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

description: A dataset containing 94186183 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is iNaturalist Research-grade Observations"
 ]
}

 The dataset includes 94186183 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0108150-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/usctheses-ouc113998uip
title: Nayib Bukele & The Rise of Authoritarian Populism in El Salvador: Inevitable or Avoidable?
DOI: 10.5281/zenodo.13527819
title: PTA SWIR and LWIR Spectral Libraries

description: Spectral libraries that encompass the mineral endmembers identified most commonly in the high resolution images
DOI: 10.5281/zenodo.13527820
title: PTA SWIR and LWIR Spectral Libraries

description: Spectral libraries that encompass the mineral endmembers identified most commonly in the high resolution images
DOI: 10.25549/usctheses-ouc113998tfs
title: Self-reported and physiological responses to hate speech and criticisms of systemic social inequality: an investigation of response patterns and their mediation…
DOI: 10.25549/usctheses-ouc113998tfk
title: Corporate instructional design in the modern day: ID perceptions of practice
DOI: 10.25549/usctheses-ouc113998tf0
title: Dismantling settler crimmigration: histories of Indigenous transborder mobilities
DOI: 10.15468/dl.q2ftsv
title: Occurrence Download

description: A dataset containing 4736 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "Issue is one of (Zero coordinate, Coordinate out of range, Coordinate invalid, Coordinate rounded, Geodetic datum invalid, Geodetic datum assumed WGS84, Coordinate reprojected, Coordinate reprojection failed, Coordinate reprojection suspicious, Coordinate accuracy invalid, Coordinate precision invalid, Coordinate uncertainty meters invalid, Coordinate precision/uncertainty mismatch, Footprint SRS invalid, Footprint WKT mismatch, Footprint WKT invalid, Country coordinate mismatch, Country mismatch, Country invalid, Country derived from coordinates, Continent coordinate mismatch, Continent country mismatch, Continent invalid, Continent derived from country, Continent derived from coordinates, Presumed swapped coordinate, Presumed negated longitude, Presumed negated latitude, Recorded date mismatch, Recorded date invalid, Recorded date unlikely, Taxon match fuzzy, Taxon match higher rank, Taxon match aggregate, The scientific name ID was not used in backbone matching, The taxon concept ID was not used in backbone matching, The taxon ID was not used in backbone matching, The scientific name ID was not found in the checklist, The taxon concept ID was not found in the checklist, The taxon ID was not found in the checklist, The scientific name differs on the record from that found in the checklist, Taxon match none, The backbone match found using the taxon IDs differs from what would be found if using the scientific names , Depth not metric, Depth unlikely, Depth min max swapped, Depth non numeric, Elevation unlikely, Elevation min max swapped, Elevation not metric, Elevation non numeric, Modified date invalid, Modified date unlikely, Identified date unlikely, Identified date invalid, Basis of record invalid, Type status invalid, Multimedia date invalid, Multimedia URI invalid, References URI invalid, Error during interpretation, Individual count invalid, Individual count conflicts with occurrence status, Occurrence status unparsable, Occurrence status inferred from individual count, ccurrence status inferred from basis of record, Georeferenced date unlikely, Georeferenced date invalid, Ambiguous institution, Ambiguous collection, Institution match none, Collection match none, Institution match fuzzy, Collection match fuzzy, Institution collection mismatch, Possibly on loan, Different owner institution)",
 "OccurrenceStatus is Present",
 "TaxonKey is Parapiptadenia Brenan"
 ]
}

