cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.hvwtxw
title: Occurrence Download

description: A dataset containing 247 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Solanum cheesmaniae"
 ]
}

 The dataset includes 247 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004971-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25346/s6eecgix/ilv3ux
title: msXL_334v_31_22.jpg
DOI: 10.25346/s6eecgix/kt4x1t
title: msXL_334v_31_21.jpg
DOI: 10.25346/s6eecgix/diascr
title: msXL_334v_31_20.jpg
DOI: 10.25346/s6eecgix/rtzvsi
title: msXL_334v_31_19.jpg
DOI: 10.25346/s6eecgix/zdi4bg
title: msXL_334v_31_18.jpg
DOI: 10.25346/s6eecgix/pej2py
title: msXL_334v_31_17.jpg
DOI: 10.25346/s6eecgix/1mzvjs
title: msXL_334v_31_16.jpg
DOI: 10.25346/s6eecgix/jqilzh
title: msXL_334v_31_15.jpg
DOI: 10.25346/s6eecgix/lmvrjt
title: msXL_334v_31_14.jpg
DOI: 10.25346/s6eecgix/sc4zki
title: msXL_334v_31_13.jpg
DOI: 10.25346/s6eecgix/0mynat
title: msXL_334v_22_73.jpg
DOI: 10.25346/s6eecgix/hlghbx
title: msXL_334v_22_72.jpg
DOI: 10.25346/s6eecgix/8zasjq
title: msXL_334v_22_71.jpg
DOI: 10.25346/s6eecgix/ibavym
title: msXL_334v_22_70.jpg
DOI: 10.25346/s6eecgix/vqt0pu
title: msXL_334v_22_69.jpg
DOI: 10.25346/s6eecgix/zetlr3
title: msXL_334v_22_68.jpg
DOI: 10.25346/s6eecgix/p39bed
title: msXL_334v_22_67.jpg
DOI: 10.25346/s6eecgix/gzspxy
title: msXL_334v_22_66.jpg
DOI: 10.25346/s6eecgix/drucum
title: msXL_334v_22_65.jpg
DOI: 10.25346/s6eecgix/sjgj1a
title: msXL_334v_22_64.jpg
DOI: 10.25346/s6eecgix/szodhf
title: msXL_334v_22_63.jpg
DOI: 10.25346/s6eecgix/clbpwn
title: msXL_334v_22_62.jpg
DOI: 10.25346/s6eecgix/oz2osd
title: msXL_334v_15_12.jpg
DOI: 10.25346/s6eecgix/aj8fr6
title: msXL_334v_15_11.jpg
DOI: 10.25346/s6eecgix/4simzj
title: msXL_334v_15_10.jpg
DOI: 10.25346/s6eecgix/kppwgk
title: msXL_334v_15_09.jpg
DOI: 10.25346/s6eecgix/dvqhzw
title: msXL_334v_15_08.jpg
DOI: 10.25346/s6eecgix/pr2ftk
title: msXL_334v_15_07.jpg
DOI: 10.25346/s6eecgix/b6vi2f
title: msXL_334v_15_06.jpg
DOI: 10.25346/s6eecgix/5cjgcf
title: msXL_334v_15_05.jpg
DOI: 10.25346/s6eecgix/py2lfa
title: msXL_334v_15_04.jpg
DOI: 10.25346/s6eecgix/y7radt
title: msXL_334v_15_03.jpg
DOI: 10.25346/s6eecgix/syoqyi
title: msXL_334v_15_02.jpg
DOI: 10.25346/s6eecgix/zbbluq
title: msXL_334v_15_01.jpg
DOI: 10.25346/s6eecgix/usdctf
title: msXL_334v_10_61.jpg
DOI: 10.60873/fk2/by0fpb
title: Prefeitura de Porto Alegre - Sinalização Viária - Porto Alegre [Agosto-2025]

description: Detalhamento da sinalização viária implementada em Porto Alegre até 2025/08. Dados possuem endereço e são georreferenciados. EPSG: 4326 (WGS84).
DOI: 10.25346/s6eecgix/lcwefr
title: msXL_334v_10_60.jpg
DOI: 10.25346/s6eecgix/fqdjtf
title: msXL_334v_10_59.jpg
DOI: 10.25346/s6eecgix/tffrav
title: msXL_334v_10_58.jpg
DOI: 10.25346/s6eecgix/uqw3mo
title: msXL_334v_10_57.jpg
DOI: 10.15468/dl.68nkwe
title: Occurrence Download

description: A dataset containing 259 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "OccurrenceStatus is Present",
 "TaxonKey is Puma concolor concolor"
 ]
}

