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README.md CHANGED
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  license: cc0-1.0
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  language:
4
  - en
5
- pretty_name: kabr-tools methodology dataset
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  task_categories:
7
  - time-series-forecasting
8
  - tabular-classification
9
  tags:
10
  - biology
 
11
  - behavioural-ecology
12
  - animals
13
  - zebras
14
  - giraffes
 
15
  - drone
 
16
  - csv
17
  - xml
 
 
 
 
18
  size_categories:
19
  - 1K<n<10K
20
  ---
21
 
22
- # Dataset Card for *kabr-tools methodology dataset*
23
 
24
- A curated collection of CSV and XML files describing time-budget data, focal observations, scan samples, and object-detection annotations for African ungulatesincluding Grevy’s zebras, plains zebras, and giraffesrecorded both from the ground and from drones. The dataset supports:
25
 
26
  - **Object detection** benchmarks (PASCAL-VOC XML).
27
- - **Behaviour classification** and **time-budget** analyses from per-individual CSV logs.
28
  - **Methodological comparisons** between focal, scan, and drone-based sampling.
29
 
30
- ## Dataset Description
 
 
 
 
 
31
 
32
- - **Curated by:** Jenna Kline et al., The Ohio State University & Imageomics Institute
33
- - **Homepage:** TO BE ADDED
 
 
34
  - **Repository:** https://github.com/Imageomics/kabr-tools
35
  - **Paper:** kabr-tools (manuscript in preparation)
36
 
37
- ---
38
 
39
  ## Dataset Structure
40
 
41
  ```
42
  kabr-methodology
43
- ├── focalvsdrone
44
  │ ├── focal_drone_df_12_01_23_female_grevy.csv
45
  │ ├── focal_drone_df_16_01_23_thick_neck_stripes.csv
46
  │ ├── focal_drone_df_16_01_23_white_female.csv
47
  │ ├── focal_drone_df_17_01_23_scar_cleaned.csv
48
  │ └── readme.txt
49
- └── scanvsfocal
50
- ├── giraffe_focal1-female.csv
51
- ├── giraffe_focal1-male.csv
52
- ├── giraffe_focal2-white_neck_male.csv
53
- ├── Scan_giraffe_group_1.csv
54
- ├── Scan_giraffe_group_2.csv
55
- ├── Focal_grevys_group_1_morning_01_11.csv
56
- ├── Focal_grevys_group_2_a_01_11.csv
57
- ├── Focal_grevys_group_2_b_01_11.csv
58
- ├── Focal_grevys_group_2_c_01_11.csv
59
- ├── Scan_grevys_group_1_morning_01_11.csv
60
- ├── Scan_grevys_group_2_a_01_11.csv
61
- ├── Scan_grevys_group_2_b_01_11.csv
62
- ├── Scan_grevys_group_2_c_01_11.csv
63
- ├── plain_Focal_group_1.csv
64
- ├── plain_Focal_group_2.csv
65
- ├── plain_Scan_group_1.csv
66
- └── plain_Scan_group_2.csv
 
 
 
67
  ```
68
 
69
- ## File-by-file purposes
70
 
71
- `kabr-methodology/focalvsdrone`
72
 
73
- ### Description:
74
- Paired focal observation and synchronized drone log for 4 individual zebras. Each CSV contains time-aligned behaviour states from ground focal sampling and drone footage, enabling direct comparison of behavioural data collected via the two methods.
75
 
76
- ### Columns:
 
 
 
 
 
 
 
 
 
 
 
 
77
 
78
  |Column | Description|
79
  |---|---|
80
  |frame | Sequential frame number in the processed dataset|
81
  |behavior |Behavior label from drone video annotation in CVAT |
82
  |video_frame | Original frame number from the source video file |
83
- | time-date| Timestamp (YYYY-MM-DD HH:MM:SS) |
84
  | focal_behavior | Behavior recorded from focal animal observation|
85
 
