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Linked collectors and determiners for: The fast-running flies (Diptera, Hybotidae, Tachydromiinae) of Singapore and adjacent regions.
Natural history specimen data linked to collectors and determiners held within, "The fast-running flies (Diptera, Hybotidae, Tachydromiinae) of Singapore and adjacent regions". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/e809cd3a-36bb-413d-9f19-70837d7db34b">https://bionomia.net/dataset/e809cd3a-36bb-413d-9f19-70837d7db34b</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/e809cd3a-36bb-413d-9f19-70837d7db34b">https://gbif.org/dataset/e809cd3a-36bb-413d-9f19-70837d7db34b</a>. Formatted as a Frictionless Data package.
Gradual Pressure Error in an HPLC-HRMS Measurement Run
<p>This dataset contains files of a reverse-phase HPLC-HRMS orbitrap measurement run during which the pump pressure gradually got more unstable. While a run was being recorded, the pump for the organic eluent ceased functioning slowly. This resulted in a high amount of new signals appearing in the form of "stripes" at specific retention times.</p> <p>It is intended as a control group for assigned data quality scores, with the reasoning that a highly unstable pressure in the chromatography will lead to more purely noise-induced signals. The relevant pressure profiles are also included. The mzML files were produced from the raw data using MSconvert and only contain the MS1 spectra.</p> <p>The last file in the series (<a href="https://zenodo.org/api/records/14046512/draft/files/20240805_AA_DK_Ibu_pH6_1min_20240808161549.raw/content" target="_blank" rel="noopener noreferrer">20240805_AA_DK_Ibu_pH6_1min_20240808161549</a>) remains incomplete since the instrument shut off halfway through the measurement.</p>
Hydrodynamic daily climatology of South Eastern Australia based on a 22 year run of the SEA-COFS model
<p>This dataset is a daily climatology created based on a 22 year free run of the SEA-COFS model full domain (EAC 25.25 - 45.55 °S) forced with BARRA-R winds and tides, spanning from January 1994 to September 2016. The model has 30 sigma-stretch vertical levels and an across-shore resolution of 2.5 km (on shelf) – 6 km (off shelf) and 5 km along-shore. The original dataset can be access here:</p> <p>Li, J & Kerry C. (2021). A high-resolution, 22-year, free-running, hydrodynamic simulation of the East Australia Current System using the Regional Ocean Modeling System (Version 2.0). <a href="https://doi.org/10.26190/TT1Q-NP46">https://doi.org/10.26190/TT1Q-NP46</a></p> <p>For a full description of this version of the model and its validation refer to the following papers:</p> <ul> <li><strong>Li, J, Roughan, M. & Kerry, C.</strong> (2021). <a href="https://doi.org/10.1029/2021GL094115%20">Dynamics of interannual eddy kinetic energy modulations in a Western Boundary Current</a>. <em>Geophysical Research Letters,</em>Vol 48, October 2021.DOI: <a href="https://doi.org/10.1029/2021GL094115">https://doi.org/10.1029/2021GL094115</a> <a href="http://www.oceanography.unsw.edu.au/private/publications/2021/2021GL094115.pdf">[PDF file]</a></li> <li> </li> <li><strong>Li, J, Roughan, M. & Kerry, C.</strong> (2022). <a href="https://doi.org/10.1175/JCLI-D-21-0622.1">Variability and Drivers of Ocean Temperature Extremes in a Warming Western Boundary Current</a>. <em>Journal Of Climate,</em> February 2022. DOI: <a href="https://doi.org/10.1175/JCLI-D-21-0622.1">https://doi.org/10.1175/JCLI-D-21-0622.1</a> <a href="http://www.oceanography.unsw.edu.au/private/publications/2022/Li_2022.pdf">[PDF file]</a></li> </ul> <p>This daily climatology of 365 days was created with NCO tools by taking the mean of the 22 ROMS output files (one for each year) containing daily averages, to generate each climatological day. Specific commands used are recorded in the NetCDF file history. Leap year days were excluded since its climatology was computed with only 5 instances.