs ; • les « données » d’un médiéviste sont des sources archivistiques, archéologiques, épigraphiques, iconographiques, littéraires ; • les « données » d’un géologue rassemblent des coupes et observations de terrain consignées sur un carnet, des résultats de carottage, des analyses d’échantillons, des données sismographiques… • • Pourquoi partager ses données ? "La science ouverte vise à construire un écosystème dans lequel la science est plus cumulative, plus fortement étayée par des données, plus transparente, plus rapide et d’accès plus universel.La science ouverte favorise également les avancées scientifiques, particulièrement les avancées imprévues, ainsi que l’innovation, les progrès économiques et sociaux, en France, dans les pays développés et dans les pays en développement. Enfin, la science ouverte constitue un levier pour l’intégrité scientifique et favorise la confiance des citoyens dans la science. Elle constitue un progrès scientifique et un progrès de société." Source : Plan national pour la Science Ouverte (2018) Les enjeux de l'Open Data • enjeux patrimoniaux o preuve et mémoire (éviter les pertes de données) • enjeux économiques o valeur économique de la donnée o réutilisation gratuite ou payante des données, exploitation des résultats de recherches antérieures (éviter de refaire ce qui a déjà été validé), o accélération de l'innovation et le retour sur investissement dans la R&D • enjeux scientifiques o de "hypothesis-driven" à "data-driven" o plus de visibilité pour le scientifique • enjeux sociétaux o participation des citoyens et de la société civile : "Citizen science" o confiance en la recherche Pour aller plus loin • Site Doranum : https://doranum.fr/enjeux-benefices/fiche-synthetique/ • Adopter de bonnes pratiques tout au long du cycle de vie des données De bonnes pratiques de gestion à toutes les étapes du cycle de vie de la donnée sont un préalable indispensable à l’ouverture des données et à leur réutilisation. • Rechercher des données Pour identifier des jeux de données (datasets) pertinents pour votre thèse, des outils de recherche sont disponibles. Suivez ces liens pour les découvrir : • Site Doranum : https://doranum.fr/acces-visualisation/rechercher-donnees/ • Site DataCC - Vos besoins, trouver des données : https://www.datacc.org/vos-besoins/trouver-des-donnees/ • Fiche CoopIST : Trouver des jeux de données via des bases pluridisciplinaires et des moteurs de recherche Pensez-aussi à consulter l'entrepôt institutionnel Data INRAE Page de présentation du portail • Choisir les bons formats et bien organiser vos données  Choisir des formats de fichier : https://www6.inrae.fr/datapartage/Gerer/Choisir-des-formats-de-fichier  Nommer et organiser vos fichiers de données : https://www6.inrae.fr/datapartage/Gerer/Nommer-et-organiser-ses-fichiers-de-donnees Pour aller plus loin • Jaouen, G.- Gérer ses données. Pourquoi, Comment ? Séminaire - Guadeloupe, du 25 au 27 Novembre 2019 – CRAG INRA • Bien décrire et documenter ses données La description d’un jeu de données se fait à l’aide de métadonnées (*) qui doivent apporter suffisamment d'éléments (sur la collecte des données, les unités de mesure employées...) pour chercher et trouver le jeu de données, juger de sa qualité/fiabilité, et pouvoir le comprendre ou le réutiliser dans un autre contexte. (*) Définition des métadonnées : Ensemble d’informations structurées qui décrit, explicite, localise une ressource informationnelle, dans le but d’en faciliter la recherche, l’usage, et la gestion. Source : NISO. Understanding Metadata. 2004. Quelques liens utiles : • Site Doranum : https://doranum.fr/metadonnees-standards-formats/ • DataCC : https://www.datacc.org/vos-besoins/documenter-ses-donnees/metadonnees/ • Site DataPartage INRAE : https://www6.inrae.fr/datapartage/Gerer/Documenter-les-donnees En complément des métadonnées, la rédaction d'un fichier READ ME.txt est également recommandée. • Stocker, sécuriser, préserver ses données Bien différencier les notions de stockage et d'archivage. Anticiper pour déterminer les données à éliminer et celles qui doivent être préservées à long terme. • Dans l'environnement INRAE : https://www6.inrae.fr/datapartage/Gerer/Stocker-les-donnees • Site Doranum : https://doranum.fr/stockage-archivage/ • Site DataCC : https://www.datacc.org/vos-besoins/conserver-ses-donnees/ • Partager, ne pas partager ses données ? Dans le cadre de la Science Ouverte, il y a de plus en plus d'incitations voire d'exigences pour rendre accessibles les données, en particulier les données liées aux publications : • de l'édition scientifique : de plus en plus de revues adoptent une "data policy" (à consulter dans les instructions aux auteurs) et exigent des auteurs qu'ils fournissent les données associées aux publications, • des organismes de financement (ANR, Commission Européenne ...), • des politiques nationale (Plan national pour la Science ouverte - Ministère ESR - Juillet 2018) et institutionnelle. Mais attention, toutes