cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.35077/age-fig
title: Or_3_Fly_2: Whole brain confocal imaging of fly driver line C155-GAL4, UAS-nSyb.eGFP (red channel) with the indicated bristle dye filled using DiD (green channel)

description: Confocal z-stacks (.tiff stacks) of single labeled mechanosensory neurons that innervate specific bristles on the Drosophila melanogaster head. The bristle mechanosensory neurons (BMNs) were labeled by dye filling from specific head bristles using the dye DiD. The processed images of the BMNs are published in Figure 4 and Figure 4 – figure supplements 1-5 of the manuscript “Somatotopic organization among parallel sensory pathways that promote a grooming sequence in Drosophila (https://doi.org/10.1101/2023.02.11.528119)”. The confocal stacks used to produce these images are organized based on the specific bristle filled (e.g. Oc or Ant_1 bristles) and the individual fly that was tested (e.g. Fly_1 and Fly_2).
description: A full protocol describing this work is available in the associated manuscript at manuscript “Somatotopic organization among parallel sensory pathways that promote a grooming sequence in Drosophila (https://doi.org/10.1101/2023.02.11.528119)”.
description: DATASET METADATA: title:'Or_3_Fly_2: Whole brain confocal imaging of fly driver line C155-GAL4, UAS-nSyb.eGFP (red channel) with the indicated bristle dye filled using DiD (green channel)', GeneralModality:'other', Technique:'other', Other:'Bristle dye fill' SPECIMEN METADATA: LocalID:'Or_3_Fly_2', Species:'Fruit fly', NCBITaxonomy:'NCBI:txid7227', Age:'5 to 8', Ageunit:'Days', Sex:'Male', Genotype:'w, C155-GAL4, UAS-nSyb.e GFP', OrganName:'Brain', SampleLocalID:'20190615_CL_major_1_left_brain_4_offtarget_DID_nsyb_GFP', Locations:'Subesophageal zone (SEZ)' INSTRUMENT METADATA: MicroscopeType:'Confocal', MicroscopeManufacturerAndModel:'Zeiss LSM 800 Axio Imager.Z2', ObjectiveName:'Zeiss 20x objective (Plan-Apochromat 20x/0.8)', ObjectiveNA:'0.8', ObjectiveMagnification:'20x' IMAGE METADATA: xAxis:'left-to-right', yAxis:'left-to-right', zAxis:'left-to-right', Number:'1,2,red,green', displayColor:'(1.0, 0.0, 0.0),(0.0, 1.0, 0.0)', stepSizeX:'0.6239', stepSizeY:'0.6239'
DOI: 10.35077/age-fin
title: Or_3_Fly_3: Whole brain confocal imaging of fly driver line C155-GAL4, UAS-nSyb.eGFP (red channel) with the indicated bristle dye filled using DiD (green channel)

description: Confocal z-stacks (.tiff stacks) of single labeled mechanosensory neurons that innervate specific bristles on the Drosophila melanogaster head. The bristle mechanosensory neurons (BMNs) were labeled by dye filling from specific head bristles using the dye DiD. The processed images of the BMNs are published in Figure 4 and Figure 4 – figure supplements 1-5 of the manuscript “Somatotopic organization among parallel sensory pathways that promote a grooming sequence in Drosophila (https://doi.org/10.1101/2023.02.11.528119)”. The confocal stacks used to produce these images are organized based on the specific bristle filled (e.g. Oc or Ant_1 bristles) and the individual fly that was tested (e.g. Fly_1 and Fly_2).
description: A full protocol describing this work is available in the associated manuscript at manuscript “Somatotopic organization among parallel sensory pathways that promote a grooming sequence in Drosophila (https://doi.org/10.1101/2023.02.11.528119)”.
description: DATASET METADATA: title:'Or_3_Fly_3: Whole brain confocal imaging of fly driver line C155-GAL4, UAS-nSyb.eGFP (red channel) with the indicated bristle dye filled using DiD (green channel)', GeneralModality:'other', Technique:'other', Other:'Bristle dye fill' SPECIMEN METADATA: LocalID:'Or_3_Fly_3', Species:'Fruit fly', NCBITaxonomy:'NCBI:txid7227', Age:'5 to 8', Ageunit:'Days', Sex:'Male', Genotype:'w, C155-GAL4, UAS-nSyb.e GFP', OrganName:'Brain', SampleLocalID:'20190511_CL_major_1_left_brain_2_DID_nsyb_GFP', Locations:'Subesophageal zone (SEZ)' INSTRUMENT METADATA: MicroscopeType:'Confocal', MicroscopeManufacturerAndModel:'Zeiss LSM 800 Axio Imager.Z2', ObjectiveName:'Zeiss 20x objective (Plan-Apochromat 20x/0.8)', ObjectiveNA:'0.8', ObjectiveMagnification:'20x' IMAGE METADATA: xAxis:'left-to-right', yAxis:'left-to-right', zAxis:'left-to-right', Number:'1,2,red,green', displayColor:'(1.0, 0.0, 0.0),(0.0, 1.0, 0.0)', stepSizeX:'0.6239', stepSizeY:'0.6239'
DOI: 10.35077/age-fit
title: Ant_1_Fly_1: Whole brain confocal imaging of fly driver line C155-GAL4, UAS-nSyb.eGFP (red channel) with the indicated bristle dye filled using DiD (green channel)