 The dataset includes 4736 records from 193 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0108163-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25493/1kfr-pxz
title: 3D cyto- and receptor architectonic map of macaque lateral premotor area F4v

description: A multimodal analysis of the macaque motor cortex was carried out to create a 3D atlas integrating and reconciling discrepancies between previously published maps. This involved a quantitative and statistically testable approach to the analysis of cytoarchitecture in the macaque monkey frontal agranular cortex, in combination with an in vitro quantitative receptor autoradiographic analysis of the regional and laminar distribution patterns of 13 different neurotransmitter receptors. The borders of the identified areas were traced onto a reference macaque monkey brain in stereotaxic space. We provide here for the first time a map of lateral premotor area F4v of the macaque monkey brain integrating cyto- and receptor architectonic features in stereotaxic space. It constitutes a valuable resource for the analysis of functional experiments with non-human primates, and provides crucial data for modeling approaches with realistic synaptic dynamics.
DOI: 10.25493/3e6y-e8g
title: Study of Slow Waves (SWs) propagation through wide-field calcium imaging of the right cortical hemisphere of GCaMP6f mice

description: Slow waves (SWs) are spatio-temporal patterns of cortical activity that occur both during natural sleep and anesthesia and are preserved across species. Even though electrophysiological recordings have been largely used to characterize brain states, they are limited in spatial resolution and cannot target specific neuronal populations. Recently, large-scale optical imaging techniques coupled with functional indicators overcame these restrictions and will give new important insight on the SW phenomenon. Here we combined wide-field fluorescence microscopy and a transgenic mouse model expressing a calcium indicator (GCaMP6f) in excitatory neurons to study and model SW propagation. This dataset is used in the analytical workflow ["Slow Wave Analysis Pipeline (SWAP)"](https://wiki.ebrains.eu/bin/view/Collabs/slow-wave-analysis-pipeline).
DOI: 10.25493/nhq9-kv2
title: Probabilistic map of the right fornix (atlas of deep white matter fibre bundles, version 2018)

description: This data contains the probability map of the right fornix (atlas of deep white matter fibre bundles, version 2018), in the MNI ICBM152 reference brain. This bundle was identified by fibre clustering, from the tractography datasets of 78 subjects in Neurospin’s ARCHI database. The map shows the probability of finding a fibre belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibres going through.
DOI: 10.25493/a7y0-nx9
title: Whole-brain parcellation of the Julich-Brain Cytoarchitectonic Atlas (v2.4)

description: This dataset contains the whole-brain parcellation of the Julich-Brain Cytoarchitectonic Atlas in the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space ([Evans et al., 2012](https://www.sciencedirect.com/science/article/pii/S1053811912000419?via%3Dihub)). The parcellation is derived from the individual probability maps (PMs) of the cytoarchitectonic regions released in the Julich-Brain Cytoarchitectonic Atlas, that are further combined into a Maximum Probability Map (MPM). The MPM is calculated by considering for each voxel the probability of all cytoarchitectonic areas released in the atlas, and determining the most probable assignment ([Eickhoff 2005](https://www.sciencedirect.com/science/article/pii/S105381190400792X?via%3Dihub)). Gap maps complement cortical maps to achieve full cortical coverage. The table “Julich-Brain\_shrinkage\_corrected\_individual\_volumes” contains shrinkage corrected individual volumes and hemispheric and gender specific coefficients of variations of all Julich-Brain areas. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.
DOI: 10.25493/9nnw-tjm
title: In-depth characterization of striatal neurons in non-human primates using single-cell RNA-seq

description: The goal was to define the cellular subpopulation structure in the striatum of non-human primates (NHP) and the characterization of the functional and pathologic changes associated with nigro-striatal denervation and L-dopa treatment for striatal neurons and interneurons. Therefore, after isolation of NeunN-expressing nuclei from fresh-frozen striatal tissues (putamen and caudate nucleus), nuclei were used to generate single-cell transcriptomes using a commercial microwell-based assay. After library preparation and sequencing, the generated data were further processed and aligned against the reference genome. Gene expression matrixes are provided which can be further analyzed and overlaid with synaptic maps and organization, microanatomical and physiological data, and integrated for the generation of a macaca fascicularis brain atlas.
DOI: 10.25493/9zwb-eez
title: Probabilistic cytoarchitectonic map of Area Fo3 (OFC) (v3.2)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area Fo3 (OFC) in the individual, single subject template of the MNI Colin 27 reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to the reference space, where each voxel was assigned the probability to belong to Area Fo3 (OFC). The probability map of Area Fo3 (OFC) is provided in NifTi format for each hemisphere in the reference space. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and updated probability estimates for new brain structures may in some cases lead to measurable but negligible deviations of existing probability maps, as compared to earlier released datasets. Other available data versions of Area Fo3 (OFC): Henssen et al. (2019) [Data set, v3.4] [DOI: 10.25493/E1YQ-65U](https://doi.org/10.25493%2FE1YQ-65U) The most probable delineation of Area Fo3 (OFC) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D)
DOI: 10.25493/xpce-cg1
title: Probabilistic cytoarchitectonic map of HC-Prosubiculum (Hippocampus) (v13.0)