 The dataset includes 259 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004972-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25346/s6eecgix/k23oii
title: msXL_334v_10_56.jpg
DOI: 10.25346/s6eecgix/w0xfrl
title: msXL_334v_10_55.jpg
DOI: 10.25346/s6eecgix/0q4thj
title: msXL_334v_10_54.jpg
DOI: 10.25346/s6eecgix/ecifad
title: msXL_334v_10_53.jpg
DOI: 10.25346/s6eecgix/qhhyro
title: msXL_334v_10_52.jpg
DOI: 10.25346/s6eecgix/vz8gcg
title: msXL_334v_10_51.jpg
DOI: 10.25346/s6eecgix/nju3uv
title: msXL_334v_10_50.jpg
DOI: 10.25346/s6eecgix/vlbeym
title: msXL_334v_0_ring.jpg
DOI: 10.25346/s6eecgix/akbrd3
title: msXL_334v.jpg
DOI: 10.25346/s6eecgix/b6udla
title: msXL_334r_65_49.tif
DOI: 10.25346/s6eecgix/bqiapx
title: msXL_334r_65_48.tif
DOI: 10.25346/s6eecgix/dnl7aj
title: msXL_334r_65_47.tif
DOI: 10.25346/s6eecgix/fsb4nm
title: msXL_334r_65_46.tif
DOI: 10.25346/s6eecgix/pclpmf
title: msXL_334r_65_45.tif
DOI: 10.25346/s6eecgix/9zi3zp
title: msXL_334r_65_44.tif
DOI: 10.25346/s6eecgix/wqrrgt
title: msXL_334r_65_43.tif
DOI: 10.25346/s6eecgix/psjnqm
title: msXL_334r_65_42.tif
DOI: 10.25346/s6eecgix/7k3qyw
title: msXL_334r_65_41.tif
DOI: 10.25346/s6eecgix/exjrc8
title: msXL_334r_65_40.tif
DOI: 10.25346/s6eecgix/esropn
title: msXL_334r_65_39.tif
DOI: 10.15468/dl.gjwya5
title: Occurrence Download

description: A dataset containing 824 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Potos flavus"
 ]
}

 The dataset includes 824 records from 63 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004974-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25346/s6eecgix/dixig4
title: msXL_334r_65_38.tif
DOI: 10.25346/s6eecgix/hirycq
title: msXL_334r_46_37.tif
DOI: 10.25346/s6eecgix/wl7dd1
title: msXL_334r_46_36.tif
DOI: 10.15468/dl.gzfwz2
title: Occurrence Download

description: A dataset containing 494 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Solanum chilense"
 ]
}

 The dataset includes 494 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004975-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25346/s6eecgix/n77z9r
title: msXL_334r_46_35.tif
DOI: 10.25346/s6eecgix/n2uyqs
title: msXL_334r_46_34.tif
DOI: 10.25346/s6eecgix/hvr4gn
title: msXL_334r_46_33.tif
DOI: 10.25346/s6eecgix/sccvbw
title: msXL_334r_46_32.tif
DOI: 10.25346/s6eecgix/xy0q3g
title: msXL_334r_46_31.tif
DOI: 10.25346/s6eecgix/rcse0a
title: msXL_334r_46_30.tif
DOI: 10.25346/s6eecgix/n0r1fz
title: msXL_334r_46_29.tif
DOI: 10.25346/s6eecgix/pr0uoq
title: msXL_334r_46_28.tif
DOI: 10.25346/s6eecgix/nzjom0
title: msXL_334r_46_27.tif
DOI: 10.5281/zenodo.17128113
title: Truck Image Dataset

description: Collection of annotated truck images, from a side point view, used to extract information about truck axles, collected on a highway in the State of São Paulo, Brazil. This is still a work in progress dataset and will be updated regularly, as new images are acquired. More info can be found on: Researchgate Lab Page, OrcID Profiles, or ITS Lab page on Github.

The dataset includes 1053 cropped images of trucks, with mixed real world trucks and synthetic trucks from Euro Truck Simulator 2.