description: Confocal z-stacks (.tiff stacks) of single labeled mechanosensory neurons that innervate specific bristles on the Drosophila melanogaster head. The bristle mechanosensory neurons (BMNs) were labeled by dye filling from specific head bristles using the dye DiD. The processed images of the BMNs are published in Figure 4 and Figure 4 – figure supplements 1-5 of the manuscript “Somatotopic organization among parallel sensory pathways that promote a grooming sequence in Drosophila (https://doi.org/10.1101/2023.02.11.528119)”. The confocal stacks used to produce these images are organized based on the specific bristle filled (e.g. Oc or Ant_1 bristles) and the individual fly that was tested (e.g. Fly_1 and Fly_2).
description: A full protocol describing this work is available in the associated manuscript at manuscript “Somatotopic organization among parallel sensory pathways that promote a grooming sequence in Drosophila (https://doi.org/10.1101/2023.02.11.528119)”.
description: DATASET METADATA: title:'Ant_1_Fly_1: Whole brain confocal imaging of fly driver line C155-GAL4, UAS-nSyb.eGFP (red channel) with the indicated bristle dye filled using DiD (green channel)', GeneralModality:'other', Technique:'other', Other:'Bristle dye fill' SPECIMEN METADATA: LocalID:'Ant_1_Fly_1', Species:'Fruit fly', NCBITaxonomy:'NCBI:txid7227', Age:'5 to 8', Ageunit:'Days', Sex:'Male', Genotype:'w, C155-GAL4, UAS-nSyb.e GFP', OrganName:'Brain', SampleLocalID:'20190425_ANT_major_1_left_brain_1_and_off_target_DID_nsyb_eGFP', Locations:'Subesophageal zone (SEZ)' INSTRUMENT METADATA: MicroscopeType:'Confocal', MicroscopeManufacturerAndModel:'Zeiss LSM 800 Axio Imager.Z2', ObjectiveName:'Zeiss 20x objective (Plan-Apochromat 20x/0.8)', ObjectiveNA:'0.8', ObjectiveMagnification:'20x' IMAGE METADATA: xAxis:'left-to-right', yAxis:'left-to-right', zAxis:'left-to-right', Number:'1,2,red,green', displayColor:'(1.0, 0.0, 0.0),(0.0, 1.0, 0.0)', stepSizeX:'0.6239', stepSizeY:'0.6239'
DOI: 10.6084/m9.figshare.22721065.v2
title: Effects of seed traits on the cache size of a scatter-hoarding rodent, Leopoldamys edwardsi

description: We analyzed the effects of seed traits on the cache size of a scatter-hoarding rodent, Leopoldamys edwardsi
DOI: 10.5281/zenodo.8013288
title: NINCO (Out-Of-Distribution detection dataset for ImageNet)