86
- ### Files:
87
- - focal_drone_df_12_01_23_female_grevy.csv — Paired focal observation and synchronized drone log for a female Grevy's zebra on 12-01-2023.
88
- - focal_drone_df_16_01_23_thick_neck_stripes.csv — Paired focal vs drone for ID "thick_neck_stripes" on 16-01-2023.
89
- - focal_drone_df_16_01_23_white_female.csv — Paired focal vs drone for "white_female" on 16-01-2023.
90
- - focal_drone_df_17_01_23_scar_cleaned.csv — Cleaned paired focal vs drone trace for "scar" on 17-01-2023.
91
 
92
- `kabr-methodology/scanvsfocal`
93
- ### Description:
94
- Paired scan sample and focal observation logs for groups of giraffes, Grevy’s zebras, and plains zebras. Each CSV contains time-aligned behaviour states from group-level scan sampling and individual focal sessions, enabling direct comparison of behavioural data collected via the two methods, exported from the AnimalBehaviourPro app.
95
 
96
- ### Columns:
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
97
 
98
  |Column | Description|
99
  |---|---|
100
  |Date_ymd |Date of observation (YYYY-MM-DD format)|
101
- |Time_Absolute_hms |Absolute time of day when behavior was recorded (HH:MM:SS)|
102
  |Time_Relative_hms |Time relative to start of observation session (HH:MM:SS)|
103
  |Time_Relative_s |Time relative to start of observation session in seconds|
104
- |Time_Lag_s |Time lag between current and previous event in seconds|
105
  |Duration_s |Duration of the behavioral state in seconds|
106
- |Observation_Name |Type of observation method ("AdLib" for ad libitum sampling)|
107
- |Log_File_Name |Source log file name|
108
  |Actor |Individual animal being observed (e.g., "Male w/ scar on neck")|
109
  |Behavior |Observed behavior (e.g., "Stand", "Walk")|
110
  |Receiver |Target of social behaviors (empty for non-social behaviors)|
111
  |Modifier_1 through Modifier_6 |Additional behavioral modifiers (mostly empty)|
112
- |Other_Modifiers |Additional modifiers not captured in numbered fields|
113
- |Event_Type |Whether this marks the start or stop of a behavioral state|
114
- |Observer_ID |Unique identifier for the observer (1234)|
115
  |Notes |Additional notes about the observation (empty in this dataset)|
116
  |Coding_Scheme |Behavioral coding scheme used ("Zebra")|
117
 
118
- ### Files:
119
- - giraffe_focal1-female.csv — Giraffe focal log (behaviour states over time).
120
- - giraffe_focal1-male.csv — Giraffe focal log (behaviour states over time).
121
- - giraffe_focal2-white_neck_male.csv — Giraffe focal log (behaviour states over time).
122
- - Scan_giraffe_group_1.csv — Group-level scan sample for giraffes (group 1).
123
- - Scan_giraffe_group_2.csv — Group-level scan sample for giraffes (group 2).
124
- - Focal grevys group 1 morning 01_11.csv — Group 1 focal session (AM), 01-11.
125
- - Focal grevys group 2_a 01_11.csv — Group 2 focal session (sub-bout a), 01-11.
126
- - Focal grevys group 2_b 01_11.csv — Group 2 focal session (sub-bout b), 01-11.
127
- - Focal grevys group 2_c 01_11.csv — Group 2 focal session (sub-bout c), 01-11.
128
- - Scan grevys group 1 morning 01_11.csv — Group 1 scan sample (AM), 01-11.
129
- - Scan grevys group 2_a 01_11.csv — Group 2 scan sample (sub-bout a), 01-11.
130
- - Scan grevys group 2_b 01_11.csv — Group 2 scan sample (sub-bout b), 01-11.
131
- - Scan grevys group 2_c 01_11.csv — Group 2 scan sample (sub-bout c), 01-11.
132
- - plain_Focal-group_1.csv — Plains zebra focal log (group 1).
133
- - plain_Focal-group_2.csv — Plains zebra focal log (group 2).
134
- - plain_Scan-group_1.csv — Plains zebra group scan (group 1).
135
- - plain_Scan-group_2.csv — Plains zebra group scan (group 2).
136
-
137
 