description: This dataset contains the distinct architectonic HC-Prosubiculum (Hippocampus) in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to both reference spaces, where each voxel was assigned the probability to belong to HC-Prosubiculum (Hippocampus). The probability map of HC-Prosubiculum (Hippocampus) are provided in the NifTi format for each brain reference space and hemisphere. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. The most probable delineation of Entorhinal Cortex derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2020) [Data set, v2.5] [DOI: 10.25493/8JKE-M53](https://doi.org/10.25493/8JKE-M53)
DOI: 10.25493/yfp9-0jz
title: Probabilistic cytoarchitectonic map of Area STS1 (STS) (v5.0)

description: This dataset contains the distinct architectonic Area STS1 (STS) in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to both reference spaces, where each voxel was assigned the probability to belong to Area STS1 (STS). The probability map of Area STS1 (STS) is provided in the NifTi format for each brain reference space and hemisphere. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. Other available data versions of Area STS1 (STS): Zachlod et al. (2019) [Data set, v3.0] [DOI: 10.25493/2G11-1WA](https://doi.org/10.25493%2F2G11-1WA) Zachlod et al. (2019) [Data set, v3.1] [DOI: 10.25493/F6DF-H8P](https://doi.org/10.25493%2FF6DF-H8P) The most probable delineation of Area STS1 (STS) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D) Amunts et al. (2020) [Data set, v2.4] [DOI: 10.25493/A7Y0-NX9](https://doi.org/10.25493%2FA7Y0-NX9) Amunts et al. (2020) [Data set, v2.5] [DOI: 10.25493/8JKE-M53](https://doi.org/10.25493/8JKE-M53) Amunts et al. (2021) [Data set, v2.6] [DOI: 10.25493/KJQN-AM0](https://doi.org/10.25493%2FKJQN-AM0)
DOI: 10.25493/373s-nvk
title: Probabilistic cytoarchitectonic map of Entorhinal Cortex (v11b.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Entorhinal Cortex in the individual, single subject template of the MNI Colin 27 reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to the reference space, where each voxel was assigned the probability to belong to Entorhinal Cortex. The probability map of Entorhinal Cortex is provided in NifTi format for each hemisphere in the reference space. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and updated probability estimates for new brain structures may in some cases lead to measurable but negligible deviations of existing probability maps, as compared to earlier released datasets. Other available data versions of Entorhinal Cortex: Amunts et al. (2019) [Data set, v11.1] [DOI: 10.25493/KNXY-B1Z](https://doi.org/10.25493%2FKNXY-B1Z) The most probable delineation of Entorhinal Cortex derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D)
DOI: 10.25493/5kbv-36j
title: Probabilistic cytoarchitectonic map of Area OP2 (POperc) (v9.4)

description: This dataset contains the distinct architectonic Area OP2 (POperc) in the MNI Colin 27 and MNI ICBM 152 reference spaces. As part of the Julich-Brain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently, the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 and MNI ICBM 152 reference spaces where each voxel is assigned with the probability to belong to Area OP2 (POperc). The probability map of Area OP2 (POperc) is provided in the NifTi format for each brain reference space and hemisphere. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. Other available data versions of Area OP2 (POperc): Eickhoff et al. (2018) [Data set, v9.2] [DOI: 10.25493/F8W5-HNB](https://doi.org/10.25493%2FF8W5-HNB) Eickhoff et al. (2020) [Data set, v11.0] [DOI: 10.25493/SDW0-YEZ](https://doi.org/10.25493%2FSDW0-YEZ) The most probable delineation of Area OP2 (POperc) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D)
DOI: 10.34810/data72/359
title: 01CO_1.MR.0004.0221.2016.02.04.15.44.11.109375.67143776.IMA
DOI: 10.25493/knxy-b1z
title: Probabilistic cytoarchitectonic map of Entorhinal Cortex (v11.1)