description: The <strong>NINCO</strong> (<strong>N</strong>o <strong>I</strong>mageNet <strong>C</strong>lass <strong>O</strong>bjects) dataset is introduced in the ICML 2023 paper<strong> </strong>In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation. The images in this dataset are free from objects that belong to any of the 1000 classes of ImageNet-1K (ILSVRC2012), which makes NINCO suitable for evaluating out-of-distribution detection on ImageNet-1K . The NINCO main dataset consists of 64 OOD classes with a total of 5879 samples. These OOD classes were selected to have no categorical overlap with any classes of ImageNet-1K. Each sample was inspected individually by the authors to not contain ID objects. Besides NINCO, included are (in the same .tar.gz file) <strong>truly OOD versions of 11 popular OOD datasets</strong> with in total 2715 OOD samples. Further included are <strong>17 OOD unit-tests</strong>, with 400 samples each. Code for loading and evaluating on each of the three datasets is provided at <strong>https://github.com/j-cb/NINCO.</strong> When using <strong>NINCO</strong>, please consider citing (besides the bibtex given below) the following data sources that were used to create NINCO: Hendrycks et al.: ”Scaling out-of-distribution detection for real-world settings”, ICML, 2022. Bossard et al.: ”Food-101 – mining discriminative components with random forests”, ECCV 2014. Zhou et al.: ”Places: A 10 million image database for scene recognition”, IEEE PAMI 2017. Huang et al.: ”Mos: Towards scaling out-of-distribution detection for large semantic space”, CVPR 2021. Li et al.: ”Caltech 101 (1.0)”, 2022. Ismail et al.: ”MYNursingHome: A fully-labelled image dataset for indoor object classification.”, Data in Brief (V. 32) 2020. The iNaturalist project: https://www.inaturalist.org/ When using <strong>NINCO_popular_datasets_subsamples</strong>, additionally to the above, please consider citing: Cimpoi et al.: ”Describing textures in the wild”, CVPR 2014. Hendrycks et al.: ”Natural adversarial examples”, CVPR 2021. Wang et al.: ”Vim: Out-of-distribution with virtual-logit matching”, CVPR 2022. Bendale et al.: ”Towards Open Set Deep Networks”, CVPR 2016. Vaze et al.: ”Open-set Recognition: a Good Closed-set Classifier is All You Need?”, ICLR 2022. Wang et al.: ”Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition.” ICML, 2022. Galil et al.: “A framework for benchmarking Class-out-of-distribution detection and its application to ImageNet”, ICLR 2023.<br> For citing our paper, we would appreciate using the following bibtex entry (this will be updated once the ICML 2023 proceedings are public): <br> @inproceedings{<br> bitterwolf2023ninco,<br> title={In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation},<br> author={Julian Bitterwolf and Maximilian Mueller and Matthias Hein},<br> booktitle={ICML},<br> year={2023},<br> url={https://proceedings.mlr.press/v202/bitterwolf23a.html}<br> }
DOI: 10.5281/zenodo.8013287
title: NINCO (Out-Of-Distribution detection dataset for ImageNet)