 
138
 
139
  ## Data Splits
140
 
@@ -143,50 +177,91 @@ The dataset ships as a single corpus. Create custom splits by video ID, individu
143
  ## Dataset Creation
144
 
145
  ### Curation Rationale
146
- (i) Quantify strengths/weaknesses of ground- vs drone-based behavioural sampling; (ii) provide detection and coarse behaviour benchmarks in the wild.
 
 
 
 
 
147
 
148
  ### Collection & Processing
149
- Ground focal/scan samples by trained observers; DJI drone footage (~20 m AGL); CVAT for bounding boxes; custom scripts for ethogram coding; standardized CSV/XML exports. Field scan and focal sampling collected with AnimalBehaviourPro App.
150
 
151
  ### Source Data Producers
152
- Field teams at Mpala Research Centre, Laikipia (Kenya).
153
 
 
154
 
155
- ## Bias • Risks • Limitations
156
 
157
- - Class imbalance (Grevy’s zebras dominate) may bias detectors/behaviour models.
158
- - Daytime-only footage limits generalization to low-light/night conditions.
159
- - Coarse behaviour labels omit fine motor actions.
160
  - Recommendations: Use stratified sampling or class weighting; evaluate with cross-view splits (ground vs drone).
161
 
162
  ## Licensing
163
 
164
- Compilation released under CC0 1.0 (public-domain dedication). Individual media may retain original licenses noted in metadata.
165
 
166
  ## Citation
167
 
 
 
 
 
168
  ```bibtex
169
  @misc{kline2025kabr-tools-methodology,
170
  author = {Jenna Kline and Maksim Kholiavchenko and Michelle Ramirez and Sam Stevens and
171
- Reshma Ramesh Babu and Namrata Banerji and Elizabeth Campolongo and Nina Van Tiel and Jackson Miliko and Isla Duporge and Neil Rosser and Tanya Berger-Wolf and Daniel Rubenstein},
172
- title = {kabr-tools methodology comparison dataset},
 
173
  year = {2025},
174
- url = {https://github.com/Imageomics/kabr-tools},
175
- publisher = {Imageomics Institute}
 
176
  }
177
  ```
178
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
179
 
180
  ## Acknowledgements
181
- Supported by the Imageomics Institute (NSF HDR Award #2118240). We thank the field experts at Mpala Research Centre for their support with data collection and logistics.
182
 
183
- ## Contact
184
- Questions or benchmark submissions? Open an issue on the GitHub repo: https://github.com/Imageomics/kabr-tools
 
 
 
 
185
 
186
  ## Dataset Card Authors
187
 
188
- [Jenna Kline]
 
 
 
 
189
 
190
- ## Dataset Card Contact
191
 
192
- [kline dot 377 at osu dot edu]
 
2
  license: cc0-1.0
3
  language:
4
  - en
5
+ pretty_name: "kabr-tools Methodology Dataset"
6
  task_categories:
7
  - time-series-forecasting
8
  - tabular-classification
9
  tags:
10
  - biology
11
+ - behavioral-ecology
12
  - behavioural-ecology
13
  - animals
14
  - zebras
15
  - giraffes
16
+ - grevys
17
  - drone
18
+ - UAV
19
  - csv
20
  - xml
21
+ - time-budget
22
+ - scan-samples
23
+ - focal-observation
24
+ - ungulates
25
  size_categories:
26
  - 1K<n<10K
27
  ---
28
 