les données ne sont pas partageables : assurez-vous que vos données sont bien diffusables au regard du droit et des conditions d'exercice de votre thèse et de son mode de financement (se reporter à votre contrat de thèse). Les données produites dans les organismes de recherche publics sont communicables à tous si elles n'entrent pas dans le cadre d'exceptions légales (sécurité défense, sécurité des populations, patrimoine scientifique et technique, données personnelles, données liées au secret, statistique, etc.) Liens utiles : • sur le site Data Partage, la page Partager-Publier ou la page : "Données de la recherche : qui a les droits, qui doit partager ?" • le site INRAE dédié à la protection des données personnelles et l'application du RGPD (Règlement général sur la protection des données) : https://intranet.inrae.fr/cil-dpo • Valoriser ses données Voici les principales voies de diffusion •  Partager ses données en les déposant dans un entrepôt  Choisir un entrepôt  Déposer dans Data INRAE  Partager ses données comme matériel supplémentaire d'un article (à la demande de l'éditeur)  Publier un Data Paper (article de données) : la meilleure voie en terme de visibilité des données, et pour faciliter leur réutilisation. Pour aller plus loin • Site Doranum o Dépôts et entrepôts. Comment et où déposer mes données ? o Data papers et Data journals. Comment publier mes données comme un article scientifique ? • Site DataCC o Valoriser ses données • Site CoopIST o Déposer des données de recherche dans un entrepôt o Rédiger et publier un data paper dans une revue scientifique A télécharger : Synthèse du processus de rédaction d'un article avec des données associées • Pourquoi ne pas rédiger un plan de gestion de données (PGD) pour votre thèse ? La thèse peut être assimilée à un projet et certaines universités au Royaume Uni, aux Pays-Bas et plus récemment en France préconisent la rédaction d'un plan de gestion associé à la thèse. Le PGD (ou DMP = Data Management Plan) est un outil de planification qui peut vous aider à anticiper et bien gérer toutes les étapes du cycle de vie de vos données, à limiter les risques de perte ou corruption de données, à adopter de bonnes pratiques de gestion, pour in fine produire des données respectueuses des principes FAIR, adoptés aujourd'hui par l'ensemble des acteurs de la recherche. Il est désormais exigé par la plupart des financeurs de la recherche (Commission Européenne et ANR ...) dans le cadre de projets financés. Rédiger un PGD pour votre thèse, peut être un bon exercice pour vous préparer à la future rédaction de réponses à des appels d'offre. Comment faire en pratique ? • Site DataPartage : Pourquoi et comment rédiger un plan de gestion de données ? • Site Doranum : https://doranum.fr/plan-gestion-donnees-dmp/, La minute vidéo PGD • Site DataCC : https://www.datacc.org/bonnes-pratiques/adopter-un-plan-de-gestion-des-donnees/ • Suivre une classe virtuelle INRAE : Open Class "Rédaction d'un PGD" • Produire des données FAIR ! Favoriser la production de données FAIR (Findable - Accessible - Interoperable - Reusable) est aujourd'hui un objectif soutenu par l'ensemble des acteurs de la recherche. Source : https://open-science-training-handbook.gitbook.io/book/ Si vous suivez les conseils et recommandations de cette page, vous avez toutes les chances d'avoir produit des données de qualité. Si vous préférez une version illustrée : "Pensez FAIR" - https://datapartage.inrae.fr/Gerer/Cycle-de-la-donnee Affiche cycle de vie des données réalisée dans le cadre des Missions QualiNous & RGPD, INRAE-ACT Vous pouvez tester le niveau de "Fairification" de vos données grâce à ces outils :  ARDC : https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool • D'autres ressources pour se former ou s'autoformer En interne INRAE • Formation à la science ouverte OSCAR - Module "Gestion et partage des données" • Le site "Gestion et partage des données" • Des classes virtuelles d'environ 2h (Open Class) sont régulièrement proposées sur : o la rédaction des plans de gestion de données, o le dépôt et la description d'un jeu de données dans Data INRAE, o la rédaction et la publication de data papers, Sites externes • Le site DORANUM (Données de la Recherche : Apprentissage NUMérique à la gestion et au partage) propose un dispositif de formation à distance intégrant de nombreuses ressources d’auto-formation déclinées sur différents supports (textes, infographies, vidéos) et sur 9 thématiques. o Parcours interactif sur la gestion des données de la recherche (2020) o • Le site DataCC. Accompagnement à la gestion des données de recherche en physique et en chimie : https://www.datacc.org/ o Data Stories : https://www.datacc.org/reseau-datacc/data-stories/ o • Le dossier "Open Access & Open Data" réalisé par l'Ecole des Ponts - ParisTech • • The Open Science Training Handbook : https://www.ouvrirlascience.fr/the-open-science-training-handbook/

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