description: The <strong>NINCO</strong> (<strong>N</strong>o <strong>I</strong>mageNet <strong>C</strong>lass <strong>O</strong>bjects) dataset is introduced in the ICML 2023 paper<strong> </strong>In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation. The images in this dataset are free from objects that belong to any of the 1000 classes of ImageNet-1K (ILSVRC2012), which makes NINCO suitable for evaluating out-of-distribution detection on ImageNet-1K . The NINCO main dataset consists of 64 OOD classes with a total of 5879 samples. These OOD classes were selected to have no categorical overlap with any classes of ImageNet-1K. Each sample was inspected individually by the authors to not contain ID objects. Besides NINCO, included are (in the same .tar.gz file) <strong>truly OOD versions of 11 popular OOD datasets</strong> with in total 2715 OOD samples. Further included are <strong>17 OOD unit-tests</strong>, with 400 samples each. Code for loading and evaluating on each of the three datasets is provided at <strong>https://github.com/j-cb/NINCO.</strong> When using <strong>NINCO</strong>, please consider citing (besides the bibtex given below) the following data sources that were used to create NINCO: Hendrycks et al.: ”Scaling out-of-distribution detection for real-world settings”, ICML, 2022. Bossard et al.: ”Food-101 – mining discriminative components with random forests”, ECCV 2014. Zhou et al.: ”Places: A 10 million image database for scene recognition”, IEEE PAMI 2017. Huang et al.: ”Mos: Towards scaling out-of-distribution detection for large semantic space”, CVPR 2021. Li et al.: ”Caltech 101 (1.0)”, 2022. Ismail et al.: ”MYNursingHome: A fully-labelled image dataset for indoor object classification.”, Data in Brief (V. 32) 2020. The iNaturalist project: https://www.inaturalist.org/ When using <strong>NINCO_popular_datasets_subsamples</strong>, additionally to the above, please consider citing: Cimpoi et al.: ”Describing textures in the wild”, CVPR 2014. Hendrycks et al.: ”Natural adversarial examples”, CVPR 2021. Wang et al.: ”Vim: Out-of-distribution with virtual-logit matching”, CVPR 2022. Bendale et al.: ”Towards Open Set Deep Networks”, CVPR 2016. Vaze et al.: ”Open-set Recognition: a Good Closed-set Classifier is All You Need?”, ICLR 2022. Wang et al.: ”Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition.” ICML, 2022. Galil et al.: “A framework for benchmarking Class-out-of-distribution detection and its application to ImageNet”, ICLR 2023.<br> For citing our paper, we would appreciate using the following bibtex entry (this will be updated once the ICML 2023 proceedings are public): <br> @inproceedings{<br> bitterwolf2023ninco,<br> title={In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation},<br> author={Julian Bitterwolf and Maximilian Mueller and Matthias Hein},<br> booktitle={ICML},<br> year={2023},<br> url={https://proceedings.mlr.press/v202/bitterwolf23a.html}<br> }
DOI: 10.35077/age-fix
title: Ant_1_Fly_2: Whole brain confocal imaging of fly driver line C155-GAL4, UAS-nSyb.eGFP (red channel) with the indicated bristle dye filled using DiD (green channel)

description: Confocal z-stacks (.tiff stacks) of single labeled mechanosensory neurons that innervate specific bristles on the Drosophila melanogaster head. The bristle mechanosensory neurons (BMNs) were labeled by dye filling from specific head bristles using the dye DiD. The processed images of the BMNs are published in Figure 4 and Figure 4 – figure supplements 1-5 of the manuscript “Somatotopic organization among parallel sensory pathways that promote a grooming sequence in Drosophila (https://doi.org/10.1101/2023.02.11.528119)”. The confocal stacks used to produce these images are organized based on the specific bristle filled (e.g. Oc or Ant_1 bristles) and the individual fly that was tested (e.g. Fly_1 and Fly_2).
description: A full protocol describing this work is available in the associated manuscript at manuscript “Somatotopic organization among parallel sensory pathways that promote a grooming sequence in Drosophila (https://doi.org/10.1101/2023.02.11.528119)”.
description: DATASET METADATA: title:'Ant_1_Fly_2: Whole brain confocal imaging of fly driver line C155-GAL4, UAS-nSyb.eGFP (red channel) with the indicated bristle dye filled using DiD (green channel)', GeneralModality:'other', Technique:'other', Other:'Bristle dye fill' SPECIMEN METADATA: LocalID:'Ant_1_Fly_2', Species:'Fruit fly', NCBITaxonomy:'NCBI:txid7227', Age:'5 to 8', Ageunit:'Days', Sex:'Male', Genotype:'w, C155-GAL4, UAS-nSyb.e GFP', OrganName:'Brain', SampleLocalID:'20190512_ANT_major_1_left_brain_2_DID_nsyb_GFP', Locations:'Subesophageal zone (SEZ)' INSTRUMENT METADATA: MicroscopeType:'Confocal', MicroscopeManufacturerAndModel:'Zeiss LSM 800 Axio Imager.Z2', ObjectiveName:'Zeiss 20x objective (Plan-Apochromat 20x/0.8)', ObjectiveNA:'0.8', ObjectiveMagnification:'20x' IMAGE METADATA: xAxis:'left-to-right', yAxis:'left-to-right', zAxis:'left-to-right', Number:'1,2,red,green', displayColor:'(1.0, 0.0, 0.0),(0.0, 1.0, 0.0)', stepSizeX:'0.6239', stepSizeY:'0.6239'
DOI: 10.35077/age-fly
title: Ant_1_Fly_3: Whole brain confocal imaging of fly driver line C155-GAL4, UAS-nSyb.eGFP (red channel) with the indicated bristle dye filled using DiD (green channel)

