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Supplemental Figure S3. Variation of the His plasma concentration (μmol/L) over 6 time points in the experiment for each treatment group (CTRL, MetLys, MetLysHis).
<p><strong>Supplemental Figure S3.</strong> Variation of the His plasma concentration (μmol/L) over 6 time points in the experiment for each treatment group (CTRL, MetLys, MetLysHis). Day 1 is the reference, d18 and d29 are in the depletion period, d54 and d79 in the RP-AA period and d113 in the cross-back period. Day 29, d79 and d113 also represent the last d of each period. During the whole experiment all treatment groups received a low protein diet (CTRL). The cows in the MetLys group received rumen-protected (RP) Met (Excential Rumenpass MET, Orffa Additives) and RP-Lys (AjiPro-L, Ajinomoto H&N) during the RP-AA period. The cows in the MetLysHis group received RP-Met, RP-Lys and RP-His (experimental RP-His product, Ajinomoto Co.) during the RP-AA period. Significant differences (<em>P</em> ≤ 0.05) between groups within a d are indicated by different letters as determined by Tukey’s test.</p>
Supplemental Figure S2. Variation of the Lys plasma concentration (μmol/L) over 6 time points in the experiment for each treatment group (CTRL, MetLys, MetLysHis).
<p><strong>Supplemental Figure S2.</strong> Variation of the Lys plasma concentration (μmol/L) over 6 time points in the experiment for each treatment group (CTRL, MetLys, MetLysHis). Day 1 is the reference, d18 and d29 are in the depletion period, d54 and d79 in the RP-AA period and d113 in the cross-back period. Day 29, d79 and d113 also represent the last d of each period. During the whole experiment all treatment groups received a low protein diet (CTRL). The cows in the MetLys group received rumen-protected (RP) Met (Excential Rumenpass MET, Orffa Additives) and RP-Lys (AjiPro-L, Ajinomoto H&N) during the RP-AA period. The cows in the MetLysHis group received RP-Met, RP-Lys and RP-His (experimental RP-His product, Ajinomoto Co.) during the RP-AA period. Significant differences (<em>P</em> ≤ 0.05) between groups within a d are indicated by different letters as determined by Tukey’s test.</p>
Supplemental Figure S1. Variation of the Met plasma concentration (μmol/L) over 6 time points in the experiment for each treatment group (CTRL, MetLys, MetLysHis).
<p><strong>Supplemental Figure S1.</strong> Variation of the Met plasma concentration (μmol/L) over 6 time points in the experiment for each treatment group (CTRL, MetLys, MetLysHis). Day 1 is the reference, d18 and d29 are in the depletion period, d54 and d79 in the RP-AA period and d113 in the cross-back period. Day 29, d79 and d113 also represent the last d of each period. During the whole experiment all treatment groups received a low protein diet (CTRL). The cows in the MetLys group received rumen-protected (RP) Met (Excential Rumenpass MET, Orffa Additives) and RP-Lys (AjiPro-L, Ajinomoto H&N) during the RP-AA period. The cows in the MetLysHis group received RP-Met, RP-Lys and RP-His (experimental RP-His product, Ajinomoto Co.) during the RP-AA period. Significant differences (<em>P</em> ≤ 0.05) between groups within a d are indicated by different letters as determined by Tukey’s test.</p>
Genomic analyses point to a low evolutionary potential of prospective source populations for assisted migration in a forest herb
<p>Climate change is increasingly impacting temperate forest ecosystems and many forest herbs might be unable to track the changing climate due to dispersal limitation. Forest herbs with a low adaptive capacity may therefore benefit from conservation strategies that mitigate dispersal limitation and evolutionary constraints, such as assisted migration. However, assisted migration strategies rarely consider evolutionary constraints of potential source populations that may jeopardize their success. In cases where climate adaptation is overshadowed by competing evolutionary processes, assisted migration is unlikely to support adaptation to future climates. Using