727 images were taken with three different cameras, on five different locations.



727 images

Format: JPG

Resolution: 1920xVarious, 96dpi, 24bits

Naming pattern: <video_name>_<color|gray>-<Region_of_Interest_ID>-<truck_ID>.jpg


326 images were collected from Trucker's MP website.



326 images

Format: JPG

Resolution: 1920xVarious, 96dpi, 24bits

Naming Pattern: <HEXID>.jpg


All annotated objects were created with LabelMe, and saved in JSON files for each image. For more information about the annotation format, please refer to the LabelMe documentation.

Annotated objects are all related to truck axles, in 4 categories, Truck, Axle, Tandem, Tridem. Tandem is a double axle composition, and tridem is a triple axle composition. The number of objects in each category is as follows: 



Truck: 1053 

Axle: 3927

Tandem: 1172

Tridem: 188


If this dataset helps in any way your research, please feel free to contact the authors. We really enjoy knowing about other researcher's projects and how everybody is making use of the images on this dataset. We are also open for collaborations and to answer any questions. We also have a paper that uses this dataset, so if you want to officially cite us in your research, please do so! We appreciate it!

Marcomini, Leandro Arab, and André Luiz Cunha. "Truck Axle Detection with Convolutional Neural Networks." arXiv preprint arXiv:2204.01868 (2022).

 
description: v2.0.0
- Added annotation files for every image;
- Four new objects annotated: Truck, Axle, Tandem, Tridem;

v1.0.0
- Added base truck dataset;
DOI: 10.5281/zenodo.5744736
title: Truck Image Dataset

description: Collection of annotated truck images, from a side point view, used to extract information about truck axles, collected on a highway in the State of São Paulo, Brazil. This is still a work in progress dataset and will be updated regularly, as new images are acquired. More info can be found on: Researchgate Lab Page, OrcID Profiles, or ITS Lab page on Github.

The dataset includes 1053 cropped images of trucks, with mixed real world trucks and synthetic trucks from Euro Truck Simulator 2.

727 images were taken with three different cameras, on five different locations.



727 images

Format: JPG

Resolution: 1920xVarious, 96dpi, 24bits

Naming pattern: <video_name>_<color|gray>-<Region_of_Interest_ID>-<truck_ID>.jpg


326 images were collected from Trucker's MP website.



326 images

Format: JPG

Resolution: 1920xVarious, 96dpi, 24bits

Naming Pattern: <HEXID>.jpg


All annotated objects were created with LabelMe, and saved in JSON files for each image. For more information about the annotation format, please refer to the LabelMe documentation.

Annotated objects are all related to truck axles, in 4 categories, Truck, Axle, Tandem, Tridem. Tandem is a double axle composition, and tridem is a triple axle composition. The number of objects in each category is as follows: 



Truck: 1053 

Axle: 3927

Tandem: 1172

Tridem: 188


If this dataset helps in any way your research, please feel free to contact the authors. We really enjoy knowing about other researcher's projects and how everybody is making use of the images on this dataset. We are also open for collaborations and to answer any questions. We also have a paper that uses this dataset, so if you want to officially cite us in your research, please do so! We appreciate it!

Marcomini, Leandro Arab, and André Luiz Cunha. "Truck Axle Detection with Convolutional Neural Networks." arXiv preprint arXiv:2204.01868 (2022).

 
description: v2.0.0
- Added annotation files for every image;
- Four new objects annotated: Truck, Axle, Tandem, Tridem;