</p> <p>A sample file with the climatological data for January 1st is provided here as an example of the NetCDF format of the dataset. The entire dataset is one file of approximately 62GB and can be provided upon request. </p> <p><strong>NOTE: </strong>The ocean_time variable reflects the dates of the year 1994, but the values of the variables correspond to climatological values computed as described. </p> <p>The ROMS variables below are present in this daily climatology, as well as the S-coordinate stretching curves, grid defining variables and other time independent parameters:</p> <p>AKs = "time-averaged salinity vertical diffusion coefficient" [meter2 seconds-1]</p> <p>AKt = "time-averaged temperature vertical diffusion coefficient" [meter2 seconds-1]</p> <p>AKv = "time-averaged vertical viscosity coefficient" [meter2 seconds-1]</p> <p>bustr = "time-averaged bottom u-momentum stress" [newton meter-2]</p> <p>bvstr = "time-averaged bottom v-momentum stress" [newton meter-2]</p> <p>omega = "time-averaged S-coordinate vertical momentum component" [meter3 second-1]</p> <p>pvorticity = "time-averaged potential vorticity" [meter-1 second-1]</p> <p>pvorticity = "time-averaged 2D potential vorticity" [meter-1 second-1]</p> <p>rho = "time-averaged density anomaly" [kilogram meter-3]</p> <p>rvorticity = "time-averaged relative vorticity, vertical component" [second-1]</p> <p>rvorticity_bar = "time-averaged 2D relative vorticity" [second-1]</p> <p>salt: = "time-averaged salinity" [PSU]</p> <p>shflux = "time-averaged surface net heat flux" [watt meter-2]</p> <p>ssflux = "time-averaged surface net salt flux, (E-P)*SALT" [meter second-1]</p> <p>sustr = "time-averaged surface u-momentum stress" [newton meter-2]</p> <p>svstr = "time-averaged surface v-momentum stress" [newton meter-2]</p> <p>temp = "time-averaged potential temperature" [Celsius]</p> <p>u = "time-averaged u-momentum component" [meter second-1]</p> <p>u_eastward = "time-averaged eastward momentum component at RHO-points" [meter second-1]</p> <p>ubar = "time-averaged vertically integrated u-momentum component" [meter second-1]</p> <p>ubar_eastward = "time-averaged eastward vertically integrated momentum component at RHO-points" [meter second-1]</p> <p>uu = "time-averaged u-momentum times u-momentum" [meter2 second-2]</p> <p>uv = "time-averaged u-momentum times v-momentum" [meter2 second-2]</p> <p>v = "time-averaged v-momentum component" [meter second-1]</p> <p>v_northward = "time-averaged northward momentum component at RHO-points" [meter second-1]</p> <p>vbar = "time-averaged vertically integrated v-momentum component" [meter second-1]</p> <p>vbar_northward = "time-averaged northward vertically integrated momentum component at RHO-points" [meter second-1]</p> <p>vv = "time-averaged v-momentum times v-momentum" [meter2 second-2]</p> <p>w = "time-averaged vertical momentum component" [meter second-1]</p>
Example dataset of stLFR linked reads to run Aquila_stLFR
<p>We provide this small chromosome (chr21) example dataset to run Aquila_stLFR. </p> <p>Aquila_stLFR: <a href="https://github.com/maiziex/Aquila_stLFR">https://github.com/maiziex/Aquila_stLFR</a></p> <p> </p>
Model run with METROMS to evaluate open boundary conditions in CICE [idealized wind]
<p>Support for time-varying open boundary conditions (OBC) have been developed for sea ice in the Los Alamos Sea Ice Model (CICE) by Pedro Duarte (NPI, Norway). This dataset is a result of using the coupled ocean (ROMS) and sea ice (CICE) modelling framework METROMS (<a href="https://github.com/metno/metroms">https://github.com/metno/metroms</a>) in order to test the effects of the above mentioned boundary conditions. The specific application of METROMS that was used was MET Norway's main forecasting system for the Barents Sea; the Barents-2.5km model; details about the model can be found at <a href="https://ocean.met.no/models">https://ocean.met.no/models</a>.