a combination of population and landscape genomic analyses, we disentangled local adaptation drivers and quantified the adaptability and vulnerability to climate change of the self-incompatible deciduous forest herb <em>Primula</em> <em>elatior</em>. Southern populations displayed a sharp genetic turnover, and a considerable amount of local adaptation under diversifying selection was discovered. However, most of the outlier loci could not be linked to climate variables (71%) and were likely related to other local adaptation drivers, such as photoperiodism. Furthermore, specific adaptations to climate extremes, such as drought stress, could not be detected. This is in line with the typical occurrence of forest herbs in buffered climatic conditions, which can be expected to reduce selection pressures imposed by climate. Finally, populations in the south of the distribution area had increased sensitivity to climate change due to a reduced adaptive capacity and a moderate genetic offset, while central European populations were sensitive due to a high genetic offset. We conclude that assisted migration from southern source populations could bear significant risk due to nonclimatic maladaptation and a low adaptive capacity. Regional admixture and restoration of ecological connectivity to increase the adaptive capacity, and assisted range expansion to suitable habitat in the north might be more appropriate mitigation strategies.</p>
Correlated k coefficients for H2-He atmospheres; 196 spectral windows and 1460 pressure-temperature points
<p>There are 108 correlated k-coefficients datasets, using the naming convention sonora_2020_fehxxxx_co_yyy.data.196.tar.gz (78 models) or sonora_2020_fehxxxx_co_yyy_noTiOVO.data.196.tar.gz (78 models), where xxxx is the metallicity in 10x dex relative to solar, and yyy is the 100x C/O ratio relative to solar, as a multiplication factor. For example a metallicity of +000 and a C/O ratio of 100 indicates solar abundances, feh+070 should be read as a metallicity of +0.7 dex, feh-100 as -1.0 dex, co_025 should be read as 0.25x C/O relative to solar, and co_200 as 2x C/O relative to solar. We use the Lodders et al. 2010 value for the solar C/O=0.458. The files with “noTiOVO” in the filename contain the correlated k-coefficients calculated without the opacity of TiO and VO. The rest of the molecular abundances and opacities are the same as the equilibrium chemistry values in the regular files. These files are useful for calculating models without any TiO- and VO-induced temperature inversion in the atmosphere.</p> <p>The correlated-k coefficients are calculated using pre-mixed opacities, with abundances given by equilibrium chemistry for each metallicity-C/O combination, as described in Marley et al. 2021. There are 13 Fe/H values: 0.0, 0.3, 0.5, 0.7, 1.0, 1.3, 1.5, 1.7, 2.0, -0.3, -0.5, -0.7, and -1.0; and 6 C/O values: 0.25, 0.5, 1.0, 1.5, 2.0 and 2.5. The k-coefficients are calculated for a grid of 1460 pressure-temperature points, from10^−6 to 3000 bar and from 75 to 4000 K, listed in the file 1460_layer_list, and can be read in using the IDL script read_k_coefficients.pro. The spectral windows are listed in the file 196_windows.txt (intervals defined as starting at lambda1 and ending at lambda2). NB Please note that for metallicities between 1.3 and 2.0 the value of <em>max_windows</em> has changed from 200 to 1000.</p> <p>The opacity sources included in the calculations are: C2H2, C2H4, C2H6, CH4, CO, CO2, CrH, Fe, FeH, H2, H3+, H2O, H2S, HCN, LiCl, LiF, LiH, MgH, N2, NH3, OCS, PH3, SiO, TiO, and VO, in addition to alkali metals (Li, Na, K, Rb, Cs). The corresponding high resolution opacities for these atoms and molecules can be found in the Zenodo repository 10.5281/zenodo.6600976. The references for the line lists used in these opacity calculations are listed in the file Opacity_references_2021.pdf. Please include these references, as well as the reference to this Zenodo repository when publishing your paper.</p> <p>Each dataset contains the following files:</p> <p>ascii_data: the correlated k coefficients file in ascii format. This can be read by the included IDL code.</p> <p>binary_data: the correlated k coefficients file in binary format</p> <p>full_abunds: the relative abundances for all the species from the chemistry files, on the 1460-point pressure-temperature grid</p> <p>sum_in_atoms: relative abundances for the alkali metals</p> <p>sum_in_layer: relative abundances for all molecules included in the correlated k-coefficients calculations</p> <p><em>Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center.</em></p>
