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Selection: with tag big-data [11 articles] 


Big data of tree species distributions: how big and how good?

Forest Ecosystems, Vol. 4, No. 1. (2018), 30,


Trees play crucial roles in the biosphere and societies worldwide, with a total of 60,065 tree species currently identified. Increasingly, a large amount of data on tree species occurrences is being generated worldwide: from inventories to pressed plants. While many of these data are currently available in big databases, several challenges hamper their use, notably geolocation problems and taxonomic uncertainty. Further, we lack a complete picture of the data coverage and quality assessment for open/public databases of tree occurrences. ...


A versatile data-intensive computing platform for information retrieval from big geospatial data

Future Generation Computer Systems, Vol. 81 (April 2018), pp. 30-40
edited by Elsevier
Keywords: big-data   cloud-computing   foss   geospatial  


The increasing amount of free and open geospatial data of interest to major societal questions calls for the development of innovative data-intensive computing platforms for the efficient and effective extraction of information from these data. This paper proposes a versatile petabyte-scale platform based on commodity hardware and equipped with open-source software for the operating system, the distributed file system, and the task scheduler for batch processing as well as the containerization of user specific applications. Interactive visualization and processing based on ...


Comparing and selecting spatial predictors using local criteria

Vol. 2013 (2013), 21-13


Remote sensing technology for the study of Earth and its environment has led to “Big Data” that, paradoxically, have global extent but may be spatially sparse. Furthermore, the variability in the measurement error and the latent process error may not fit conveniently into the Gaussian linear paradigm. In this paper, we consider the problem of selecting a predictor from a finite collection of spatial predictors of a spatial random process defined on D, a subset of d-dimensional Euclidean space. Critically, we ...


More accountability for big-data algorithms

Nature, Vol. 537, No. 7621. (21 September 2016), pp. 449-449,


To avoid bias and improve transparency, algorithm designers must make data sources and profiles public. [Excerpt] [...] Algorithms, from the simplest to the most complex, follow sets of instructions or learn to accomplish a goal. In principle, they could help to make impartial analyses and decisions by reducing human biases and prejudices. But there is growing concern that they risk doing the opposite, and will replicate and exacerbate human failings [...]. And in an era of powerful computers, machine learning and big data, ...


The pathologies of big data

Commun. ACM, Vol. 52, No. 8. (August 2009), pp. 36-44,


Scale up your datasets enough and your apps come undone. What are the typical problems and where do the bottlenecks surface? ...


HPC Processor Technologies and Their Impact on Simulation

In Computational Science and High Performance Computing IV, Vol. 115 (2011), pp. 17-28,


Moore’s law has come to an end with respect to the clock speed of the single processor. Clock rates are no longer increasing. Parallelism carries the day and accelerators are making the most of this. What is the future of processors for HPC going to look like? This talk will give a short overview and discuss some potential solutions. ...


Privacy, big data, and the public good: frameworks for engagement



Massive amounts of new data on human beings can now be accessed and analyzed. Much has been made of the many uses of such data for pragmatic purposes, including selling goods and services, winning political campaigns, and identifying possible terrorists. Yet “big data” can also be harnessed to serve the public good: scientists can use new forms of data to do research that improves the lives of human beings, federal, state and local governments can use data to improve services ...


(INRMM-MiD internal record) List of keywords of the INRMM meta-information database - part 5

