Documents.irevues.inist.fr/bitstream/handle/2042/28893/Sur la transdisciplinarité.pdf?sequence=3. Why open access makes no sense | Higher Education Network | Guardian Professional. The fundamental argument for providing open access to academic research is that research that is funded by the tax-payer should be available to the tax-payer.
Those who have paid for the research, it is urged, should not have to pay a second time for access to the publication of that research. Proponents of what has come to be called 'open access' claim that this is simply obvious, but in fact this argument mistakes the fundamental nature of academic research, it mistakes nature and process of academic publication, and it mistakes what is involved in providing access to academic research. I shall limit my claims here to research in the Humanities, but very similar arguments apply to research in the sciences also. The problem is that the two situations are quite different. But the differences go further than this important question of who sets the agenda.
But this isn't what publication of humanities research means at all. There can be no such thing as free access to academic research. Eprints.rclis.org/5463/1/do_open_access_CRL.pdf. TF-IDF. Un article de Wikipédia, l'encyclopédie libre. Le TF-IDF (de l'anglais Term Frequency-Inverse Document Frequency) est une méthode de pondération souvent utilisée en recherche d'information et en particulier dans la fouille de textes. Cette mesure statistique permet d'évaluer l'importance d'un terme contenu dans un document, relativement à une collection ou un corpus. Le poids augmente proportionnellement au nombre d'occurrences du mot dans le document. Il varie également en fonction de la fréquence du mot dans le corpus. Des variantes de la formule originale sont souvent utilisées dans des moteurs de recherche pour apprécier la pertinence d'un document en fonction des critères de recherche de l'utilisateur.
Introduction[modifier | modifier le code] La justification théorique de ce schéma de pondération repose sur l'observation empirique de la fréquence des mots dans un texte qui est donnée par la Loi de Zipf. Définition formelle[modifier | modifier le code] où : = qui). On obtient : How We Help. PDFMiner. Last Modified: Mon Mar 24 12:02:47 UTC 2014 Python PDF parser and analyzer Homepage Recent Changes PDFMiner API What's It? PDFMiner is a tool for extracting information from PDF documents. Unlike other PDF-related tools, it focuses entirely on getting and analyzing text data.
Features Written entirely in Python. PDFMiner is about 20 times slower than other C/C++-based counterparts such as XPdf. Online Demo: (pdf -> html conversion webapp) Download Source distribution: github: Where to Ask Questions and comments: How to Install Install Python 2.4 or newer. For CJK languages In order to process CJK languages, you need an additional step to take during installation: # make cmap python tools/conv_cmap.py pdfminer/cmap Adobe-CNS1 cmaprsrc/cid2code_Adobe_CNS1.txt reading 'cmaprsrc/cid2code_Adobe_CNS1.txt'... writing 'CNS1_H.py'... ...
Command Line Tools. Www.cs.rutgers.edu/~mlittman/courses/ml03/iCML03/papers/ramos.pdf. MyScienceWork. Quick Start. Available in other languages: Japanese Tutorial: Download it in PDF. The tutorial follows the following steps with LesMiserables sample dataset. You can find other network datasets on the wiki. Import file Visualization Layout Ranking (color) Metrics Ranking (size) Layout again Show labels Community-detection Partition Filter Preview Export Save. Choose Dataset(s) — Open Data Handbook.
Choosing the dataset(s) you plan to make open is the first step – though remember that the whole process of opening up data is iterative and you can return to this step if you encounter problems later on. If you already know exactly what dataset(s) you plan to open up you can move straight on to the next section. However, in many cases, especially for large institutions, choosing which datasets to focus on is a challenge. How should one proceed in this case? Creating this list should be a quick process that identifies which datasets could be made open to start with. There is no requirement to create a comprehensive list of your datasets.
Asking the community We recommend that you ask the community in the first instance. Prepare a short list of potential datasets that you would like feedback on. Cost basis How much money do agencies spend on the collection and maintainence of data that they hold? This argument may be fairly susceptible to concerns of freeriding. Ease of release Observe peers.
IBM. The Opportunity in Big Data Analytics and Social Business. Big Data Means Big Potential for Taiwanese Social Enterprise (Op-Ed) By Remi Kanji on 10 Jan 2013 / 0 Comment Robust public health data offers social innovators a myriad of potential opportunities, and Taiwan is sitting on a data goldmine. Taiwanese health records are digitized, kept on a Java Virtual Machine chip attached to each individual’s health card and updated in a central system.
The cards almost look like a credit or debit card, save for the photo and vital statistics. And carrying a Taiwanese health card is a bit like carrying a folder with your medical history: the chip stores individual patient data including information on allergies, medical history, and past test results. It even allows for efficient billing, so that hospitals are quickly reimbursed for their services. While many in the medical field already consider these health cards cutting edge, the data generated by health cards should be used for more than simply informing government programs. But how are these savings calculated? 10 ways big data changes everything. Code For America - Big Data For Public Good. Eric Stowe Joins Do Good Data 2013 | Data Analysts for Social Good.
