Find research datasets worth reusing

opencc-by-4.0Jul 2021View details →

Model projection of the effect of climate change and fishing pressure on key species of the South East Asia Seas

The dataset contain Projection from the Size-Spectra Bioclimatic Envelop Model (SS-DBEM), this work was part of the GCRF Blue communities Programme (www.blue-communities.org). The model provides distribution and abundance and/or biomass of fish and other species of commercial interest under climate change and fishing pressure. The model outputs are yearly abundance/biomass on a 0.5-by-0.5 degree grid, covering the period from 2000 to 2098. Further description of the model and relevant references are listed in the following file: Guide-fish-model-output-use.docx The model was run under two climate scenario: RCP4.5 and RCP8.5, with different combinations of fishing pressure expressed as the Maximum Sustainable Yield (MSY) for the following values: 0 (no fishing, climate change alone will cause variation in fish biomass), 1 (sustainable fishing), 2, 3 (overfishing), and, 4 (overfishing with destructive practice). The intent is not to reproduce current fishing level but to provide a range of scenarios with which the future of fisheries can be explored. We projected fish species that were identified as key in the South East Asia seas region by our regional partners.The full list is provided in document: Fish-list-modelguide.xlsx There are 4 zip files that contain the model outputs of in either abundance (number of fish) or biomass grams of fish) for the two climate scenario. For example Biomass-RCP45.zip will contain model outputs in biomass for projections under RCP4.5 and all MSY. within the zip files are .csv files of the outputs for each species under the 5 MSY (0 to 4), the individual file names identify the species (identified by a 6digit code), the output provided (abundance or biomass), the RCP (8.5 or 4.5), and the MSY (0, 1, 2, 3, or 4). For example the file labelled 600107-Abundance-rcp85-msy4.csv contains the outputs for species 600107 (Skipjack tuna, Katsuwonnus pelamis), as abundance, under RCP8.5 with MSY4. Headers indicate what is in each column (latitude, longitude and year).   Note: some knowledge of Python, R, or a similar software is recommended to ensure easy of use.

opencc-by-4.0Jan 2023View details →

Accompanying data for the open-source book Modeling of Hydrological Systems in Semi-Arid Central Asia

This data set is used to reproduce examples in the open-source book <a href="https://hydrosolutions.github.io/caham_book/">"Modeling of Hydrological Systems in Semi-Arid Central Asia"</a> which is part of a free course on hydrological modeling in Central Asia. The course teaches how to use publicly available data to implement a hydrological model for climate impact studies (Marti et al., 2023).  To use the data set to reproduce the examples in the book: Download the book from https://doi.org/10.5281/zenodo.6350042 and this data set to the same hierarchical level in your file system:  |- caham_book |- caham_data    |- AmuDarya    |- central_asia_domain    |- student_case_study_basins    |- SyrDarya You will need a working installation of R (https://www.r-project.org/) and a GUI (e.g. Posit, formerly RStudio https://posit.co/) to reproduce the scripted examples in the book. Once your software is set up, you can proceed to run the examples.

opencc-by-4.0Feb 2023View details →

A study on biomedical researchers' perspectives on public engagement in Southeast Asia

Survey data from biomedical researchers in Southeast Asia about their perceptions of public engagement. The survey used open and closed questions.

opencc-by-4.0Apr 2023View details →

A study on biomedical researchers' perspectives on public engagement in Southeast Asia

Qualitative data on researcher's perceptions of public and community engagement in South and Southeast Asia

opencc-by-4.0Dec 2022View details →

A new inventory of High Mountain Asia surging glaciers derived from multiple elevation datasets since the 1970s

Glacier surging is an unusual undulation instability of ice flow and complete surging glacier inventories are important for regional mass balance studies and assessing glacier-related hazards. Glacier surge events in High Mountain Asia (HMA) are widely reported. Through the estimated elevation changes from multiple DEMs sources that acquired from 1970s to 2020, and morphologic changes from 1986 to 2021, here we present a new surging glacier inventory across HMA. The inventory has incorporated 890 surging and 336 surge-like glaciers, each glacier is assigned with indicators of surging feature and surge possibility. Compared to previous surging glacier inventory in HMA, our inventory is theoretically more complete because of the much longer observation period. This data repository contains the surging glacier inventory and glacier elevation change maps. The inventory is stored in the format of GeoPackage (.gpkg) and ESRI Shapefile format (.shp), which is represented by glacier polygon (from GAMDAM2) or surface point with geometric attributes. The multi-temporal elevation change maps of identified surging glaciers were divided into 1×1° tiles, storing in the format of GeoTiff(*.tif). Detailed description of the dataset including the file contents and attributes information can be found in the metadata file (README.txt).