(February 2014)
Keywords: betula-populifolia   betula-potamophila   betula-psammophila   betula-pubescens   betula-raddeana   betula-recurvata   betula-skvorsovii   betula-spp   betula-sunanensis   betula-szaferi   betula-utilis   betula-zinserlingii   betulaceae   bias   bias-correction   bias-disembodied-science-vs-computational-scholarship   bias-toward-primacy-of-theory-over-reality   bibliometrics   bifurcation-analysis   big-data   binomial-distribution   bio-based-economy   biochemical-product   bioclimatic-envelope-models   bioclimatic-predictors   biocontrol-agents   biodiversity   biodiversity-hotspot   biodiversity-impacts   biodiversity-indicator   biodiversity-offsets   bioeconomy   bioenergy   bioethanol   biofilm   biofiltration   biofuel   biogenic-volatile-organic-compounds   biogeography   bioinformatics   biological-control   biological-invasions   biology   biomass   biomass-burning   biomass-production   biomass-to-energy   biome   biomonitoring   bioscience   biosecurity   biotechnology   biotic-effects   biotic-factors   biotic-homogenization   biotic-interactions   birches   bird-conservation   bird-dispersal   bird-pollination   birds   biscogniauxia-atropunctata   biscogniauxia-mediterranea   biscogniauxia-nummularia   bismarckia-nobilis   bison-bonasus   bixa-orellana   black-aphid   black-carbon   black-pine   black-poplar   black-sea-region   blechnum-spicant   blitz   blue-tits   blue-water   bogs   boiss   bombacopsis-quinata   bombax-malabaricum   bone-attachment   boolean-expressions   bootstrap   bootstrapping   borassus-flabellifer   borch-forest   border-effect   boreal-continental-forest   boreal-forest   boreal-forests   boreal-mountain-system   bosnia-herzegovina   boswellia-sacra   botanical-macro-remains   botany   botryosphaeria-spp   bottom-up   brachylaena-huillensis   brachylaena-rotundata   inrmm-list-of-tags  


List of indexed keywords within the transdisciplinary set of domains which relate to the Integrated Natural Resources Modelling and Management (INRMM). In particular, the list of keywords maps the semantic tags in the INRMM Meta-information Database (INRMM-MiD). [\n] The INRMM-MiD records providing this list are accessible by the special tag: inrmm-list-of-tags ( ). ...


Big Data analysis on autopilot?

BioData Mining, Vol. 6, No. 1. (2013), 22,


Biomedical sciences, especially fields such as genomics, are becoming Big Data fields, driven, to a large extent, simply by the ability to generate enormous data sets. For fields such as biology, where data has traditionally been small, the influx of Big Data, noise and all, has caused a need to rapidly shift research practices. Ways of dealing with these data have led to importing collaborators from statistics, computer science, and physics. However, these new "biologists" are simply not biologists and classically ...


Computing: a vision for data science

Nature, Vol. 493, No. 7433. (23 January 2013), pp. 473-475,


To get the best out of big data, funding agencies should develop shared tools for optimizing discovery and train a new breed of researchers, says Chris A. Mattmann. ...


Finding correlations in big data

Nature Biotechnology, Vol. 30, No. 4. (10 April 2012), pp. 334-335,


In today's era of large data sets, statistical methods that facilitate exploratory analyses to detect patterns and generate hypotheses are critical to progress in biology. Last year, David Reshef and colleagues published a new approach to such analysis, called maximal information criteria or MIC (Science 334, 1518–1524, 2011). Nature Biotechnology solicited comments from several practitioners versed in data-intensive biological research. Their responses not only highlight the appeal of methods like MIC for biological research, but also raise some important reservations as ...

This page of the database may be cited as:
Integrated Natural Resources Modelling and Management - Meta-information Database.

Publication metadata

Bibtex, RIS, RSS/XML feed, Json, Dublin Core

Meta-information Database (INRMM-MiD).
This database integrates a dedicated meta-information database in CiteULike (the CiteULike INRMM Group) with the meta-information available in Google Scholar, CrossRef and DataCite. The Altmetric database with Article-Level Metrics is also harvested. Part of the provided semantic content (machine-readable) is made even human-readable thanks to the DCMI Dublin Core viewer. Digital preservation of the meta-information indexed within the INRMM-MiD publication records is implemented thanks to the Internet Archive.
The library of INRMM related pubblications may be quickly accessed with the following links.
Search within the whole INRMM meta-information database:
Search only within the INRMM-MiD publication records:
Full-text and abstracts of the publications indexed by the INRMM meta-information database are copyrighted by the respective publishers/authors. They are subject to all applicable copyright protection. The conditions of use of each indexed publication is defined by its copyright owner. Please, be aware that the indexed meta-information entirely relies on voluntary work and constitutes a quite incomplete and not homogeneous work-in-progress.
INRMM-MiD was experimentally established by the Maieutike Research Initiative in 2008 and then improved with the help of several volunteers (with a major technical upgrade in 2011). This new integrated interface is operational since 2014.