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Project: GCOS Earth Heat Inventory - A study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory (EHI), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period from 1960 to present. Summary: The file “GCOS_EHI_1960-2020_Earth_Heat_Inventory_Ocean_Heat_Content_data.nc” contains a consistent long-term Earth system heat inventory over the period 1960-2020. Human-induced atmospheric composition changes cause a radiative imbalance at the top-of-atmosphere which is driving global warming. Understanding the heat gain of the Earth system from this accumulated heat – and particularly how much and where the heat is distributed in the Earth system - is fundamental to understanding how this affects warming oceans, atmosphere and land, rising temperatures and sea level, and loss of grounded and floating ice, which are fundamental concerns for society. This dataset is based on a study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory published in von Schuckmann et al. (2020), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period 1960-2020. The dataset also contains estimates for global ocean heat content over 1960-2020 for different depth layers, i.e., 0-300m, 0-700m, 700-2000m, 0-2000m, 2000-bottom, which are described in von Schuckmann et al. (2022). This version includes an update of heat storage of global ocean heat content, where one additional product (Li et al., 2022) had been included to the initial estimate. The Earth heat inventory had been updated accordingly, considering also the update for continental heat content (Cuesta-Valero et al., 2023).

The radiative capture cross section of a highly pure (99.999%), 6.125(2) grams and 9.56(5)E-4 atoms/barn areal density 238U sample has been measured with the Total Absorption Calorimeter (TAC) in the 185 m flight path at the CERN neutron time-of-flight facility n_TOF. This measurement is in response to the NEA High Priority Request list, which demands an accuracy in this cross section of less than 3% below 25 keV. These data have undergone careful background subtraction, with special care being given to the background originating from neutrons scattered by the 238U sample. Pileup and dead-time effects have been corrected for. The measured cross section covers an energy range between 0.2 eV and 80 keV, with an accuracy that varies with neutron energy, being better than 4% below 25 keV and reaching at most 6% at higher energies.