Correlated k coefficients for H2-He atmospheres; 180 spectral windows and 1460 pressure-temperature points
<p>There are 108 correlated k-coefficients datasets, using the naming convention sonora_2020_fehxxxx_co_yyy.data.196.tar.gz (78 models) or sonora_2020_fehxxxx_co_yyy_noTiOVO.data.196.tar.gz (78 models), where xxxx is the metallicity in 10x dex relative to solar, and yyy is the 100x C/O ratio relative to solar, as a multiplication factor. For example a metallicity of +000 and a C/O ratio of 100 indicates solar abundances, feh+070 should be read as a metallicity of +0.7 dex, feh-100 as -1.0 dex, co_025 should be read as 0.25x C/O relative to solar, and co_200 as 2x C/O relative to solar. We use the Lodders et al. 2010 value for the solar C/O=0.458. The files with “noTiOVO” in the filename contain the correlated k-coefficients calculated without the opacity of TiO and VO. The rest of the molecular abundances and opacities are the same as the equilibrium chemistry values in the regular files. These files are useful for calculating models without any TiO- and VO-induced temperature inversion in the atmosphere.</p> <p>The correlated-k coefficients are calculated using pre-mixed opacities, with abundances given by equilibrium chemistry for each metallicity-C/O combination, as described in Marley et al. 2021. There are 13 Fe/H values: 0.0, 0.3, 0.5, 0.7, 1.0, 1.3, 1.5, 1.7, 2.0, -0.3, -0.5, -0.7, and -1.0; and 6 C/O values: 0.25, 0.5, 1.0, 1.5, 2.0 and 2.5. The k-coefficients are calculated for a grid of 1460 pressure-temperature points, from10^−6 to 3000 bar and from 75 to 4000 K, listed in the file 1460_layer_list, and can be read in using the IDL script read_k_coefficients.pro. The spectral windows are listed in the file 180_windows.txt (intervals defined as starting at lambda1 and ending at lambda2). NB Please note that for metallicities between 1.3 and 2.0 the value of <em>max_windows</em> has changed from 200 to 1000.</p> <p>The opacity sources included in the calculations are: C2H2, C2H4, C2H6, CH4, CO, CO2, CrH, Fe, FeH, H2, H3+, H2O, H2S, HCN, LiCl, LiF, LiH, MgH, N2, NH3, OCS, PH3, SiO, TiO, and VO, in addition to alkali metals (Li, Na, K, Rb, Cs). The corresponding high resolution opacities for these atoms and molecules can be found in the Zenodo repository 10.5281/zenodo.6600976. The references for the line lists used in these opacity calculations are listed in the file Opacity_references_2021.pdf. Please include these references, as well as the reference to this Zenodo repository when publishing your paper.</p> <p>Each dataset contains the following files:</p> <p>ascii_data: the correlated k coefficients file in ascii format. This can be read by the included IDL code.</p> <p>binary_data: the correlated k coefficients file in binary format</p> <p>full_abunds: the relative abundances for all the species from the chemistry files, on the 1460-point pressure-temperature grid</p> <p>sum_in_atoms: relative abundances for the alkali metals</p> <p>sum_in_layer: relative abundances for all molecules included in the correlated k-coefficients calculations</p> <p><em>Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center.</em></p>
Correlated k coefficients for H2-He atmospheres; 11 spectral windows and 1460 pressure-temperature points