v1.0.0
- Added base truck dataset;
DOI: 10.25346/s6eecgix/ut3hjk
title: msXL_334r_46_26.tif
DOI: 10.25346/s6eecgix/whmtyu
title: msXL_334r_46_25.tif
DOI: 10.25346/s6eecgix/m11tbz
title: msXL_334r_31_24.tif
DOI: 10.25346/s6eecgix/x9gup9
title: msXL_334r_31_23.tif
DOI: 10.25346/s6eecgix/4hcw3l
title: msXL_334r_31_22.tif
DOI: 10.25346/s6eecgix/q1fist
title: msXL_334r_31_21.tif
DOI: 10.25346/s6eecgix/gymljl
title: msXL_334r_31_20.tif
DOI: 10.25346/s6eecgix/ofni8r
title: msXL_334r_31_19.tif
DOI: 10.25346/s6eecgix/ovpzdx
title: msXL_334r_31_18.tif
DOI: 10.25346/s6eecgix/pwuccz
title: msXL_334r_31_17.tif
DOI: 10.25346/s6eecgix/lwotvc
title: msXL_334r_31_16.tif
DOI: 10.25346/s6eecgix/xwywa7
title: msXL_334r_31_15.tif
DOI: 10.25346/s6eecgix/659ek7
title: msXL_334r_31_14.tif
DOI: 10.25346/s6eecgix/fvskux
title: msXL_334r_31_13.tif
DOI: 10.25346/s6eecgix/c5ohpg
title: msXL_334r_15_12.tif
DOI: 10.25346/s6eecgix/ejljgg
title: msXL_334r_15_11.tif
DOI: 10.25346/s6eecgix/twsrht
title: msXL_334r_15_09.tif
DOI: 10.25346/s6eecgix/wsbcya
title: msXL_334r_15_08.tif
DOI: 10.25346/s6eecgix/cf6kyg
title: msXL_334r_15_07.tif
DOI: 10.25346/s6eecgix/eotmt5
title: msXL_334r_15_06.tif
DOI: 10.25346/s6eecgix/xanre7
title: msXL_334r_15_05.tif
DOI: 10.25346/s6eecgix/25nlje
title: msXL_334r_15_04.tif
DOI: 10.25346/s6eecgix/6melmk
title: msXL_334r_15_03.tif
DOI: 10.25346/s6eecgix/yi3fog
title: msXL_334r_15_02.tif
DOI: 10.25346/s6eecgix/uorx63
title: msXL_334r_15_01.tif
DOI: 10.25346/s6eecgix/8lasiz
title: msXL_334r_0_ring.tif
DOI: 10.25346/s6eecgix/gie7yk
title: msXL_334r.tif
DOI: 10.25346/s6eecgix/0ig8ib
title: plane_8.jpg
DOI: 10.25346/s6eecgix/f87wmh
title: plane_7.jpg
DOI: 10.5066/p13jmmdm
title: Tracking Data for Caribou (Rangifer tarandus granti)

description: This data release includes 3 child items with tracking data for caribou, a terrestrial mammal that ranges between the Alaska and Canada.
Child Item 1: "GPS Tracking Data for Caribou (Rangifer tarandus granti) from the Denali Herd, Alaska, 2010-2018"
Child Item 2: "GPS Tracking Data for Caribou (Rangifer tarandus granti) from the Fortymile Herd, Alaska, 1998-1999"
Child Item 3: "GPS Tracking Data for Caribou (Rangifer tarandus granti) from the Nelchina Herd, Alaska, 1999-2002"
DOI: 10.25346/s6eecgix/qx2dbl
title: plane_6.jpg
DOI: 10.25346/s6eecgix/l1bgq3
title: plane_5.jpg
DOI: 10.25346/s6eecgix/nu8huq
title: plane_4.jpg
DOI: 10.25346/s6eecgix/2k5h4i
title: plane_3.jpg
DOI: 10.25346/s6eecgix/vfbms2
title: plane_2.jpg
DOI: 10.25346/s6eecgix/e5unmk
title: plane_1.jpg
DOI: 10.25346/s6eecgix/babtpm
title: plane_0.jpg
DOI: 10.25346/s6eecgix/kyuhw7
title: info.json
DOI: 10.25346/s6eecgix/vaprhg
title: msXL_334r.relight
DOI: 10.25346/s6eecgix/xslvr9
title: msXL_334r_65_49.jpg
DOI: 10.25346/s6eecgix/ac8g9x
title: msXL_334r_65_48.jpg
DOI: 10.25346/s6eecgix/bmczi3
title: msXL_334r_65_47.jpg
DOI: 10.25346/s6eecgix/ycvnwz
title: msXL_334r_65_46.jpg
DOI: 10.25346/s6eecgix/zd2ivy
title: msXL_334r_65_45.jpg
DOI: 10.25346/s6eecgix/y070ba
title: msXL_334r_65_44.jpg
DOI: 10.25346/s6eecgix/fndsx0
title: msXL_334r_65_43.jpg
DOI: 10.25346/s6eecgix/xsi2g4
title: msXL_334r_65_42.jpg
DOI: 10.25346/s6eecgix/xwme3n
title: msXL_334r_65_41.jpg
DOI: 10.25346/s6eecgix/7yxwdn
title: msXL_334r_65_40.jpg
DOI: 10.25346/s6eecgix/ee8gcj
title: msXL_334r_65_39.jpg
DOI: 10.25346/s6eecgix/brqvvt
title: msXL_334r_65_38.jpg
DOI: 10.25346/s6eecgix/kwnkmv
title: msXL_334r_46_37.jpg
DOI: 10.25346/s6eecgix/ssxktr
title: msXL_334r_46_36.jpg
DOI: 10.25346/s6eecgix/6uckn5
title: msXL_334r_46_35.jpg
DOI: 10.25346/s6eecgix/uctqza
title: msXL_334r_46_34.jpg
DOI: 10.25346/s6eecgix/d7ye36
title: msXL_334r_46_33.jpg
DOI: 10.25346/s6eecgix/ikacc9
title: msXL_334r_46_32.jpg
DOI: 10.25346/s6eecgix/6h6kff
title: msXL_334r_46_31.jpg
DOI: 10.25346/s6eecgix/zzjprl
title: msXL_334r_46_30.jpg
DOI: 10.25346/s6eecgix/2dzzm3
title: msXL_334r_46_29.jpg
DOI: 10.25346/s6eecgix/kog2xn
title: msXL_334r_46_28.jpg
DOI: 10.25346/s6eecgix/wmnlxl
title: msXL_334r_46_27.jpg
DOI: 10.25346/s6eecgix/uqgiri
title: msXL_334r_46_26.jpg
DOI: 10.15468/dl.y4mwxa
title: Occurrence Download