29
+ # Dataset Card for *kabr-tools Methodology Dataset*
30
 
31
+ A curated collection of CSV and XML files describing time-budget data, focal observations, scan samples, and object-detection annotations for African ungulates&mdash;including Grevy’s zebras, plains zebras, and giraffes&mdash;recorded both from the ground and from drones. This dataset complements the original [KABR Mini-Scene Dataset](https://huggingface.co/datasets/imageomics/KABR), providing ground-based sampling to correspond with a subset of the published mini-scenes. Specifically designed to compare Kenyan animal behavior observation methods, this dataset supports:
32
 
33
  - **Object detection** benchmarks (PASCAL-VOC XML).
34
+ - **Behavior classification** and **time-budget** analyses from per-individual CSV logs.
35
  - **Methodological comparisons** between focal, scan, and drone-based sampling.
36
 
37
+ <!-- !!!
38
+ Add the filepath from main to the URL below, then update caption/alt text as needed
39
+ |![Gantt chart showing comparison of time-budget observations by method](https://huggingface.co/datasets/imageomics/kabr-methodology/resolve/main/<filepath>)|
40
+ |:--|
41
+ |**Figure 1.** Gantt chart showing comparison of time-budget observations by method.|
42
+ -->
43
 
44
+ ## Dataset Details
45
+
46
+ - **Curated by:** Jenna Kline, Michelle Ramirez, Sam Stevens, Reshma Ramesh Babu, Jackson Miliko, Isla Duporge, Neil Rosser, Tanya Berger-Wolf, and Daniel Rubenstein
47
+ - **Homepage:** https://imageomics.github.io/KABR
48
  - **Repository:** https://github.com/Imageomics/kabr-tools
49
  - **Paper:** kabr-tools (manuscript in preparation)
50
 
 
51
 
52
  ## Dataset Structure
53
 
54
  ```
55
  kabr-methodology
56
+ ├── focalvsdrone/
57
  │ ├── focal_drone_df_12_01_23_female_grevy.csv
58
  │ ├── focal_drone_df_16_01_23_thick_neck_stripes.csv
59
  │ ├── focal_drone_df_16_01_23_white_female.csv
60
  │ ├── focal_drone_df_17_01_23_scar_cleaned.csv
61
  │ └── readme.txt
62
+ └── scanvsfocal/
63
+ ├── giraffe/
64
+ ├── giraffe_focal1-female.csv
65
+ ├── giraffe_focal1-male.csv
66
+ ├── giraffe_focal2-white_neck_male.csv
67
+ ├── Scan_giraffe_group_1.csv
68
+ ├── Scan_giraffe_group_2.csv
69
+ ├── grevys/
70
+ ├── Focal_grevys_group_1_morning_01_11.csv
71
+ ├── Focal_grevys_group_2_a_01_11.csv
72
+ ├── Focal_grevys_group_2_b_01_11.csv
73
+ ├── Focal_grevys_group_2_c_01_11.csv
74
+ ├── Scan_grevys_group_1_morning_01_11.csv
75
+ ├── Scan_grevys_group_2_a_01_11.csv
76
+ ├── Scan_grevys_group_2_b_01_11.csv
77
+ ├── Scan_grevys_group_2_c_01_11.csv
78
+ └── plain/
79
+ ├── plain_Focal_group_1.csv
80
+ ├── plain_Focal_group_2.csv
81
+ ├── plain_Scan_group_1.csv
82
+ └── plain_Scan_group_2.csv
83
  ```
84
 