<p>There are 108 correlated k-coefficients datasets, using the naming convention sonora_2020_fehxxxx_co_yyy.data.196.tar.gz (78 models) or sonora_2020_fehxxxx_co_yyy_noTiOVO.data.196.tar.gz (78 models), where xxxx is the metallicity in 10x dex relative to solar, and yyy is the 100x C/O ratio relative to solar, as a multiplication factor. For example a metallicity of +000 and a C/O ratio of 100 indicates solar abundances, feh+070 should be read as a metallicity of +0.7 dex, feh-100 as -1.0 dex, co_025 should be read as 0.25x C/O relative to solar, and co_200 as 2x C/O relative to solar. We use the Lodders et al. 2010 value for the solar C/O=0.458. The files with “noTiOVO” in the filename contain the correlated k-coefficients calculated without the opacity of TiO and VO. The rest of the molecular abundances and opacities are the same as the equilibrium chemistry values in the regular files. These files are useful for calculating models without any TiO- and VO-induced temperature inversion in the atmosphere.</p> <p>The correlated-k coefficients are calculated using pre-mixed opacities, with abundances given by equilibrium chemistry for each metallicity-C/O combination, as described in Marley et al. 2021. There are 13 Fe/H values: 0.0, 0.3, 0.5, 0.7, 1.0, 1.3, 1.5, 1.7, 2.0, -0.3, -0.5, -0.7, and -1.0; and 6 C/O values: 0.25, 0.5, 1.0, 1.5, 2.0 and 2.5. The k-coefficients are calculated for a grid of 1460 pressure-temperature points, from10^−6 to 3000 bar and from 75 to 4000 K, listed in the file 1460_layer_list, and can be read in using the IDL script read_k_coefficients.pro. The spectral windows are listed in the file 11_windows.txt (intervals defined as starting at lambda1 and ending at lambda2). NB Please note that for metallicities between 1.3 and 2.0 the value of <em>max_windows</em> has changed from 200 to 1000.</p> <p>The opacity sources included in the calculations are: C2H2, C2H4, C2H6, CH4, CO, CO2, CrH, Fe, FeH, H2, H3+, H2O, H2S, HCN, LiCl, LiF, LiH, MgH, N2, NH3, OCS, PH3, SiO, TiO, and VO, in addition to alkali metals (Li, Na, K, Rb, Cs). The corresponding high resolution opacities for these atoms and molecules can be found in the Zenodo repository 10.5281/zenodo.6600976. The references for the line lists used in these opacity calculations are listed in the file Opacity_references_2021.pdf. Please include these references, as well as the reference to this Zenodo repository when publishing your paper.</p> <p>Each dataset contains the following files:</p> <p>ascii_data: the correlated k coefficients file in ascii format. This can be read by the included IDL code.</p> <p>binary_data: the correlated k coefficients file in binary format</p> <p>full_abunds: the relative abundances for all the species from the chemistry files, on the 1460-point pressure-temperature grid</p> <p>sum_in_atoms: relative abundances for the alkali metals</p> <p>sum_in_layer: relative abundances for all molecules included in the correlated k-coefficients calculations</p> <p><em>Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center.</em></p>
Correlated k coefficients for H2-He atmospheres; 30 spectral windows and 1460 pressure-temperature points
<p>There are 108 correlated k-coefficients datasets, using the naming convention sonora_2020_fehxxxx_co_yyy.data.196.tar.gz (78 models) or sonora_2020_fehxxxx_co_yyy_noTiOVO.data.196.tar.gz (78 models), where xxxx is the metallicity in 10x dex relative to solar, and yyy is the 100x C/O ratio relative to solar, as a multiplication factor. For example a metallicity of +000 and a C/O ratio of 100 indicates solar abundances, feh+070 should be read as a metallicity of +0.7 dex, feh-100 as -1.0 dex, co_025 should be read as 0.25x C/O relative to solar, and co_200 as 2x C/O relative to solar. We use the Lodders et al. 2010 value for the solar C/O=0.458. The files with “noTiOVO” in the filename contain the correlated k-coefficients calculated without the opacity of TiO and VO. The rest of the molecular abundances and opacities are the same as the equilibrium chemistry values in the regular files. These files are useful for calculating models without any TiO- and VO-induced temperature inversion in the atmosphere.