</p> <p>The model was initialized from the TOPAZ4 model (Sakov et al., 2012)<strong> </strong>and was run for the period 2019.09.01 - 2019.09.20, one time without OBC and one time with OBC enabled using input data from TOPAZ4<strong> </strong>at the boundaries. In both runs the model was set up realistically, but with the exception of idealized wind forcing. More specifically, the wind was blowing 10 m/s in the positive xi-direction until 2019-09.07 and then 10/m/s in the negative xi-direction for the rest of the simulation. The reasoning for this was to clearly demonstrate the effects of the OBC. Initially, the wind is blowing the ice away from the boundary and without using OBC for ice, it leaves open water in its path due to no information coming in through the boundary. With OBC enabled however, the simulation appear a lot more sensible with sea ice from TOPAZ4 coming in through the boundaries. When the wind switches to the opposite direction after 2019.09.07, on the case without OBC results in ice piling up at the boundary after some time. However, with OBC enabled, the ice exists the model domain.</p> <p> </p>
Model run with METROMS to evaluate open boundary conditions in CICE
<p>Support for time-varying open boundary conditions (OBC) have been developed for sea ice in the Los Alamos Sea Ice Model (CICE) by Pedro Duarte (NPI, Norway). This dataset is a result of using the coupled ocean (ROMS) and sea ice (CICE) modelling framework METROMS (<a href="https://github.com/metno/metroms">https://github.com/metno/metroms</a>) in order to test the effects of the above mentioned boundary conditions. The specific application of METROMS that was used was MET Norway's main forecasting system for the Barents Sea; the Barents-2.5km model; details about the model can be found at <a href="https://ocean.met.no/models">https://ocean.met.no/models</a>.</p> <p>The model was initialized from the TOPAZ4 model (Sakov et al., 2012)<strong> </strong>and was run for the period 2019.09.01 - 2019.10.03, one time without OBC and one time with OBC enabled using input data from TOPAZ4<strong> </strong>at the boundaries. This dataset can further be used to compare with satelite observations of sea ice concentration in order to help evaluate the impacts of the OBC's in CICE.</p>
Physiological data and R script for running physiology combined model for Drosophila suzukii
<p>This is the dataset that accompanies an article entitled "The use of insect life tables in optimizing invasive pest distributional models" that would be published in Ecography. The dataset include two R script that used to generate physical model and the physiology combined model respectively. Our paper shows that the physiology combined model show good performance when applying ecological niche model in risk assessment. We addressed this by determining whether incorporating physiological data from life table analyses of an invasive insect, Drosophila suzukii, improved predictions of ecological niche models. The dataset also include the physiology data D. suzukii that we assembled for running our physiology combined model.</p>
Text-fig. 1. Result of cumulative random counting of MN 5 localities in central Europe and the Iberian Peninsula. Ten simulations were run for each area. a. Results of the count including the average in bold, showing the clearly lower diversity in IB. b. The average lines standardized, showing similar patterns in the two areas. Note that in the simulation around thirty localities were needed to capture 80 % of the regional diversity. in Generically Speaking, A Survey On Neogene Rodent Diversity At The Genus Level In The Now Database
Text-fig. 1. Result of cumulative random counting of MN 5 localities in central Europe and the Iberian Peninsula. Ten simulations were run for each area. a. Results of the count including the average in bold, showing the clearly lower diversity in IB. b. The average lines standardized, showing similar patterns in the two areas. Note that in the simulation around thirty localities were needed to capture 80 % of the regional diversity.
Figs. 45–47 in New species of fast-running flies (Diptera: Empidoidea, Hybotidae, Tachydromiinae) from mangroves in Singapore
Figs. 45–47. Stilpon ubinensis, new species, male: 45, right epandrial lamella, lateral view; 46, epandrium with cerci, dorsal view; 47, left cercus and surstylus, lateral view. Scale bar = 0.1 mm.