{"references": ["Adam, David. 2020. \"Special Report: The Simulations Driving the World's Response to COVID-19.\" Nature 580 (7803): 316\u201318. doi.org/10.1038/d41586-020-01003-6.", "Akhmerov, Anton, Maria Cruz, Niels Drost, Cees Hof, Tomas Knapen, Mateusz Kuzak, Carlos Martinez-Ortiz, Yasemin Turkyilmaz-van der Velden, and Ben van Werkhoven. 2019. \"Raising the Profile of Research Software,\" August. https://doi.org/10.5281/ZENODO.3378572.", "Barton, C. Michael, Marina Alberti, Daniel Ames, Jo-An Atkinson, Jerad Bales, Edmund 5 Burke, Min Chen, et al. 2020. \"Call for Transparency of COVID-19 Models.\" Edited by Jennifer Sills. Science 368 (6490): 482.2-483. https://doi.org/10.1126/science.abb8637.", "Carmack, John. n.d. \"'The Imperial College Epidemic Simulation Code That I Helped a Little on Is Now Public:' / Twitter.\" Twitter. Accessed May 6, 2020. https://twitter.com/id_aa_carmack/status/1254872368763277313.", "Carver, Jeffrey C., Sandra Gesing, Daniel S. Katz, Karthik Ram, and Nicholas Weber. 2018. \"Conceptualization of a US Research Software Sustainability Institute (URSSI).\" Computing in Science & Engineering 20 (3): 4\u20139. https://doi.org/10.1109/MCSE.2018.03221924.", "Cl\u00e9ment-Fontaine, M\u00e9lanie, Roberto Di Cosmo, Bastien Guerry, Patrick MOREAU, and Fran\u00e7ois Pellegrini. 2019. \"Encouraging a Wider Usage of Software Derived from Research.\" Research Report. https://hal.archives-ouvertes.fr/hal-02545142.", "Jim\u00e9nez, Rafael C., Mateusz Kuzak, Monther Alhamdoosh, Michelle Barker, B\u00e9r\u00e9nice Batut, Mikael Borg, Salvador Capella-Gutierrez, et al. 2017. \"Four Simple Recommendations to Encourage Best Practices in Research Software.\" F1000Research 6 (June): 876. https://doi.org/10.12688/f1000research.11407.1.", "Krylov, Anna, Theresa L. Windus, Taylor Barnes, Eliseo Marin-Rimoldi, Jessica A. Nash, Benjamin Pritchard, Daniel G. A. Smith, et al. 2018. \"Perspective: Computational Chemistry Software and Its Advancement as Illustrated through Three Grand Challenge Cases for Molecular Science.\" The Journal of Chemical Physics 149 (18): 180901. https://doi.org/10.1063/1.5052551.", "NSF. 2017. \"Software Infrastructure for Sustained Innovation (SSE, SSI, S2I2): Software Elements, Frameworks and Institute Conceptualizations.\" 2017. https://www.nsf.gov/publications/pub_summ.jsp?ods_key=nsf17526.", "Research Data Alliance. 2020. \"RDA COVID-19 Guidelines and Recommendations.\" RDA. April 23, 2020. https://www.rd-alliance.org/group/rda-covid19-rda-covid19- omics-rda-covid19-epidemiology-rda-covid19-clinical-rda-covid19-0.", "Research Data Alliance. 2020. \"FAIR4RS WG.\" April 28, 2020. https://www.rd-alliance.org/groups/fair- 4-research-software-fair4rs-wg.", "Sheehan, Jeremy. 2016. \"Increasing Access to the Results of Federally Funded Science.\" Whitehouse.Gov. February 22, 2016. https://obamawhitehouse.archives.gov/blog/2016/02/22/increasing-accessresults- federally-funded-science.", "Smith, Arfon M., Daniel S. Katz, Kyle E. Niemeyer, and FORCE11 Software Citation Working Group. 2016. \"Software Citation Principles.\" PeerJ Computer Science 2: e86. https://doi.org/10.7717/peerj-cs.86.", "The HEP Software Foundation, Johannes Albrecht, Antonio Augusto Alves, Guilherme Amadio, Giuseppe Andronico, Nguyen Anh-Ky, Laurent Aphecetche, et al. 2019. \"A Roadmap for HEP Software and Computing R&D for the 2020s.\" Computing and Software for Big Science 3 (1): 7. doi.org/10.1007/s41781-018-0018-8.", "Wilkins-Diehr, Nancy, Michael Zentner, Marlon Pierce, Maytal Dahan, Katherine Lawrence, Linda Hayden, and Nayiri Mullinix. 2018. \"The Science Gateways Community Institute at Two Years.\" In Proceedings of the Practice and Experience on Advanced Research Computing, 1\u20138. Pittsburgh PA USA: ACM. https://doi.org/10.1145/3219104.3219142."]} The Research Software Alliance (ReSA) welcomes this opportunity to inform approaches for ensuring broad public access to the peer-reviewed scholarly publications, data, and code that result from federally-funded scientific research. This submission focuses on how improving the recognition and value of research software can increase the access to unclassified published research, digital scientific data, and code supported by the US Government. ReSA is the international organization representing the research software community. ReSA’s vision is that research software be recognized and valued as a fundamental and vital component of research worldwide.

Many governments who preside over liberalised energy markets are developing policies aimed at promoting investment in renewable generation whilst maintaining the level of security of supply customers have come to expect. Of particular interest is the mix and amount of generation investment over time in response to policies promoting high penetrations of variable output renewable power such as wind. Modelling the dynamics of merchant generation investment in market environments can inform the debate. Such models need improved methods to calculate expected output, costs and revenue of thermal generation subject to varying load and random independent thermal outages in a power system with high penetrations of wind. This paper presents a dynamic simulation model of the aggregated Great Britain (GB) generation investment market. The short-term energy market is simulated using probabilistic production costing based on the Mix of Normals distribution technique with a residual load calculation (load net of wind output). Price mark-ups due to market power are accounted for. These models are embedded in a dynamic model in which generation companies use a Value at Risk (VaR) criterion for investment decisions. An `energy-only' market setting is used to estimate the economic profitability of investments and forecast the evolution of security of supply. Simulated results for the GB market case study show a pattern of increased relative security of supply risk during the 2020s. In addition, fixed cost recovery for many new investments can only occur during years in which more frequent supply shortages push energy prices higher. A sensitivity analyses on a number of key model assumptions provides insight into factors affecting the simulated timing and level of generation investment. This is achieved by considering the relative change in simulated levels of security of supply risk metric such as de-rated capacity margins and expected energy unserved. The model can be used as a decision support tool in policy design, in particular how to address the increased `energy-only market revenue risk facing thermal generation, particularly peaking units, that rely on a small number of high price periods to recover fixed costs and make adequate returns on investment.