</p> <p>The correlated-k coefficients are calculated using pre-mixed opacities, with abundances given by equilibrium chemistry for each metallicity-C/O combination, as described in Marley et al. 2021. There are 13 Fe/H values: 0.0, 0.3, 0.5, 0.7, 1.0, 1.3, 1.5, 1.7, 2.0, -0.3, -0.5, -0.7, and -1.0; and 6 C/O values: 0.25, 0.5, 1.0, 1.5, 2.0 and 2.5. The k-coefficients are calculated for a grid of 1460 pressure-temperature points, from10^−6 to 3000 bar and from 75 to 4000 K, listed in the file 1460_layer_list, and can be read in using the IDL script read_k_coefficients.pro. The spectral windows are listed in the file 30_windows.txt (intervals defined as starting at lambda1 and ending at lambda2). NB Please note that for metallicities between 1.3 and 2.0 the value of <em>max_windows</em> has changed from 200 to 1000.</p> <p>The opacity sources included in the calculations are: C2H2, C2H4, C2H6, CH4, CO, CO2, CrH, Fe, FeH, H2, H3+, H2O, H2S, HCN, LiCl, LiF, LiH, MgH, N2, NH3, OCS, PH3, SiO, TiO, and VO, in addition to alkali metals (Li, Na, K, Rb, Cs). The corresponding high resolution opacities for these atoms and molecules can be found in the Zenodo repository 10.5281/zenodo.6600976. The references for the line lists used in these opacity calculations are listed in the file Opacity_references_2021.pdf. Please include these references, as well as the reference to this Zenodo repository when publishing your paper.</p> <p>Each dataset contains the following files:</p> <p>ascii_data: the correlated k coefficients file in ascii format. This can be read by the included IDL code.</p> <p>binary_data: the correlated k coefficients file in binary format</p> <p>full_abunds: the relative abundances for all the species from the chemistry files, on the 1460-point pressure-temperature grid</p> <p>sum_in_atoms: relative abundances for the alkali metals</p> <p>sum_in_layer: relative abundances for all molecules included in the correlated k-coefficients calculations</p> <p><em>Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center.</em></p>
Dataset related to the publication "An invar-based fabry-perot cavity refractometer with a gallium fixed-point cell for assessment of pressure"
<p>The data set consists of; The published paper, all figures that present measurement or simulation data in .png and .fig format and the underlying data plotted in the figures in text format. The published plots were generated from the fig files. The text files were generated by reading the plotted data from the fig files. The files are named Fig_XX were XX corresponds to the figure number in the publication. The format of the text file is as follows. Before every data set there is a header consisting of; The number of the subplot where the data is plotted (Plot: XX), the number of the data set in the sub plot (DataSet: XX), and the color of the line or marker in the plot (Color: XX). The description of what each color represents can be found in the publication.</p>
Examining the Influence of Focal Point Location on Heat Transfer Dynamics in Phase Change Material: A Dataset Publication
<p>Dataset including experimental data from small-scale solar facade module that consists of several components: Fresnel lens; cone-shaped air gap; aerogel (semi-transparent) as insulation layer; PCM container. Variations of the experimental settings in test setups are different cone diameter (2, 3 and 4 cm) and insulation layer thickness is (3,5 and 7cm). Nine different compositions were tested in laboratory to evaluate the ompact of focal point location. The focal point has been described with three different values that reflect its location on the inner and outer surfaces and in the middle of the PCM container. The corresponding figures can be found in the paper The Effect of Fresnel Lens Focal Point Location on Heat Transfer in Phase Change Material (PCM) Enhanced Dynamic Solar Facade.</p> <p>This study has been supported by Fundamental and Applied Research project ‘Smart building EnVElope with solaR Energy STorage (EVEREST)’, project No. lzp-2019/1-0363, funded by the Latvian Council of Science.</p> <p> </p>