85
+ ## Data Instances and Files (by Folder/Task)
86
 
87
+ ### `focalvsdrone/`
88
 
89
+ **Description:**
 
90
 
91
+ Paired focal observation and synchronized drone log for four individual zebras. Each CSV contains time-aligned behavior states from ground focal sampling and drone footage, enabling direct comparison of behavioral data collected via the two methods. Note that the zebras in these files are given descriptive nicknames to distinguish them.
92
+
93
+ **Files (CSVs):**
94
+ - focal_drone_df_12_01_23_female_grevy.csv — Paired focal observation and synchronized drone log for a female Grevy's zebra on 12-01-2023.
95
+ - 'Zebra A': This zebra is found in video DJI_0997, miniscenes 12, 20, 33, 39, 62, and DJI_0998, miniscene 1.
96
+ - focal_drone_df_16_01_23_thick_neck_stripes.csv — Paired focal vs drone for ID "thick_neck_stripes" on 16-01-2023.
97
+ - 'Zebra B': This zebra is found in DJI_0001, mini-scene 47 and DJI_0002, mini-scene 5 and 9.
98
+ - focal_drone_df_16_01_23_white_female.csv — Paired focal vs drone for "white_female" on 16-01-2023.
99
+ - 'Zebra C': This zebra is found in DJI_0001, miniscene 8,9,11,31,44.
100
+ - focal_drone_df_17_01_23_scar_cleaned.csv — Cleaned paired focal vs drone trace for "scar" on 17-01-2023.
101
+ - 'Zebra D':This zebra is found in DJI_0008, mini-scene 40 and 41, and DJI_010 mini-scene 5.
102
+
103
+ **CSV Contents:**
104
 
105
  |Column | Description|
106
  |---|---|
107
  |frame | Sequential frame number in the processed dataset|
108
  |behavior |Behavior label from drone video annotation in CVAT |
109
  |video_frame | Original frame number from the source video file |
110
+ | time-date| Timestamp (YYYY-MM-DD HH:MM:SS in local time) |
111
  | focal_behavior | Behavior recorded from focal animal observation|
112
 
 
 
 
 
 
113
 
114
+ ### `kabr-methodology/scanvsfocal/`
115
+
116
+ **Description:**
117
 
118
+ Paired scan sample and focal observation logs for groups of giraffes, Grevy’s zebras, and plains zebras. Each CSV contains time-aligned behavior states from group-level scan sampling and individual focal sessions, enabling direct comparison of behavioral data collected via the two methods, exported from the [AnimalBehaviourPro app](https://research.kent.ac.uk/lprg/software/).
119
+
120
+ **Files (CSVs):**
121
+
122
+ Giraffes:
123
+ - giraffe_focal1-female.csv — Giraffe focal log (behavior states over time).
124
+ - giraffe_focal1-male.csv — Giraffe focal log (behavior states over time).
125
+ - giraffe_focal2-white_neck_male.csv — Giraffe focal log (behavior states over time).
126
+ - Scan_giraffe_group_1.csv — Group-level scan sample for giraffes (group 1).
127
+ - Scan_giraffe_group_2.csv — Group-level scan sample for giraffes (group 2).
128
+
129
+ Grevy's Zebras:
130
+ - Focal_grevys_group 1 morning 01_11.csv — Group 1 focal session (AM), 01-11.
131
+ - Focal_grevys_group 2_a 01_11.csv — Group 2 focal session (individual a), 01-11.
132
+ - Focal_grevys_group 2_b 01_11.csv — Group 2 focal session (individual b), 01-11.
133
+ - Focal_grevys_group 2_c 01_11.csv — Group 2 focal session (individual c), 01-11.
134
+ - Scan_grevys_group 1 morning 01_11.csv — Group 1 scan sample (AM), 01-11.
135
+ - Scan_grevys_group 2_a 01_11.csv — Group 2 scan sample (individual a), 01-11.
136
+ - Scan_grevys_group 2_b 01_11.csv — Group 2 scan sample (individual b), 01-11.
137
+ - Scan_grevys_group 2_c 01_11.csv — Group 2 scan sample (individual c), 01-11.
138
+
139
+ Plains Zebras:
140
+ - plain_Focal-group_1.csv — Plains zebra focal log (group 1).
141
+ - plain_Focal-group_2.csv — Plains zebra focal log (group 2).
142
+ - plain_Scan-group_1.csv — Plains zebra group scan (group 1).
143
+ - plain_Scan-group_2.csv — Plains zebra group scan (group 2).
144
+
145
+ **CSV Contents:**
146
+
147
+ Each of the above listed CSVs has the columns indicated below, where each row indicates a new behavior observation for a single individual. The one exception is `Scan_giraffe_group_2.csv`, which resulted from testing a different observation type; differences with that CSV are described in [Scan all behavior type](#scan-all-behavior-type), after the table.
148
 