opencc-by-4.0Jul 2023View details →

Simulated spatially explicit dataset (300 m) on future forest cover changes in Southeast Asia projected under the baseline shared socioeconomic pathways

This is a simulated spatially explicit dataset on future forest cover changes in Southeast Asia projected under the baseline shared socioeconomic pathways. It includes six raster maps at a spatial resolution of 300 m: (1) 2015 baseline forest and non-forest map; (2) SSP1 2050 projected net forest gain map; (3) SSP2 2050 projected net forest gain map; (4) SSP3 2050 projected net forest loss map; (5) SSP4 2050 projected net forest gain map; and SSP5 2050 projected net forest loss map. This dataset is the result of a study published in Nature Communications (2019) (https://doi.org/10.1038/s41467-019-09646-4).

opencc-by-4.0Aug 2023View details →

Tracks of western disturbances (1950-2022) impacting South Asia

WDs are identified using the feature-tracking algorithm described in Hunt et al (2018). Relative vorticity is averaged across the 450-300 hPa layer, and then spectrally truncated to T42 to remove high-frequency noise that hinders tracking. For each region of positive vorticity, the centroid is located and labelled as a candidate WD. These centroids are connected between timesteps using a nearest-neighbour algorithm, biased to take into account the steering winds of the subtropical jet. Systems that do not on average travel eastward, last fewer than 48 hours, or do not pass through the box [20-42.5°N, 60-80°E] are rejected. Applied to ERA5, this gives over seventy years of track data (1950-2022). The method followed here is identical to Nischal et al (2022), except the northern edge of the catching box is extended from 36.5°N to 42.5°N, to ensure that all WDs that potentially impact North India are included. Column titles are: timestep: a counter indicating the number of 3-hourly timesteps that have passed since 1950-01-01 00:00 track_id: a unique identifier linking points into tracks time: string describing the date and time lon: longitude lat: latitude vort: vorticity measured at the centre of the WD averaged over the 450-300 hPa layer. Can be used for intensity filtering. eccentricity: eccentricity of the region of positive vorticity. Can be used to understand local dynamics. Hunt, K. M. R., Turner, A. G., & Shaffrey, L. C. (2018). The evolution, seasonality and impacts of western disturbances. Quarterly Journal of the Royal Meteorological Society, 144(710), 278-290. Nischal, Attada, R., & Hunt, K. M. (2022). Evaluating winter precipitation over the western Himalayas in a high-resolution Indian regional reanalysis using multisource climate datasets. Journal of Applied Meteorology and Climatology, 61(11), 1613-1633.

opencc-by-4.0Aug 2023View details →

Dataset for "Adjoint Waveform Tomography for Crustal and Upper Mantle Structure the Middle East and Southwest Asia for Improved Waveform Simulations Using Openly Available Broadband Data"

