Mechanics in the earth and environmental sciences

1995 ◽  
Vol 32 (09) ◽  
pp. 32-5092-32-5092
Keyword(s):  
Environmental Sciences ◽  
The Earth

scholarly journals NSF Grant: AGU and Partners Aim at Gender Issues in Geosciences

Eos ◽  
2017 ◽  
Author(s):  
Billy Williams
Keyword(s):  
Sexual Harassment ◽  
Gender Equity ◽  
Gender Issues ◽  
Environmental Sciences ◽  
Earth Space ◽  
The Earth

Funding through July 2021 supports a project in the Earth, space, and environmental sciences to promote gender equity and train scientists to recognize and counteract sexual harassment.

scholarly journals The disconnect between researcher ambitions and reality in achieving impact in the Earth & Environmental Sciences – narrowing the gap

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 36
Author(s):  
Andrew Kelly ◽  
Victoria Gardner ◽  
Anna Gilbert
Keyword(s):  
Collective Action ◽  
Real World ◽  
Environmental Science ◽  
Response Rate ◽  
Environmental Sciences ◽  
Global Society ◽  
Research Journal ◽  
The Earth ◽  
Research Endeavour ◽  
Real World Problems

Background: There is an increasing desire for research to provide solutions to the grand challenges facing our global society, such as those expressed in the UN SDGs (“real-world impact”). Herein, we consider whether the frameworks that underpin the research endeavour are appropriately oriented to support these aspirations and maximize the capability of research to achieve these goals. Methods: We conducted a survey of authors who had published in >100 of our Earth & Environmental Science journals. The survey was sent to just under 60,000 authors and we received 2,695 responses (4% response rate).   Results: Respondents indicated that the majority of their research in the Earth & Environmental Sciences is currently concerned with addressing urgent global needs or that this will become a priority in the future; however, the impetus seems to be altruistic researcher desire, rather than incentives or support from publishers, funders, or their institutions. Indeed, when contextualised within other forms of impact, respondents indicated that citations or downloads were more important to them than contributing to tackling real-world problems. Herein, we analyse survey feedback, suggest the presence of a misalignment between researcher ambition and current realities, and discuss the role and value of the research journal in forming new connections for their researchers, both within and without academia. Conclusions: At present, it seems that this laudable ambition of achieving real-world impact is seemingly being lost amidst the realities of being a researcher. We offer for comment a series of suggestions, with the aim of simulating discussion and collective action to tackle these challenges as a community.

scholarly journals Spark Sensing: A Cloud Computing Framework to Unfold Processing Efficiencies for Large and Multiscale Remotely Sensed Data, with Examples on Landsat 8 and MODIS Data

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Hai Lan ◽  
Xinshi Zheng ◽  
Paul M. Torrens
Keyword(s):  
Time Series ◽  
Remotely Sensed ◽  
Landsat 8 ◽  
Remotely Sensed Data ◽  
Environmental Sciences ◽  
Modis Data ◽  
The Earth ◽  
Data Provision ◽  
Using Data ◽  
Local Cloud

Inquiry using data from remote Earth-observing platforms often confronts a straightforward but particularly thorny problem: huge amounts of data, in ever-replenishing supplies, are available to support inquiry, but scientists’ agility in converting data into actionable information often struggles to keep pace with rapidly incoming streams of data that amass in expanding archival silos. Abstraction of those data is a convenient response, and many studies informed purely by remotely sensed data are by necessity limited to a small study area with a relatively few scenes of imagery, or they rely on larger mosaics of images at low resolution. As a result, it is often challenging to thread explanations across scales from the local to the global, even though doing so is often critical to the science under pursuit. Here, a solution is proposed, by exploiting Apache Spark, to implement parallel, in-memory image processing with ability to rapidly classify large volumes of multiscale remotely sensed images and to perform necessary analysis to detect changes on the time series. It shows that processing on three different scales of Landsat 8 data (up to ~107.4 GB, five-scene, time series image sets) can be accomplished in 1018 seconds on local cloud environment. Applying the same framework with slight parameter adjustments, it processed same coverage MODIS data in 54 seconds on commercial cloud platform. Theoretically, the proposed scheme can handle all forms of remote sensing imagery commonly used in the Earth and environmental sciences, requiring only minor adjustments in parameterization of the computing jobs to adjust to the data. The authors suggest that the “Spark sensing” approach could provide the flexibility, extensibility, and accessibility necessary to keep inquiry in the Earth and environmental sciences at pace with developments in data provision.