description: A dataset containing 87 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Solanum chmielewskii"
 ]
}

 The dataset includes 87 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004977-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25346/s6eecgix/teierw
title: msXL_334r_46_25.jpg
DOI: 10.25346/s6eecgix/wtijn5
title: msXL_334r_31_24.jpg
DOI: 10.25346/s6eecgix/8xglhb
title: msXL_334r_31_23.jpg
DOI: 10.7910/dvn/tz5ime
title: Association between Political Distrust and Self-Reported Sleep Problems in the United States: Evidence from the 2022 General Social Survey

description: This replication dofile accompanies the manuscript "Association between Political Distrust and Self-Reported Sleep Problems in the United States: Evidence from the 2022 General Social Survey." It contains all the codes needed o reproduce the descriptive statistics, regression analyses and margins plots reported in the paper. The analysis uses data from the 2022 General Social Survey (GSS).
DOI: 10.25346/s6eecgix/b8epqq
title: msXL_334r_31_22.jpg
DOI: 10.25346/s6eecgix/7xj7dp
title: msXL_334r_31_21.jpg
DOI: 10.25346/s6eecgix/am5oq8
title: msXL_334r_31_20.jpg
DOI: 10.25346/s6eecgix/ekig2u
title: msXL_334r_31_19.jpg
DOI: 10.25346/s6eecgix/0xsmku
title: msXL_334r_31_18.jpg
DOI: 10.25346/s6eecgix/av2dxp
title: msXL_334r_31_17.jpg
DOI: 10.25346/s6eecgix/wil7gp
title: msXL_334r_31_16.jpg
DOI: 10.25346/s6eecgix/bitjad
title: msXL_334r_31_15.jpg
DOI: 10.25346/s6eecgix/wcy1d1
title: msXL_334r_31_14.jpg
DOI: 10.25346/s6eecgix/7q7hgf
title: msXL_334r_31_13.jpg
DOI: 10.25346/s6eecgix/tv82oy
title: msXL_334r_15_12.jpg
DOI: 10.25346/s6eecgix/3nvn99
title: msXL_334r_15_11.jpg
DOI: 10.25345/c5zp3wd1v
title: MassIVE MSV000099196 - PrPC-facilitated cell-signaling activates phospholipase Cgamma1 and triggers an Arc/Arg3.1 response in mouse neurons and human iPSC-derived neurons
title: Synapse loss is an early feature of prion disease, yet the underlying drivers are poorly understood. We recently revealed evidence of neuronal hyperactivity in prion-infected animal models. Herein we identified increased Arc/Arg3.1 in patients with prion disease, suggesting heightened neuronal activity also occurs in human prion-affected brain. To determine the signaling events initiated by prion aggregates (PrPSc), we developed a disease model in which human iPSC-derived excitatory neurons (iNs) are stimulated with a PrPSc-mimetic antibody that binds the cellular prion protein. Within two hours, we detected an Arc response together with transcriptomic changes previously reported in prion-infected mice. Proteomics, RNA sequencing, and a phosphokinase array revealed altered phosphorylation changes of PLC-gamma1, ERK1/2, and EGFR as PrPC-triggered cell signaling events accompanying the Arc/Arg3.1 response. These results suggest that PrPC ligands, including PrPSc, may trigger rapid signaling events linked to neuronal hyperactivity in human neurons, and indicate PLC-gamma1 as a potential interventional target.