This dataset contains the MESWA (Middle East and Southwest Asia) seismic model and auxiliary data used in the creation of the model (Rodgers, 2023).  MESWA is a three-dimensional model of the seismic properties of crust and upper mantle of the Middle East and Southwest Asia.  The MESWA model is provided in NetCDF format (readable by for example, xarray, Hoyer & Hamman, <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0057">2017</a>) and HDF5 format for viewing with ParaView (Ahrens et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0002">2005</a>) and interaction with Salvus (Afanasiev et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0001">2019</a>).    Also included are the earthquake source parameters for all 327 Global Centroid Moment Tensor events considered in this study in ASCII text format. Also included are lists of the selected 192 inversion events and 66 validation events in ASCII text format.  Lastly, we include a list of all receivers used in the creation and validation of MESWA.  This is a simple ASCII file with the event name and receiver name (composed of the network_code and station_code).   The following table provides a listing of the files in the dataset: <table> <tbody> <tr> <td> File </td> <td> Description </td> </tr> <tr> <td> MESWA.nc </td> <td> MESWA model in NetCDF format </td> </tr> <tr> <td> MESWA.h5 </td> <td> MESWA model in HDF5 format, used by Salvus </td> </tr> <tr> <td> MESWA.xmdf </td> <td> Auxiliary file for MESWA.h5, used to import model into Paraview </td> </tr> <tr> <td> events_project.csv </td> <td> Table of event source parameters for all 327 events considered in the project </td> </tr> <tr> <td> inversion_events_192.csv </td> <td> Table of 192 inversion events  (ASCII comma separated value) </td> </tr> <tr> <td> validation_events_66.csv </td> <td> Table of 66 validation events  (ASCII comma separated value) </td> </tr> <tr> <td> events_receivers_inversion.csv </td> <td> Table of waveform (event-receiver-channel) data used in the inversion (ASCII comma separated value) </td> </tr> <tr> <td> events_receivers_validation.csv </td> <td> Table of waveform (event-receiver-channel) data used in the validation (ASCII comma separated value) </td> </tr> </tbody> </table>     References Afanasiev, M, C Boehm, M van Driel, L Krischer, M Rietmann, DA May, MG Knepley, and A Fichtner (2019). Modular and flexible spectral-element waveform modelling in two and three dimensions, Geophys. J. Int., 216(3), 1675–1692, doi: 10.1093/gji/ggy469   Ahrens, J., Geveci, B., & Law, C. (2005). Paraview: An end-user tool for large data visualization. The Visualization Handbook, 717(8). <a href="https://doi.org/10.1016/b978-012387582-2/50038-1">https://doi.org/10.1016/b978-012387582-2/50038-1</a>   Hoyer, S., & Hamman, J. (2017). Xarray: N-D labeled arrays and datasets in Python. Journal of Open Research Software, 5(1). <a href="https://doi.org/10.5334/jors.148">https://doi.org/10.5334/jors.148</a>   Rodgers, A. (2023). Adjoint Waveform Tomography for Crustal and Upper Mantle Structure the Middle East and Southwest Asia for Improved Waveform Simulations Using Openly Available Broadband Data, technical report, LLNL-TR- 851939.   Acknowledgements This project was support by Lawrence Livermore National Laboratory’s Laboratory Directed Research and Development project 20-ERD-008 and the National Nuclear Security Administration.  This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-MI-852402

opencc-by-4.0Oct 2023View details →

High-resolution maps of rubber and rubber-related deforestation for Southeast Asia

This dataset contains maps of rubber plantations in 2021, and maps of rubber-related deforestation between 1993-2016 for Southeast Asia. The rubber maps have a 10 m pixel size, and the deforestation maps have a 30 m pixel size. The dataset and the methods for generating it are described in Wang et al. 2023. High-resolution maps show that rubber causes substantial deforestation. Nature. Please note that an update of this dataset will follow in September 2025.

openother-openJan 2020View details →

cldf-datasets/petersonsouthasia: CLDF data for Peterson 2017, Towards a linguistic prehistory of eastern-central South Asia

Cite the CLDF data as Cite the source dataset as <blockquote> Peterson, J. (2017). Fitting the pieces together – Towards a linguistic prehistory of eastern-central South Asia (and beyond). Journal of South Asian Languages and Linguistics, 4(2), pp. 211-257. doi:10.1515/jsall-2017-0008 </blockquote>

opencc-zeroFeb 2020View details →

FIG. 7 in A new species of Tungurictis Colbert, 1939 (Carnivora, Hyaenidae) from the middle Miocene of Junggar Basin, northwestern China and the early divergence of basal hyaenids in East Asia

FIG. 7. — Tungurictis small sp., IVPP V 11497, left dentary fragment with m1 and m2 alveolus. A, stereo photos of occlusal view; B, lingual view; C, buccal view. Scale bars: 10 mm.

opencc-zeroFeb 2020View details →

FIG. 5 in A new species of Tungurictis Colbert, 1939 (Carnivora, Hyaenidae) from the middle Miocene of Junggar Basin, northwestern China and the early divergence of basal hyaenids in East Asia

FIG. 5. — Tungurictis peignei, n. sp., IVPP V 25222, holotype, right dentary with p2-m2 (A, stereo photos, occlusal view, C, lingual, and D, buccal views) and IVPP V 11493, left dentary with p2-m1 (B, stereo photos, occlusal view; E, lingual view; F, buccal views). Scale bars: 10 mm.

opencc-zeroFeb 2020View details →

FIG. 4 in A new species of Tungurictis Colbert, 1939 (Carnivora, Hyaenidae) from the middle Miocene of Junggar Basin, northwestern China and the early divergence of basal hyaenids in East Asia

FIG. 4. — Tungurictis peignei, n. sp., IVPP V 25222, holotype, isolated right I3, mesial view (A), right upper canine, buccal view (B), left P1, and left maxilla with P3-4 (C, stereo photos of occlusal view; D, buccal view). Scale bar: 10 mm.