description: Confocal z-stacks (.tiff stacks) of single labeled mechanosensory neurons that innervate specific bristles on the Drosophila melanogaster head. The bristle mechanosensory neurons (BMNs) were labeled by dye filling from specific head bristles using the dye DiD. The processed images of the BMNs are published in Figure 4 and Figure 4 – figure supplements 1-5 of the manuscript “Somatotopic organization among parallel sensory pathways that promote a grooming sequence in Drosophila (https://doi.org/10.1101/2023.02.11.528119)”. The confocal stacks used to produce these images are organized based on the specific bristle filled (e.g. Oc or Ant_1 bristles) and the individual fly that was tested (e.g. Fly_1 and Fly_2).
description: A full protocol describing this work is available in the associated manuscript at manuscript “Somatotopic organization among parallel sensory pathways that promote a grooming sequence in Drosophila (https://doi.org/10.1101/2023.02.11.528119)”.
description: DATASET METADATA: title:'Ant_1_Fly_3: Whole brain confocal imaging of fly driver line C155-GAL4, UAS-nSyb.eGFP (red channel) with the indicated bristle dye filled using DiD (green channel)', GeneralModality:'other', Technique:'other', Other:'Bristle dye fill' SPECIMEN METADATA: LocalID:'Ant_1_Fly_3', Species:'Fruit fly', NCBITaxonomy:'NCBI:txid7227', Age:'5 to 8', Ageunit:'Days', Sex:'Male', Genotype:'w, C155-GAL4, UAS-nSyb.e GFP', OrganName:'Brain', SampleLocalID:'20190531_ANT_major_1_left_brain_4_DID_nsyb_GFP', Locations:'Subesophageal zone (SEZ)' INSTRUMENT METADATA: MicroscopeType:'Confocal', MicroscopeManufacturerAndModel:'Zeiss LSM 800 Axio Imager.Z2', ObjectiveName:'Zeiss 20x objective (Plan-Apochromat 20x/0.8)', ObjectiveNA:'0.8', ObjectiveMagnification:'20x' IMAGE METADATA: xAxis:'left-to-right', yAxis:'left-to-right', zAxis:'left-to-right', Number:'1,2,red,green', displayColor:'(1.0, 0.0, 0.0),(0.0, 1.0, 0.0)', stepSizeX:'0.6239', stepSizeY:'0.6239'
DOI: 10.17632/dyr8hgfnhb.1
title: LINF_350043100

description: Hypothetical protein - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.17632/fp46m4gvps.1
title: A Semiotics Analysis Found on Music Video of You Belong with Me by Taylor Swift

description: This research entitles “A Semiotics Analysis Found on Music Vidio of You Belong with Me”.The aim of this research was to investigate and analyze the verbal and visual signs and the meaning itself in the music video of “You Belong with Me” by Taylor Swift. The type of this research was qualitative research. In collecting data, the writer used the method of observation and documentation by classifying videos into pictures in the form of sequences.The results of this study indicate that the semiotic signs contained in this music video are in the form of visual displays contained in body language in the music video which tells about a male friend that Swift likes who actually has a lover, and verbal signs contained in the music video is a paper that contains writing that is used to communicate. Based on the result of the analysis,it can be concluded as there are two classifications,namely: verbal sign and visual sign. In verbal sign, it was found eight data. In visual sign, it was found seven data. The concept of music video of You Belong With Me describe someone who is in love with someone where that person has been with a lover who doesn't appreciate it at all. In the data found, verbal and visual sign explained about caring, disappointment, jealousy, and express feelings.
DOI: 10.17632/g733dr6wnx.1
title: Slang Variations in “Black Eyed Peas” Songs