DOI: 10.25346/s6eecgix/lpr3se
title: msXL_334r_15_10.jpg
DOI: 10.25346/s6eecgix/9aorhi
title: msXL_334r_15_09.jpg
DOI: 10.25346/s6eecgix/gjcjz2
title: msXL_334r_15_08.jpg
DOI: 10.25346/s6eecgix/xzbi13
title: msXL_334r_15_07.jpg
DOI: 10.25346/s6eecgix/vgifrm
title: msXL_334r_15_06.jpg
DOI: 10.25346/s6eecgix/m6thhm
title: msXL_334r_15_05.jpg
DOI: 10.25346/s6eecgix/ex0kwd
title: msXL_334r_15_04.jpg
DOI: 10.25346/s6eecgix/hu50l8
title: msXL_334r_15_03.jpg
DOI: 10.25346/s6eecgix/cokb63
title: msXL_334r_15_02.jpg
DOI: 10.25346/s6eecgix/2633el
title: msXL_334r_15_01.jpg
DOI: 10.25346/s6eecgix/u5zlkz
title: msXL_334r_0_ring.jpg
DOI: 10.25346/s6eecgix/g9xj8u
title: msXL_334r.jpg
DOI: 10.25346/s6eecgix/vbmi06
title: msXL_323r_65_49.tif
DOI: 10.25346/s6eecgix/jaen3i
title: msXL_323r_65_48.tif
DOI: 10.25346/s6eecgix/tv9ssk
title: msXL_323r_65_47.tif
DOI: 10.25346/s6eecgix/b7uefs
title: msXL_323r_65_46.tif
DOI: 10.25346/s6eecgix/gkvmmv
title: msXL_323r_65_45.tif
DOI: 10.15468/dl.krbe3d
title: Occurrence Download

description: A dataset containing 17563 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Solenopsis invicta",
 "Year 2002-2025"
 ]
}

 The dataset includes 17563 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004979-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25346/s6eecgix/teu8vc
title: msXL_323r_65_44.tif
DOI: 10.25346/s6eecgix/odx3v2
title: msXL_323r_65_43.tif
DOI: 10.25346/s6eecgix/dcvved
title: msXL_323r_65_42.tif
DOI: 10.25346/s6eecgix/l8rotr
title: msXL_323r_65_41.tif
DOI: 10.25346/s6eecgix/ogsx3e
title: msXL_323r_65_40.tif
DOI: 10.25346/s6eecgix/1ttsjt
title: msXL_323r_65_39.tif
DOI: 10.5281/zenodo.17128139
title: song2
DOI: 10.25346/s6eecgix/8veafi
title: msXL_323r_65_38.tif
DOI: 10.25346/s6eecgix/yrsxcm
title: msXL_323r_65_37.tif
DOI: 10.25346/s6eecgix/7nlt74
title: msXL_323r_48_36.tif
DOI: 10.25346/s6eecgix/4lmens
title: msXL_323r_48_35.tif
DOI: 10.25346/s6eecgix/8xbs5h
title: msXL_323r_48_34.tif
DOI: 10.25346/s6eecgix/c36xmj
title: msXL_323r_48_33.tif
DOI: 10.25346/s6eecgix/xlhmzl
title: msXL_323r_48_32.tif
DOI: 10.25346/s6eecgix/mtopjy
title: msXL_323r_48_31.tif
DOI: 10.25346/s6eecgix/xnkh0q
title: msXL_323r_48_30.tif
DOI: 10.5281/zenodo.17128107
title: inlists for the supernova progenitors adopted in "Discovery of a likely Type II SN at z=3.6 with JWST" by D. A. Coulter et al.

