Ascending_neuron_screen

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1 to 10 of 14 Results

Individual Driver line videos Dec 23, 2022 Ramdya, Pavan P, 2022, "Individual Driver line videos", https://doi.org/10.7910/DVN/B0O5DX, Harvard Dataverse, V1 Individual representative videos for each of 50 active driver lines analyzed in this study.
Raw MCFO confocal imaging data from AN split-Gal4 driver lines Aug 1, 2022 Chen, Chin-Lin, 2022, "Raw MCFO confocal imaging data from AN split-Gal4 driver lines", https://doi.org/10.7910/DVN/S8AMKU, Harvard Dataverse, V1 Raw confocal images showing random labeling of neurons in Ascending Neuron split-Gal4 driver lines.
Linear regression results Aug 1, 2022 Aymanns, Florian, 2022, "Linear regression results", https://doi.org/10.7910/DVN/MFE9Z6, Harvard Dataverse, V1 This dataset contains all of the regression results presented in matrix form in Chen et al., 2022. The matrices show the cross validation average. The csv files provided here contain the results of each individual cross validation fold.
Neural and behavior data Aug 1, 2022 Aymanns, Florian, 2022, "Neural and behavior data", https://doi.org/10.7910/DVN/MR7CH8, Harvard Dataverse, V1 These csv files contain synchronized neural activity and behavior information. They were the basis for our regression analysis linking neural activity to behavior.
Table S1 Aug 1, 2022 Aymanns, Florian, 2022, "Table S1", https://doi.org/10.7910/DVN/ECTIVC, Harvard Dataverse, V1 This is a machine-readable version of Table S1 from Chen et al., 2022.
02_Fig2_output_of_published_version Feb 8, 2022 Chen, Chin-Lin, 2022, "02_Fig2_output_of_published_version", https://doi.org/10.7910/DVN/V3ZZXK, Harvard Dataverse, V1 Intermediate results for the behavior classifier, synchronized behavioral and neural data used for the General Linear Model (GLM). Also for generating plots of DF/F predictions based on behavior, the turning matrix, turning-based predictions of DF/F, convolved PEs predictions of DF/F. The subfolder named "overview_..." includes the intermediate res...
00_behavior_data_preprocess Feb 6, 2022 Chen, Chin-Lin, 2022, "00_behavior_data_preprocess", https://doi.org/10.7910/DVN/RCDYAS, Harvard Dataverse, V1 Behavioral data including DeepFly3D poses, ball rotational velocities, proboscis extension tracking, CO2 puffs, and their metadata. Please download to the corresponding folder.
DLC_model_for_labelling_proboscis Feb 4, 2022 Chen, Chin-Lin, 2022, "DLC_model_for_labelling_proboscis", https://doi.org/10.7910/DVN/RBFQ0Z, Harvard Dataverse, V1 A DeepLabCut model trained for labeling the tip and base of a fly's proboscis.
01_behavior_annotation_for_behavior_classifier Feb 4, 2022 Chen, Chin-Lin, 2022, "01_behavior_annotation_for_behavior_classifier", https://doi.org/10.7910/DVN/VTKY8B, Harvard Dataverse, V1 Behavioral annotations needed to train the behavior classifier. Please follow the instructions in Git to download these data into the proper location and to run the associated analysis pipeline.
04_mcfo_traced_singleAN_exp Feb 4, 2022 Chen, Chin-Lin, 2022, "04_mcfo_traced_singleAN_exp", https://doi.org/10.7910/DVN/1Y1GRJ, Harvard Dataverse, V1 Raw confocal stacks and traced stacks for individual ANs labeled using MCFO. Please follow the instructions in Git to download these data to the proper location and to run the associated analysis pipeline.

Individual Driver line videos

Dec 23, 2022

Ramdya, Pavan P, 2022, "Individual Driver line videos", https://doi.org/10.7910/DVN/B0O5DX, Harvard Dataverse, V1

Individual representative videos for each of 50 active driver lines analyzed in this study.

Raw MCFO confocal imaging data from AN split-Gal4 driver lines

Aug 1, 2022

Chen, Chin-Lin, 2022, "Raw MCFO confocal imaging data from AN split-Gal4 driver lines", https://doi.org/10.7910/DVN/S8AMKU, Harvard Dataverse, V1

Raw confocal images showing random labeling of neurons in Ascending Neuron split-Gal4 driver lines.

Linear regression results

Aug 1, 2022

Aymanns, Florian, 2022, "Linear regression results", https://doi.org/10.7910/DVN/MFE9Z6, Harvard Dataverse, V1

This dataset contains all of the regression results presented in matrix form in Chen et al., 2022. The matrices show the cross validation average. The csv files provided here contain the results of each individual cross validation fold.

Neural and behavior data

Aug 1, 2022

Aymanns, Florian, 2022, "Neural and behavior data", https://doi.org/10.7910/DVN/MR7CH8, Harvard Dataverse, V1

These csv files contain synchronized neural activity and behavior information. They were the basis for our regression analysis linking neural activity to behavior.

Table S1

Aug 1, 2022

Aymanns, Florian, 2022, "Table S1", https://doi.org/10.7910/DVN/ECTIVC, Harvard Dataverse, V1

This is a machine-readable version of Table S1 from Chen et al., 2022.

02_Fig2_output_of_published_version

Feb 8, 2022

Chen, Chin-Lin, 2022, "02_Fig2_output_of_published_version", https://doi.org/10.7910/DVN/V3ZZXK, Harvard Dataverse, V1

Intermediate results for the behavior classifier, synchronized behavioral and neural data used for the General Linear Model (GLM). Also for generating plots of DF/F predictions based on behavior, the turning matrix, turning-based predictions of DF/F, convolved PEs predictions of DF/F. The subfolder named "overview_..." includes the intermediate res...

00_behavior_data_preprocess

Feb 6, 2022

Chen, Chin-Lin, 2022, "00_behavior_data_preprocess", https://doi.org/10.7910/DVN/RCDYAS, Harvard Dataverse, V1

Behavioral data including DeepFly3D poses, ball rotational velocities, proboscis extension tracking, CO2 puffs, and their metadata. Please download to the corresponding folder.

DLC_model_for_labelling_proboscis

Feb 4, 2022

Chen, Chin-Lin, 2022, "DLC_model_for_labelling_proboscis", https://doi.org/10.7910/DVN/RBFQ0Z, Harvard Dataverse, V1

A DeepLabCut model trained for labeling the tip and base of a fly's proboscis.

01_behavior_annotation_for_behavior_classifier

Feb 4, 2022

Chen, Chin-Lin, 2022, "01_behavior_annotation_for_behavior_classifier", https://doi.org/10.7910/DVN/VTKY8B, Harvard Dataverse, V1

Behavioral annotations needed to train the behavior classifier. Please follow the instructions in Git to download these data into the proper location and to run the associated analysis pipeline.

04_mcfo_traced_singleAN_exp

Feb 4, 2022

Chen, Chin-Lin, 2022, "04_mcfo_traced_singleAN_exp", https://doi.org/10.7910/DVN/1Y1GRJ, Harvard Dataverse, V1

Raw confocal stacks and traced stacks for individual ANs labeled using MCFO. Please follow the instructions in Git to download these data to the proper location and to run the associated analysis pipeline.

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