Accurate resampling of radial dose point kernels to a Cartesian matrix for voxelwise dose calculation
<p>Note: The version 1.1 dataset has been corrected to include only accurate kernels. Errors were found in Version 1.0 kernels with the following sizes: 1.953, 2.21, 2.4, 3.9, 4, 4.42, 4.8, and 6.8 mm</p> <p>Cartesian matrix dose point kernels, generated by resampling of prior radial dose point kernels. See accompanying paper for additional details: "<strong>Accurate resampling of radial dose point kernels to a Cartesian matrix for voxelwise dose calculation"</strong></p> <p>Refer to the upload titled "<em>Kernel_Format_README.xlsx</em>" for details regarding kernel format and modification for use.</p> <p>Source (radial) kernel generation is described in the Medical Physics article titled "Dose point kernels for 2,174 radionuclides." - https://doi.org/10.1002/mp.13789</p> <p>Source (radial) kernels can be downloaded here: https://doi.org/10.5281/zenodo.2564036</p>
Investigating the Point of View of Project Management Practitioners on Technical Debt - A Study on Stack Exchange
<p>Investigating the Point of View of Project Management Practitioners on Technical Debt - A Study on Stack Exchange.</p> <p>Journal of Software Engineering Research and Development (JSERD) 2023.</p> <p> </p>
Point Reyes National Seashore Dune Insect Records
<p>The insects of the coastal dunes of Abbott's Lagoon at Point Reyes National Seashore were inventoried between 2000 and 2013. The dataset includes information on the specimens and photographic records that are part of this inventory. Details of the study are found in P. da Silva, et al. The insect community of an endangered habitat: coastal dunes at Point Reyes National Seashore, California, U.S.A., which has been accepted by the <em>Pan Pacific Entomologist</em> and will appear in Volume 99, Number 1, to be published in March, 2023.</p>
Influence of Adaptive Coupling Points on Coalition Formation in Multi-Energy Systems: Simulation Result Tables
<p>The dataset contains the result tables used for the paper "Influence of Adaptive Coupling Points on Coalition Formation in Multi-Energy Systems". </p> <p>Short description of the tables:</p> <ul> <li>all_in_one_tab.csv: contains all calculated metrics and attributes of the graph over time and adaptation rate</li> <li>event_tab.csv: contains all toggle events of the coupling points</li> <li>impact_dict.csv: contains the calculated impact values for every coupling point</li> <li>node_region.csv: contains region attributes for every node over adaptation rate</li> <li>static_component_properties.csv: contains static attributes </li> <li>static_node_component_properties.csv: contains static node attributes</li> </ul>
Image Supplement to Hounshell, "Lines, Points, Waves, Streams. Mapping Ethnographic Types in Swiss Hausforschung, 1880s–1950s"
<p><strong>General description</strong>: The images in this repository are provided as a color supplement to the figures printed in black and white or otherwise mentioned in the following article: Eric Hounshell, “Lines, Points, Waves, Streams. Mapping Ethnographic Types in Swiss <em>Hausforschung</em>, 1880s–1950s,” <em>Historische Anthropologie</em> 31:1 (2023), 130-164 [DOI forthcoming]. All images are credited to Jakob Hunziker (1827-1901).</p> <p><strong>Image References</strong>:</p> <p>Fig. 1: Die Verbreitung der Hausformen in der Schweiz, in: Jakob Hunziker, Das Schweizerhaus nach seinen landschaftlichen Formen und seiner geschichtlichen Entwicklung, Aarau 1914, volume 8.</p> <p>Fig. 2: Untitled, in: Folder AG54.198, Jakob Hunziker Nachlass, Staatsarchiv Aargau.</p> <p>Appendix 1: Bedachung, in: Folder AG54.198, Jakob Hunziker Nachlass, Staatsarchiv Aargau.</p> <p>Appendix 2: Untitled, in: Folder AG54.198, Jakob Hunziker Nachlass, Staatsarchiv Aargau.</p> <p>Appendix 3: Untitled, in: Folder AG54.198, Jakob Hunziker Nachlass, Staatsarchiv Aargau.</p>
Data Set "Efficient automatic construction of atom-economical QM regions with point-charge variation analysis"