description: This dataset contains the distinct architectonic Entorhinal Cortex in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to both reference spaces, where each voxel was assigned the probability to belong to Entorhinal Cortex. The probability map of Entorhinal Cortex is provided in the NifTi format for each brain reference space and hemisphere. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. Other available data versions of Entorhinal Cortex: Amunts et al. (2018) [Data set, v11b.0] [DOI: 10.25493/373S-NVK](https://doi.org/10.25493%2F373S-NVK) The most probable delineation of Entorhinal Cortex derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D) Amunts et al. (2020) [Data set, v2.4] [DOI: 10.25493/A7Y0-NX9](https://doi.org/10.25493%2FA7Y0-NX9)
DOI: 10.25493/qjrz-c2x
title: Volume fraction of the mouse HLS1 cortical column per layer at light microscopy

description: Data on the volume occupied by the different elements (neuropil, neurons, glia and blood vessels) will be used to complete microanatomical data for the model of the mouse somatosensory cortex and to help the functional interpretation of the data obtained from in vivo physiological experiments. Volume fraction has been determined in the mouse hindlimb region of the somatosensory cortex (HLS1) using light microscopy in the following cortical layers: I, II, III, IV, Va, Vb and VI.
DOI: 10.25493/1db6-yxw
title: Density measurements of different receptors for Area Oc1B [rat, v1.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters of Area Oc1B obtained by means of quantitative _in vitro_ autoradiography. The receptor densities are visualized as _fingerprints_ (**fp**), which provide the mean density and standard deviation for each of the analyzed receptor types, averaged across samples. Overview of available measurements [ **receptor** \| **_neurotransmitter_** \| _labeling agent_ ]: **AMPA** \| **_glutamate_** \| _[<sup>3</sup>H]AMPA_ **kainate** \| **_glutamate_** \| _[<sup>3</sup>H]kainate_ **NMDA** \| **_glutamate_** \| _[<sup>3</sup>H]MK-801_ **GABA<sub>A</sub>** \| **_GABA_** \| _[<sup>3</sup>H]muscimol_ **GABA<sub>B</sub>** \| **_GABA_** \| _[<sup>3</sup>H]CGP54626_ **GABA<sub>A</sub>/BZ** \| **_GABA_** \| _[<sup>3</sup>H]flumazenil_ **muscarinic M<sub>1</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]pirenzepine_ **muscarinic M<sub>2</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]oxotremorine-M_ **muscarinic M<sub>3</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]4-DAMP_ **nicotinic α<sub>4</sub>β<sub>2</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]epibatidine_ **α<sub>1</sub>** \| **_noradrenalin/norepinephrine_** \| _[<sup>3</sup>H]prazosin_ **α<sub>2</sub>** \| **_noradrenalin/norepinephrine_** \| _[<sup>3</sup>H]UK-14,304_ **5-HT<sub>1A</sub>** \| **_serotonin_** \| _[<sup>3</sup>H]8-OH-DPAT_ **5-HT<sub>2</sub>** \| **_serotonin_** \| _[<sup>3</sup>H]ketanserin_ **D<sub>1</sub>** \| **_dopamine_** \| _[<sup>3</sup>H]SCH23390_ Information on the used tissue samples and corresponding subjects, as well as analyzed receptors accompanies the provided dataset. **For methodological details, see:** Zilles, K. et al. (2002). Quantitative analysis of cyto- and receptorarchitecture of the human brain, pp. 573-602. In: Brain Mapping: The Methods, 2nd edition (A.W. Toga and J.C. Mazziotta, eds.). San Diego, Academic Press. Palomero-Gallagher N, Zilles K. (2018) Cyto- and receptorarchitectonic mapping of the human brain. In: Handbook of Clinical Neurology 150: 355-387