The short list of practices is the result of literature review and expert panel input and a step wise process of considering certain critical measures (e.g. increase of soil organic carbon (SOC); greenhouse gas (GHG) emission reduction e.g. carbon dioxide, nitrous oxide, methane; system integration). The list is based on a range of practices already proposed to deliver soil carbon sequestration (SCS). First, specific practices were identified with potential for both a positive impact on SCS at farm level and an uptake rate compatible with global impact. These focus on: (a) optimising crop primary productivity; (b) reducing soil disturbance and managing soil physical properties; (c) minimising deliberate removal of C or lateral transport via erosion; (d) addition of C produced outside the system; (e) provision of additional C inputs within the cropping system. Then, economic and non‐cost barriers and incentives for land managers are considered, along with the potential externalised impacts of implementation. The provided data presents a list of greenhouse gas removal practices for soil organic carbon sequestration, which are suitable under biophysical, economic and social consideration. The list is the result of the first step in analysing the potential of agricultural soils to sequester carbon globally and is part of the NERC funded project Soils-R-GGREAT (NE/P019455/1). The work is based on literature research and expert panel and judgements. The work was supported by the Natural Environment Research Council (NE/P019455/1)

Increasingly frequent and intense heatwaves generate new challenges for many organisms. Our understanding of the ecological predictors of thermal vulnerability is improving, yet, at least in endotherms, we are still only beginning to understand one critical component of predicting resilience: exactly how do wild animals cope with sub-lethal heat? In wild endotherms, most prior work focuses on one or a few traits, leaving uncertainty about organismal consequences of heatwaves. Here, we experimentally generated a 2.8 °C heatwave for free-living nestling tree swallows (Tachycineta bicolor). Over a week-long period coinciding with the peak of post-natal growth, we quantified a suite of traits to test the hypotheses that (a) behavioral or (b) physiological responses may be sufficient for coping with inescapable heat. Heat-exposed nestlings increased panting and decreased huddling, but treatment effects on panting dissipated over time, even though heat-induced temperatures remained elevated. Physiologically, we found no effects of heat on: gene expression of three heat shock proteins in blood, muscle, and three brain regions; secretion of circulating corticosterone at baseline or in response to handling; and telomere length. Moreover, heat had a positive effect on growth and a marginal, but not significant, positive effect on subsequent recruitment. These results suggest that nestlings were generally buffered from deleterious effects of heat, with one exception: heat-exposed nestlings exhibited lower gene expression for superoxide dismutase, a key antioxidant defense. Despite this one apparent cost, our thorough organismal investigation indicates general resilience to a heatwave that may, in part, stem from behavioral buffering and acclimation. Our approach provides a mechanistic framework that we hope will improve understanding of species persistence in the face of climate change. See manuscript. 

This paper presents the electron and photon energy calibration achieved with the ATLAS detector using about 25 fb −1 of LHC proton–proton collision data taken at centre-of-mass energies of s√=7 and 8 TeV. The reconstruction of electron and photon energies is optimised using multivariate algorithms. The response of the calorimeter layers is equalised in data and simulation, and the longitudinal profile of the electromagnetic showers is exploited to estimate the passive material in front of the calorimeter and reoptimise the detector simulation. After all corrections, the Z resonance is used to set the absolute energy scale. For electrons from Z decays, the achieved calibration is typically accurate to 0.05 % in most of the detector acceptance, rising to 0.2 % in regions with large amounts of passive material. The remaining inaccuracy is less than 0.2–1 % for electrons with a transverse energy of 10 GeV, and is on average 0.3 % for photons. The detector resolution is determined with a relative inaccuracy of less than 10 % for electrons and photons up to 60 GeV transverse energy, rising to 40 % for transverse energies above 500 GeV.

Biodiversity continues to decline in the face of increasing anthropogenic pressures such as habitat destruction, exploitation, pollution and introduction of alien species. Existing global databases of species' threat status or population time series are dominated by charismatic species. The collation of datasets with broad taxonomic and biogeographic extents, and that support computation of a range of biodiversity indicators, is necessary to enable better understanding of historical declines and to project - and avert - future declines. We describe and assess a new database of more than 1.6 million samples from 78 countries representing over 28,000 species, collated from existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from terrestrial sites around the world. The database contains measurements taken in 208 (of 814) ecoregions, 13 (of 14) biomes, 25 (of 35) biodiversity hotspots and 16 (of 17) megadiverse countries. The database contains more than 1% of the total number of all species described, and more than 1% of the described species within many taxonomic groups - including flowering plants, gymnosperms, birds, mammals, reptiles, amphibians, beetles, lepidopterans and hymenopterans. The dataset, which is still being added to, is therefore already considerably larger and more representative than those used by previous quantitative models of biodiversity trends and responses. The database is being assembled as part of the PREDICTS project (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems - http://www.predicts.org.uk). We make site-level summary data available alongside this article. The full database will be publicly available in 2015.