ESS: Big Data for Social Good. Big data constitute a huge opportunity. Never before have researchers had the opportunity to mine such a wealth of information that promises to provide insights about the complex behavior of human societies. While the privacy implications of this data should not be understated, we aim to show that these types of massive datasets can be leveraged to better serve both the billions of people who generate the data, and ultimately the societies in which they live. The Causal Structure of Food Shortage in Uganda How soon in advance can we predict a food shortage? Variables such as market prices, drought, migrations, previous regional production, and seasonal variations all play a role in this classification and causal structure learning model to predict whether a rural inhabitant is likely to encounter difficulty in obtaining food. - G.
Okori, J. A Causal Model for Quality of Schooling Generative Models of the Nairobi Slums Computational Transport Planning and Modeling in Kigali. The Obama Campaign’s Chief Data Scientist on the Future of Civic Data. Photo: Courtesy of the University of Chicago Arguably Chicago’s most important and most successful startup in recent years was the Obama campaign and its celebrated data, analytics, and technical staff, which attracted some of the city’s best brains and re-elected a president. Basically every politico I’ve ever talked to describes campaigns as start-ups: a small group of smart people working impossible hours and trying to remake the industry on the fly.
But unlike start-up companies, campaigns have a strict end date, disappearing as quickly as they’re begun, and leaving a group of top talent on the job market. What the Obama campaign’s talent will do has been a topic of speculation. And the chief scientist of the campaign’s data analytics team has chosen his path. Rayid Ghani, who worked as a director of analytics research at Accenture before joining Obama for America in 2011, will be working on a host of projects at the University of Chicago.
What differences do you see this data making? Les Big Data dans tous leurs états. ParisTech Review – En l’espace de deux ou trois ans, le thème des Big Data s’est imposé dans l’espace public, suscitant enthousiasme et réticences… sans qu’on sache toujours précisément de quoi il s’agit. Pouvez-vous nous l’expliquer rapidement ? Henri Verdier – Cette confusion n’a rien de surprenant, car non seulement c’est un thème récent mais, surtout, on assiste à un affrontement politique et économique autour de sa définition.
L’expression « Big Data » renvoie à au moins trois phénomènes. Selon une acception étroite, elle désigne de nouvelles technologies informatiques dans le domaine du traitement de données massives. Selon une acception plus large, elle désigne la transformation économique et sociale induite par ces technologies. Enfin, certains analystes en font une rupture épistémologique, avec le passage des méthodes hypothético-déductives sur lesquelles s’est édifiée la science moderne à une logique inductive, très différente. Certainement pas. Mais on peut aller plus loin. Harnessing big data to address the world’s problems. Jonathan Bays McKinsey & Company Jonathan Bays is a consultant in McKinsey & Company’s Social Sector Office, which helps organizations address societal challenges.
Based in New York, he specializes in philanthropy and economic development, working primarily with grant makers and nonprofits on issues of strategy. Before joining the Social Sector Office in 2005, Bays worked as a policy advisor in the Office of the Prime Minister of Canada. He was previously a consultant in McKinsey’s Toronto office. Bays graduated from the University of Toronto with a BA and MA in history. He received his M Phil and D Phil in international relations from Oxford University, where he was a Rhodes Scholar. Steve Davis President & CEO PATH Steve Davis is President and CEO of PATH, an international nonprofit health organization that develops and delivers high-impact, low-cost health solutions.
An earthquake has struck a developing country, devastating a large city. Fortunately, data saves the situation. Data for Social Good: A Beginners Guide for Nonprofits and Social Ventures | Dutiee. Data is gaining tremendous prominence in the business world. Companies are becoming highly data driven in every aspect of their business – from gaining new customers, providing better personalized services, cutting costs, to developing new products. With the visible benefits of increased profits and efficiency, companies are spending more resources than ever to understand, visualize and monetize data. The positive outcomes for data driven businesses has got nonprofit thinkers and leaders excited to try and spark the same kind of excitement around data in the social sector. Currently, there is a talk around how big data can fuel social good, my goal with this article is to help nonprofits and social startups understand how they can apply data thinking in their everyday work and hopefully demystify this trend.
Nonprofits Are About Small Data First up, do not be daunted by all the talk around big data. Where Does Data Come From? How to Manage and Mine Data? Nexleaf Analytics & New Technology to Measure Impact | Data Analysts for Social Good. DemystData. How can big data be used for social good? | Guardian Sustainable Business | Guardian Professional. Big Data Means More Than Big Profits - Jim Fruchterman.
By Jim Fruchterman | 8:00 AM March 19, 2013 Big Data is all the rage in Silicon Valley. From Facebook to Netflix, companies are tracking and analyzing our searches, our purchases, and just about every other online activity that will give them more insight into who we are and what we want. And though they use the massive sets of data they collect to help create a better experience for their consumers (such as customized ads or tailored movie recommendations), their primary goal is to use what they learn to maximize profits.
But can Big Data also create positive social change? Many activities in the social sphere also generate lots of information. Now, as a pragmatic idealist I’ve always believed that technology could be an immense force for good in the world, but I’ve also recognized that great technology wouldn’t get developed — no matter how beneficial — if it was missing one important factor: big profits. Social entrepreneurs should focus on Big Data for the social good. Home • Crowdmap.