<p>This data set accompanies the publication "Efficient automatic construction of atom-economical QM regions with point-charge variation analysis" by Felix Brandt and Christoph R. Jacob (TU Braunschweig, Germany) </p> <p>It contains the following files:</p> <p>- PDB files of the reactant and product starting structure</p> <p>- modified AMBER95 force field file</p> <p>- AMS fragment files for the ligands and ions</p> <p>- AMS input files for all geometry optimizations and single point calculations</p>
Submitted Completed pointings to the Gravitational Wave Treasure Map for event TEST_EVENT
Attached in a .json file is the completed pointing information for 1 observation(s) for the EM counterpart search associated with the gravitational wave event TEST_EVENT. These observations were taken on the DLT40 instrument.
Vessel traffic observations and associated waterbird surveys, March 2020 – February 2021, Roberts Point and Ogden Point, Victoria, BC, Canada
<p>The coastal waters of southern British Columbia, Canada, encompass habitat of international conservation significance to coastal and marine birds, including sizeable areas designated in the early 1900s as Migratory Bird Sanctuaries (MBS) to protect overwintering waterfowl from hunting near urban centres. Two of these, Shoal Harbour (SHMBS) and Victoria Harbour (VHMBS), have seen significant marine infrastructure development in recent decades and experience considerable vessel traffic. We conducted a pilot study using shore-based observers to develop small-vessel baselines for the winter months when regional waterbird numbers are highest. During our surveys, we recorded considerable inter-site variability in vessel traffic characteristics, with one site (SHMBS) a source of nearly twice as many vessels in total as the other (VHMBS). For waterbirds, hourly 3-minute 'snapshot' surveys, combined with daily tallies of incidental observations, showed that community composition also varied by locality, with one site dominated by gulls (Laridae), cormorants (Phalacrocoracidae), and seaducks (Tribe Mergini), and the other by gulls, cormorants, and alcids (Alcidae). Our results demonstrate that fine-scale local variability must be taken into account when managing for vessel traffic disturbance of waterbirds, particularly at sites of high human population density and increasing coastal development. Results are in press in PLoS ONE (doi:10.1371/journal.pone.0283791).</p>
2-point statistics covariance with fewer mocks: covariances from various datasets and results of the fits
<p>A set of covariances from random poissonian samples, LRG and ELG, and lognormal mocks, used for plots in the relevant publication.</p>
Glass-Like Random Catalogues for Two-Point Estimates on the Light Cone (Data Repository)
<p>This is the data repository for the article Glass-Like Random Catalogues for Two-Point Estimates on the Light Cone.</p> <p>arxiv link: https://arxiv.org/abs/2304.02040</p> <p>DOI:</p> <p> </p> <p>The file grlic_data.tar.gz contains three directories: /data, /correlations, and /randoms.</p> <p>Inside the /data folder, the data catalogues used in the article are stored: in "cat_part_1162568" the three columns correspond to the redshift, the cosine of the polar angle, and the azimuthal angle, respectively. The first three columns of the "cat_high_1164853" and "cat_low_1164853" catalogues correspond to the same properties for the high-mass and low-mass halos, respectively. In addition to that, these files also contain additional information about the halos: the number of particles (4th column), their M_200b mass (5th column) and their parent ID (6th colum), which is -1 if the halo is not a subhalo.