Figs 92-93 in An ancient radiation: Ortholasmatine harvestmen in Asia - a new genus, three new species and a revision of the known species (Arachnida, Opiliones, Nemastomatidae)

Figs 92-93. Asiolasma schwendingeri sp. nov., male holotype. (92) Entire body in lateral view. (93) Prosoma in dorsal view. Scale: 0.5 mm (92-93).

Figs 112-117 in An ancient radiation: Ortholasmatine harvestmen in Asia - a new genus, three new species and a revision of the known species (Arachnida, Opiliones, Nemastomatidae)

Figs 112-117. Asiolasma billsheari sp. nov., male holotype. (112) Right chelicera in prolateral view. (113) Same in retrolateral view. (114) Apophysis of 2nd cheliceral article in prolateral view. (115) Pedipalp in prolateral view. (116) Distal part of pedipalpal femur, whole patella and proximal part of tibia in retrolateral view. (117) Apophysis on ventro-distal part of pedipalpal patella in prolateral view. Scales: 0.1 mm (114); 0.2 mm (112-113); 0.3 mm (115-116); no scale (117).

Figs 55-59 in An ancient radiation: Ortholasmatine harvestmen in Asia - a new genus, three new species and a revision of the known species (Arachnida, Opiliones, Nemastomatidae)

Figs 55-59. Asiolasma ailaoshan, male (55-56, 58), female (57, 59). (55, 57) Right chelicera in prolateral view. (56) Same in retrolateral view. (58-59) Left pedipalp in prolateral view. Scale: 0.3 mm.

Figs 47-54 in An ancient radiation: Ortholasmatine harvestmen in Asia - a new genus, three new species and a revision of the known species (Arachnida, Opiliones, Nemastomatidae)

Figs 47-54. Asiolasma ailaoshan, male holotype. (47) Glans penis in lateral view. (48) Same in dorsal view. (49) Same in ventral view. (50) Truncus penis in lateral view. (51) Same in ventral view. (52) Same in dorsal view. (53) Body in dorsal view. (54) Same in lateral view. Figs 47-48 reproduced from Zhang et al. (2018). Scales: 0.05 mm (47-49); 0.26 mm (50-52); 0.70 mm (53-54).

Figs 33-37 in An ancient radiation: Ortholasmatine harvestmen in Asia - a new genus, three new species and a revision of the known species (Arachnida, Opiliones, Nemastomatidae)

Figs 33-37. Asiolasma damingshan, male holotype. (33) Body in dorsal view. (34) Same in lateral view. (35) Chelicera in prolateral view. (36) Apophsis of 2nd cheliceral article in prolateral view. (37) Left pedipalp in retrolateral view. Figs 33-34 reproduced from Zhang & Zhang (2013). Scales: 0.03 mm (36); 0.2 mm (35); 0.4 (37); 0.5 mm (33-34).

Figs 65-71 in An ancient radiation: Ortholasmatine harvestmen in Asia - a new genus, three new species and a revision of the known species (Arachnida, Opiliones, Nemastomatidae)

Figs 65-71. Asiolasma juergengruberi sp. nov., male (65-70), female (71); specimens from Lugu Hu (65-67, 71), specimen from Lijiang area (68-70). (65) Body in lateral view. (66) Prosoma dorsal view. (67-68, 71) Right pedipalp in prolateral view. (69) Apophysis ventrodistally on patella. (70) Same dorsally. Scales: 0.03 mm (67-68, 71); 0.5 mm (65-66); no scale (69- 70).

Figs 20-26 in An ancient radiation: Ortholasmatine harvestmen in Asia - a new genus, three new species and a revision of the known species (Arachnida, Opiliones, Nemastomatidae)

Figs 20-26. Asiolasma angka, male. (20) Glans penis in lateral view. (21) Same in ventral view. (22) Same in dorsal view. (23) Truncus penis in ventral view. (24) Same in lateral view. (25) Prosoma in dorsal view. (26) Body in lateral view. Scales: 0.2 mm (20-22); 0.3 mm (23-24); 0.8 mm (25-26).