description: inlists used for making the red supergiant supernova progenitor models adopted for modeling the light curves of AT 2023adsv presented by D. A. Coulter et al. (https://arxiv.org/abs/2501.05513). MESA version r23.05.1 and mesasdk version 23.05.1 are used. "inlist_project_12" is for the 12 Msun progenitor, "inlist_project_16" is for the 16 Msun progenitor, and "inlist_project_20" is for the 20 Msun progenitor.
DOI: 10.25346/s6eecgix/qwglfb
title: msXL_323r_48_29.tif
DOI: 10.5281/zenodo.17128106
title: inlists for the supernova progenitors adopted in "Discovery of a likely Type II SN at z=3.6 with JWST" by D. A. Coulter et al.

description: inlists used for making the red supergiant supernova progenitor models adopted for modeling the light curves of AT 2023adsv presented by D. A. Coulter et al. (https://arxiv.org/abs/2501.05513). MESA version r23.05.1 and mesasdk version 23.05.1 are used. "inlist_project_12" is for the 12 Msun progenitor, "inlist_project_16" is for the 16 Msun progenitor, and "inlist_project_20" is for the 20 Msun progenitor.
DOI: 10.25346/s6eecgix/vi8ucf
title: msXL_323r_48_28.tif
DOI: 10.25346/s6eecgix/4rib3i
title: msXL_323r_48_27.tif
DOI: 10.25346/s6eecgix/yxlzys
title: msXL_323r_48_26.tif
DOI: 10.25346/s6eecgix/zucpqx
title: msXL_323r_48_25.tif
DOI: 10.25346/s6eecgix/b1nib7
title: msXL_323r_31_24.tif
DOI: 10.25346/s6eecgix/op6rcp
title: msXL_323r_31_23.tif
DOI: 10.25346/s6eecgix/sdxhna
title: msXL_323r_31_22.tif
DOI: 10.25346/s6eecgix/rpvklk
title: msXL_323r_31_21.tif
DOI: 10.25346/s6eecgix/fvcoya
title: msXL_323r_31_20.tif
DOI: 10.25346/s6eecgix/iljmx8
title: msXL_323r_31_19.tif
DOI: 10.25346/s6eecgix/sakdyf
title: msXL_323r_31_18.tif
DOI: 10.25346/s6eecgix/ssuqny
title: msXL_323r_31_17.tif
DOI: 10.25346/s6eecgix/kynhuv
title: msXL_323r_31_16.tif
DOI: 10.25346/s6eecgix/ayhil4
title: msXL_323r_31_15.tif
DOI: 10.25346/s6eecgix/k480x6
title: msXL_323r_31_14.tif
DOI: 10.25346/s6eecgix/qkgdyh
title: msXL_323r_31_13.tif
DOI: 10.25346/s6eecgix/fkypmc
title: msXL_323r_15_12.tif
DOI: 10.25346/s6eecgix/3vacrg
title: msXL_323r_15_11.tif
DOI: 10.25346/s6eecgix/vdvzec
title: msXL_323r_15_10.tif
DOI: 10.25346/s6eecgix/6hve7g
title: msXL_323r_15_09.tif
DOI: 10.25346/s6eecgix/gco6sc
title: msXL_323r_15_08.tif
DOI: 10.25346/s6eecgix/aom0pn
title: msXL_323r_15_07.tif
DOI: 10.15468/dl.rbtq6n
title: Occurrence Download

description: A dataset containing 166 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Solanum corneliomulleri"
 ]
}

 The dataset includes 166 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004980-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25346/s6eecgix/hiqzd2
title: msXL_323r_15_06.tif
DOI: 10.25346/s6eecgix/flm5wa
title: msXL_323r_15_05.tif
DOI: 10.25346/s6eecgix/bdhxe2
title: msXL_323r_15_04.tif
DOI: 10.25346/s6eecgix/yuseis
title: msXL_323r_15_03.tif
DOI: 10.25346/s6eecgix/vbkyfy
title: msXL_323r_15_02.tif
DOI: 10.25346/s6eecgix/nwzftk
title: msXL_323r_15_01.tif
DOI: 10.25346/s6eecgix/vyqaq9
title: msXL_323r_0_ring.tif
DOI: 10.25346/s6eecgix/khd6dk
title: msXL_323r.tif
DOI: 10.25346/s6eecgix/zqqtyk
title: skin.svg