Figs. 37–39 in New species of fast-running flies (Diptera: Empidoidea, Hybotidae, Tachydromiinae) from mangroves in Singapore
Figs. 37–39. Elaphropeza pallida, new species: 37, right epandrial lamella, lateral view; 38, epandrium with cerci, dorsal view; 39, left surstylus, lateral view. Scale bar = 0.1 mm.
Fig. 31 in New species of fast-running flies (Diptera: Empidoidea, Hybotidae, Tachydromiinae) from mangroves in Singapore
Fig. 31. Elaphropeza obscura, new species, male habitus lateral. Genitalia removed and illustrated in Figs. 32–34.
Figs. 20–21. Elaphropeza kranjiensis Grootaert & Shamshev 2012 in New species of fast-running flies (Diptera: Empidoidea, Hybotidae, Tachydromiinae) from mangroves in Singapore
Figs. 20–21. Elaphropeza kranjiensis Grootaert & Shamshev 2012 male habitus: 20, lateral; 21, dorsal.
Figs. 23–25 in New species of fast-running flies (Diptera: Empidoidea, Hybotidae, Tachydromiinae) from mangroves in Singapore
Figs. 23–25. Elaphropeza lowioides, new species: 23, right epandrial lamella, lateral view; 24, epandrium with cerci, dorsal view; 25, left surstylus, lateral view. Scale bar = 0.1 mm.
Figs. 17–19 in New species of fast-running flies (Diptera: Empidoidea, Hybotidae, Tachydromiinae) from mangroves in Singapore
Figs. 17–19. Elaphropeza chanoides, new species: 17, right epandrial lamella, lateral view with detail of subapical bristles; 18, epandrium with cerci, dorsal view; 19, left surstylus, lateral view. Scale bar = 0.1 mm.
Fig. 16 in New species of fast-running flies (Diptera: Empidoidea, Hybotidae, Tachydromiinae) from mangroves in Singapore
Fig. 16. Elaphropeza chanoides, new species, male habitus lateral. Genitalia removed and illustrated in Figs. 17–19.
Figs. 28–30 in New species of fast-running flies (Diptera: Empidoidea, Hybotidae, Tachydromiinae) from mangroves in Singapore
Figs. 28–30. Elaphropeza melanuroides, new species: 28, right epandrial lamella, lateral view; 29, epandrium with cerci, dorsal view; 30, left surstylus, lateral view. Scale bar = 0.1 mm.
Figs. 13–15 in New species of fast-running flies (Diptera: Empidoidea, Hybotidae, Tachydromiinae) from mangroves in Singapore
Figs. 13–15. Elaphropeza bakau, new species: 13, right epandrial lamella; 14, epandrium with cerci, dorsal view; 15, left surstylus, lateral view. Scale bar = 0.1 mm.
Figs. 8–11 in New species of fast-running flies (Diptera: Empidoidea, Hybotidae, Tachydromiinae) from mangroves in Singapore
Figs. 8–11. Elaphropeza asexa Shamshev & Grootaert, 2007: 8, right epandrial lamella, lateral view; 9, epandrium with cerci, dorsal view; 10, left cercus lateral view; 11, left surstylus, lateral view. Scale bar = 0.1 mm.
Figs. 32–34 in New species of fast-running flies (Diptera: Empidoidea, Hybotidae, Tachydromiinae) from mangroves in Singapore
Figs. 32–34. Elaphropeza obscura, new species: 32, right epandrial lamella, lateral view; 33, epandrium with cerci, dorsal view; 34, left surstylus, lateral view. Scale bar = 0.1 mm.
Figs. 6–7 in New species of fast-running flies (Diptera: Empidoidea, Hybotidae, Tachydromiinae) from mangroves in Singapore
Figs. 6–7. Elaphropeza asexa Shamshev & Grootaert, 2007: 6, habitus male lateral (photo Abdullah Samoh); 7, habitus female dorsal.
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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.