</p> <p>Inside the /randoms folder, the random catalogues for each of the data catalogues in /data are stored. Files beginning with "part" refer to randoms based on the particle catalogue, in a similar fashion the files beginning with "high" correspond to randoms based on the high-mass halo catalogue and those beginning with "low" refer to the randoms based on the low-mass halo catalogue. Files with "..._glass<x>..." correspond to the glass-like random catalogues, and files with "..._rand<x>..." correspond to the Poisson-sampled randoms, where <x> is the value of <span class="math-tex">\(\alpha\)</span> used. <span class="math-tex">\(\alpha\)</span> is the factor by which the number of objects in the data catalogue is multiplied to get the number of objects in the random catalogues, <span class="math-tex">\(N_R = \alpha N_D\)</span>. For the glass-like randoms based on the high-mass halo catalogue, the additional suffix, "..._deltagrid<y>...", refers to the number of grid-cells used in the Zeldovich approximation, where <y> is the number of grid cells, and "..._Niter<z>..." refers to the number of Zeldovich iterations perfomed, which is given by <z>. Similarly to the data catalogues, the three columns represent the redshift, cosine of the polar angle, and the azimuthal angle of each object in the catalogue.</p> <p>Inside the /correlations folder, there are two subdirectories: /correlations/full and /correlations/multipoles. The /correlations/full directory contains the raw output from CUTE for each correlated pair of catalogues. For example, the subdirectory /correlations/full/low_low contains the outputs for the low-mass halo autocorrelation, or the subdirectory /correlations/full/high_part contains the outputs for the cross-correlation between the high-mass halo catalogue and the particle catalogue. The naming convention of these files is "<type><alpha>_<catalogues>_<i>", where <type> can either be "rand" or "glass", for either the Poisson-sampled or glass-like random catalogues, <alpha> is the factor <span class="math-tex">\(\alpha\)</span> already introduced above, <catalogues> again describes which two data catalogues have been cross- (or auto-) correlated, e.g. "highlow" refers to a cross-correlation between the high-mass halo catalogue and the low-mass halo catalogue, and finally <i> is a number between 0 and 19 for the 20 independent measurements of the correlations. For the high-mass halo catalogue autocorrelations involving glass-like randoms, the additional suffix, "..._deltagrid<y>...", refers to the number of grid-cells used in the Zeldovich approximation, where <y> is the number of grid cells, and "..._Niter<z>..." refers to the number of Zeldovich iterations perfomed, which is given by <z>. The format of the files is the standard CUTE format for the 3D correlation using binning in (mu,r), i.e. see the readme of https://github.com/damonge/CUTE/tree/master/CUTE, section 5. It reads:</p> <pre>For the 3-D correlation functions the output file has 7 columns with x1 x2 xi(x1,x2) D1D2(x1,x2) D1R2(x1,x2) R1D2(x1,x2) R1R2(x1,x2) where (x1,x2) is either (pi,sigma) or (mu,r). </pre> <p>The /correlations/multipoles subdirectory contains the estimated mean multipoles and their variance derived from the 20 individual correlation measurements for each data catalogue and type of random catalogue. Similarly to the /correlations/full subdirectory, it contains a separate directory for each pair of data catalogue for which the correlation was estimated. The files are named according to "<multipole>_<type><alpha>_<catalogues>_mean_var", where <multipole> is either l0 for the monopole, l1 for the dipole, or l2 for the quadrupole. The <type>, <alpha> and <catalogues> are identical to what was described above for the /correlations/full subdirectory. Again, for the high-mass halo catalogue autocorrelations involving glass-like randoms, the additional suffix, "..._deltagrid<y>...", refers to the number of grid-cells used in the Zeldovich approximation, where <y> is the number of grid cells, and "..._Niter<z>..." refers to the number of Zeldovich iterations perfomed, which is given by <z>. The columns in each of these files are the comoving separation d, the mean multipole <span class="math-tex">\(\xi_l\)</span>, and its variance <span class="math-tex">\(\sigma^2\)</span>of each bin.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.