149
  |Column | Description|
150
  |---|---|
151
  |Date_ymd |Date of observation (YYYY-MM-DD format)|
152
+ |Time_Absolute_hms |Absolute time of day (local) when behavior was recorded (HH:MM:SS)|
153
  |Time_Relative_hms |Time relative to start of observation session (HH:MM:SS)|
154
  |Time_Relative_s |Time relative to start of observation session in seconds|
155
+ |Time_Lag_s |Time lag between current and previous event in seconds (i.e., time to log behavior in app)|
156
  |Duration_s |Duration of the behavioral state in seconds|
157
+ |Observation_Name |Type of observation method ("AdLib" for ad libitum sampling, most observations)|
158
+ |Log_File_Name |Source log file name -- original name of this file|
159
  |Actor |Individual animal being observed (e.g., "Male w/ scar on neck")|
160
  |Behavior |Observed behavior (e.g., "Stand", "Walk")|
161
  |Receiver |Target of social behaviors (empty for non-social behaviors)|
162
  |Modifier_1 through Modifier_6 |Additional behavioral modifiers (mostly empty)|
163
+ |Other_Modifiers |Additional modifiers not captured in numbered fields (mostly empty)|
164
+ |Event_Type |Whether this marks the start or stop of a behavioral state ("State start" or "State stop")|
165
+ |Observer_ID |Unique identifier for the observer (e.g., "1234")|
166
  |Notes |Additional notes about the observation (empty in this dataset)|
167
  |Coding_Scheme |Behavioral coding scheme used ("Zebra")|
168
 
169
+ #### Scan all behavior type
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
170
 
171
+ `Scan_giraffe_group_2.csv` is a product of the "Scan all behavior" observation method. Instead of producing one line per behavior per subject, this observation method produced one line per observation for all individuals and their behaviors (at a particular time). As a result, the `Actor`, `Behavior`, `Receiver`, `Modifier_1`, through `Modifier_6`, `Other_Modifiers`, and `Event_Type` columns are replaced by `Group_Size`, `Activity`, `Subjects`, `Brown male_Behaviour`, `Brown male_Receiver`, `Brown male_Modifiers`, `White male_Behaviour`, `White male_Receiver`, and `White male_Modifiers`, where "White" and "Brown" males are the two giraffes being observed. The `Activity` column is filled in when both are performing the same behavior (e.g., "Walking"), in which case their individual behaviors are also both filled in. Note that in _this CSV_ behavior is indicated "Behaviour", but the others have the column "Behavior".
172
 
173
  ## Data Splits
174
 
 
177
  ## Dataset Creation
178
 
179
  ### Curation Rationale
180
+
181
+ This dataset was curated for the purposes of
182
+
183
+ (i) quantifying the strengths and weaknesses of ground- vs drone-based behavioral sampling;
184
+
185
+ (ii) providing detection and coarse behavior benchmarks in the wild.
186
 
187
  ### Collection & Processing
188
+ Ground focal/scan samples recorded by trained observers; DJI drone footage (~30 m AGL); [CVAT](https://www.cvat.ai/) for bounding boxes; custom scripts for ethogram coding; standardized CSV/XML exports. Field scan and focal sampling collected with [AnimalBehaviourPro App](https://research.kent.ac.uk/lprg/software/).
189
 
190
  ### Source Data Producers
191
+ Field teams at [Mpala Research Centre](https://mpala.org/), Laikipia (Kenya), worked with this group to collect the animal behavior observations.
192
 
193
+ ## Considerations for Using the Data
194
 
195
+ ### Bias • Risks • Limitations
196
 
197
+ - Class imbalance (Grevy’s zebras dominate) may bias detectors/behavior models.
198
+ - Daytime-only footage and observations limit potential for generalization to low-light/night conditions.
199
+ - Coarse behavior labels omit fine motor actions.
200
  - Recommendations: Use stratified sampling or class weighting; evaluate with cross-view splits (ground vs drone).
201
 