Beyond article publishing - support and opportunities for researchers in FAIR data sharing

2020 ◽  
Author(s):  
Graham Smith ◽  
Andrew Hufton
Keyword(s):  
Data Sharing ◽  
San Francisco ◽  
Scientific Data ◽  
Research Data ◽  
Research Assessment ◽  
Environmental Sciences ◽  
Data Repositories ◽  
The Earth ◽  
Data Citation ◽  
The Impact

<p>Researchers are increasingly expected by funders and journals to make their data available for reuse as a condition of publication. At Springer Nature, we feel that publishers must support researchers in meeting these additional requirements, and must recognise the distinct opportunities data holds as a research output. Here, we outline some of the varied ways that Springer Nature supports research data sharing and report on key outcomes.</p><p>Our staff and journals are closely involved with community-led efforts, like the Enabling FAIR Data initiative and the COPDESS 2014 Statement of Commitment <sup>1-4</sup>. The Enabling FAIR Data initiative, which was endorsed in January 2019 by <em>Nature</em> and <em>Scientific Data</em>, and by <em>Nature Geoscience</em> in January 2020, establishes a clear expectation that Earth and environmental sciences data should be deposited in FAIR<sup>5</sup> Data-aligned community repositories, when available (and in general purpose repositories otherwise). In support of this endorsement, <em>Nature</em> and <em>Nature Geoscience</em> require authors to share and deposit their Earth and environmental science data, and <em>Scientific Data</em> has committed to progressively updating its list of recommended data repositories to help authors comply with this mandate.</p><p>In addition, we offer a range of research data services, with various levels of support available to researchers in terms of data curation, expert guidance on repositories and linking research data and publications.</p><p>We appreciate that researchers face potentially challenging requirements in terms of the ‘what’, ‘where’ and ‘how’ of sharing research data. This can be particularly difficult for researchers to negotiate given that huge diversity of policies across different journals. We have therefore developed a series of standardised data policies, which have now been adopted by more than 1,600 Springer Nature journals. </p><p>We believe that these initiatives make important strides in challenging the current replication crisis and addressing the economic<sup>6</sup> and societal consequences of data unavailability. They also offer an opportunity to drive change in how academic credit is measured, through the recognition of a wider range of research outputs than articles and their citations alone. As signatories of the San Francisco Declaration on Research Assessment<sup>7</sup>, Nature Research is committed to improving the methods of evaluating scholarly research. Research data in this context offers new mechanisms to measure the impact of all research outputs. To this end, Springer Nature supports the publication of peer-reviewed data papers through journals like <em>Scientific Data</em>. Analysis of citation patterns demonstrate that data papers can be well-cited, and offer a viable way for researchers to receive credit for data sharing through traditional citation metrics. Springer Nature is also working hard to improve support for direct data citation. In 2018 a data citation roadmap developed by the Publishers Early Adopters Expert Group was published in <em>Scientific Data</em><sup>8</sup>, outlining practical steps for publishers to work with data citations and associated benefits in transparency and credit for researchers. Using examples from this roadmap, its implementation and supporting services, we outline how a FAIR-led data approach from publishers can help researchers in the Earth and environmental sciences to capitalise on new expectations around data sharing.</p><p>__</p><ol><li>https://doi.org/10.1038/d41586-019-00075-3</li> <li>https://doi.org/10.1038/s41561-019-0506-4</li> <li>https://copdess.org/enabling-fair-data-project/commitment-statement-in-the-earth-space-and-environmental-sciences/</li> <li>https://copdess.org/statement-of-commitment/</li> <li>https://www.force11.org/group/fairgroup/fairprinciples</li> <li>https://op.europa.eu/en/publication-detail/-/publication/d375368c-1a0a-11e9-8d04-01aa75ed71a1</li> <li>https://sfdora.org/read/</li> <li>https://doi.org/10.1038/sdata.2018.259</li> </ol>

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