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First release of the the MagnetoTellurics time series data publication (MTtsdp) codes. The MTtsdp_v0 codes were developed as part of the Magnetotellurics (MT) component of the 2017-2020 Australian Research Data Commons (ARDC) funded Geoscience Data enhanced Virtual Laboratory (GeoDeVL) project (https://zenodo.org/record/4278997#.ZAgaIuJBzao). The MTtsdp_v1 codes were developed as part of the 2021-2023 Australian Research Data Commons (ARDC), AuScope, NCI, TERN co-funded 2030 Geophysics Collections project: https://doi.org/10.47486/XN002.

Ocean acidification may have deleterious effects on many species, but anticipating long-term changes in the abundance of populations will require an understanding of ocean acidification as an evolutionary force. Here I show that ocean acidification alters natural selection on offspring size and is likely to drive contemporary evolution. In a detailed study of a coastal fish species (California Grunion), I demonstrate that larval mortality is highly sensitive to ocean acidification and that mortality rates are lower for larger larvae. However, these effects are countered by tradeoffs between offspring size and number, suggesting that measurements of maternal fitness are critical for quantifying selection through ocean acidification. Measurements of selection and genetic variation were used to project the evolution of larval size as seawater conditions changed incrementally over many decades. Results for California Grunion suggest that contemporary evolution may offset the projected decline in reproductive success by about 50%. Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: OCE-1948975

Added support for printf-style formatting.

No description provided.

Complete reimplementation of RnaChipIntegrator to unify internal algorithms, simplify usage and substantially update the documentation. No distinction is now made between 'summits' and 'peaks'; the same algorithm is applied in each case. The program always finds the nearest features to each peak, and vice versa. The same distance cutoff and maximum number of hits are applied to both and can be specified using the --cutoff and --number options. By default nearest distances between peaks and features are calculated from the TSS of the feature to whichever of the peak edges are closer; alternatively distances can be calculated between the nearest pair of peak/feature edges by specifying the --edge=both option. Any differential expression flags in the input features file are ignored unless the --only-DE option is specified, in which case only the differentially expressed features are considered in the analyses. By default each peak/feature pair is reported on a separate line; the --compact option reports all nearest features/peaks on a single line of output. Excel output is only produced if the --xls option is specified; summary output is only produced if --summary is specified. The rearrange_columns.py utility has been dropped.

The pdf with the Lakhnawi editon of the Fusus has been converted to plain unicode. Only the original text is retained. Footnotes and page numbers have been removed. From there I made some exports to html and pdf, which are attached. This is just for informational purposes. Later we plain to produce a Text-Fabric version of this text, which will include the exact positions of all words in the original pdf. From there you can get a plain text easily. There might still be some rough edges.

This is the first release of the Feasibility Jump reference implementation and it is supplemental material for the paper: Bjørnar Luteberget, Giorgio Sartor. Feasibility Jump: an LP-free Lagrangian MIP heuristic, Mathematical Programming Computation, 2023.

This zip archive contains the user-study results and associated analysis code for the paper "HEMVIP: Human Evaluation of Multiple Videos in Parallel" presented at the International Conference on Multimodal Interaction (ICMI) 2021; DOI: https://doi.org/10.1145/3462244.3479957. The code was written by Gustav Eje Henter and is released under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.