202
  ## Licensing
203
 
204
+ This compilation is dedicated to the public domain (released under [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)) for the benefit of scientific pursuits.
205
 
206
  ## Citation
207
 
208
+ If you make use of this dataset for your research, please cite both it and the original KABR mini-scene as below:
209
+
210
+ **Datasets:**
211
+
212
  ```bibtex
213
  @misc{kline2025kabr-tools-methodology,
214
  author = {Jenna Kline and Maksim Kholiavchenko and Michelle Ramirez and Sam Stevens and
215
+ Reshma Ramesh Babu and Namrata Banerji and Elizabeth Campolongo and Nina Van Tiel and
216
+ Jackson Miliko and Isla Duporge and Neil Rosser and Tanya Berger-Wolf and Daniel Rubenstein},
217
+ title = {kabr-tools Methodology Dataset},
218
  year = {2025},
219
+ url = {https://huggingface.co/datasets/imageomics/kabr-methodology},
220
+ publisher = {Hugging Face},
221
+ doi = {}
222
  }
223
  ```
224
 
225
+ ```
226
+ @misc{KABR_Data,
227
+ author = {Kholiavchenko, Maksim and Kline, Jenna and Ramirez, Michelle and Stevens, Sam and Sheets, Alec and Babu, Reshma and Banerji, Namrata and Campolongo, Elizabeth and Thompson, Matthew and Van Tiel, Nina and Miliko, Jackson and Bessa, Eduardo and Duporge, Isla and Berger-Wolf, Tanya and Rubenstein, Daniel and Stewart, Charles},
228
+ title = {KABR: In-Situ Dataset for Kenyan Animal Behavior Recognition from Drone Videos},
229
+ year = {2023},
230
+ url = {https://huggingface.co/datasets/imageomics/KABR},
231
+ doi = {10.57967/hf/1010},
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+ publisher = {Hugging Face}
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+ }
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+ ```
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+
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+ **KABR mini-scene Paper:**
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+
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+ ```
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+ @inproceedings{kholiavchenko2024kabr,
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+ title={KABR: In-Situ Dataset for Kenyan Animal Behavior Recognition from Drone Videos},
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+ author={Kholiavchenko, Maksim and Kline, Jenna and Ramirez, Michelle and Stevens, Sam and Sheets, Alec and Babu, Reshma and Banerji, Namrata and Campolongo, Elizabeth and Thompson, Matthew and Van Tiel, Nina and Miliko, Jackson and Bessa, Eduardo and Duporge, Isla and Berger-Wolf, Tanya and Rubenstein, Daniel and Stewart, Charles},
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+ booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
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+ pages={31-40},
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+ year={2024}
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+ }
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+ ```
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  ## Acknowledgements
 
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+ This work was supported by the [Imageomics Institute](https://imageomics.org), which is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under [Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118240) (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Additional support was also provided by the [AI Institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE)](https://icicle.osu.edu/), which is funded by the US National Science Foundation under [Award #2112606](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2112606). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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+
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+ We thank the field experts at [Mpala Research Centre](https://mpala.org/) for their support with data collection and logistics.
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+
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+ The data was gathered at the [Mpala Research Centre](https://mpala.org/) in Kenya, in accordance with Research License No. NACOSTI/P/22/18214. The data collection protocol adhered strictly to the guidelines set forth by the Institutional Animal Care and Use Committee under permission No. IACUC 1835F.
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+
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  ## Dataset Card Authors
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+ Jenna Kline and Elizabeth Campolongo
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+
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+ ## Contact
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+
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+ **Questions or benchmark submissions?** Open an issue on the GitHub repo: https://github.com/Imageomics/kabr-tools
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+ ### Dataset Card Contact
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+ kline dot 377 at osu dot edu