description: This article explores the use of sociolinguistic slang in the lyrics of Black Eyed Peas songs. The study employs qualitative research methods to analyze the language variations and unconventional vocabulary present in the music text. By observing and recording the lyrics of several Black Eyed Peas songs, the researcher examines how slang contributes to the appeal and relatability of the group's music. Sociolinguistic slang is seen as a form of informal language that develops within specific social groups, often serving as a means of social identification and solidarity. The study reveals that the Black Eyed Peas incorporate slang to establish a contemporary and informal flavor in their songs, facilitating a connection with their audience. The findings contribute to our understanding of how slang can shape the cultural and linguistic aspects of music, highlighting its role in expressing identity and fostering social connections.
DOI: 10.17632/34gysk34xv.1
title: The Sign Found in The Movie “Mr Harrigan’s Phone”

description: The title of this research is “The Sign Found in The Movie Mr Harrigan’s Phone”. This research has difference with the other previous. Previous research was analyzing kind and function. This research analyzes types and meaning by Peirce and Barthes. Previous research used action, adventure, and fantasy movie genres. This reseach used horror and drama genres. The purpose of this research are observe and provide information to readers about the types and inform the meanings contained in the movie "Mr. Harrigan's Phone". Therefore this research can inform the reader's about sign systems contained in the movie “Mr. Harrigan's Phone” and this research can be expected a reference for readers as to learn the sign. This research used qualitative method by Creswell to analyze the data. This research has 2 problems.  The first is type used Peirce's theory. Peirce has three types of signs namely icon, index, and symbol. The results of the identification in the Mr Harrigan's Phone movie get 5 icons, 5 indexes and 5 symbols. The second is the meaning.  This study used Barthes' theory to identifying meaning. Barthes has 3 system namely denotation, connotation, and myth.
DOI: 10.35077/age-fog
title: Ant_2_Fly_1: Whole brain confocal imaging of fly driver line C155-GAL4, UAS-nSyb.eGFP (red channel) with the indicated bristle dye filled using DiD (green channel)

description: Confocal z-stacks (.tiff stacks) of single labeled mechanosensory neurons that innervate specific bristles on the Drosophila melanogaster head. The bristle mechanosensory neurons (BMNs) were labeled by dye filling from specific head bristles using the dye DiD. The processed images of the BMNs are published in Figure 4 and Figure 4 – figure supplements 1-5 of the manuscript “Somatotopic organization among parallel sensory pathways that promote a grooming sequence in Drosophila (https://doi.org/10.1101/2023.02.11.528119)”. The confocal stacks used to produce these images are organized based on the specific bristle filled (e.g. Oc or Ant_1 bristles) and the individual fly that was tested (e.g. Fly_1 and Fly_2).
description: A full protocol describing this work is available in the associated manuscript at manuscript “Somatotopic organization among parallel sensory pathways that promote a grooming sequence in Drosophila (https://doi.org/10.1101/2023.02.11.528119)”.
description: DATASET METADATA: title:'Ant_2_Fly_1: Whole brain confocal imaging of fly driver line C155-GAL4, UAS-nSyb.eGFP (red channel) with the indicated bristle dye filled using DiD (green channel)', GeneralModality:'other', Technique:'other', Other:'Bristle dye fill' SPECIMEN METADATA: LocalID:'Ant_2_Fly_1', Species:'Fruit fly', NCBITaxonomy:'NCBI:txid7227', Age:'5 to 8', Ageunit:'Days', Sex:'Male', Genotype:'w, C155-GAL4, UAS-nSyb.e GFP', OrganName:'Brain', SampleLocalID:'20190512_ANT_major_2_left_brain_1_DID_nsyb_GFP', Locations:'Subesophageal zone (SEZ)' INSTRUMENT METADATA: MicroscopeType:'Confocal', MicroscopeManufacturerAndModel:'Zeiss LSM 800 Axio Imager.Z2', ObjectiveName:'Zeiss 20x objective (Plan-Apochromat 20x/0.8)', ObjectiveNA:'0.8', ObjectiveMagnification:'20x' IMAGE METADATA: xAxis:'left-to-right', yAxis:'left-to-right', zAxis:'left-to-right', Number:'1,2,red,green', displayColor:'(1.0, 0.0, 0.0),(0.0, 1.0, 0.0)', stepSizeX:'0.6239', stepSizeY:'0.6239'