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| 1 | +datasets: |
| 2 | +- neurodata_type_def: TimestampVectorData |
| 3 | + neurodata_type_inc: VectorData |
| 4 | + dtype: float |
| 5 | + dims: |
| 6 | + - num_times |
| 7 | + shape: |
| 8 | + - null |
| 9 | + doc: A 1-dimensional VectorData that stores timestamps in seconds. |
| 10 | + attributes: |
| 11 | + - name: unit |
| 12 | + dtype: text |
| 13 | + value: seconds |
| 14 | + doc: The unit of measurement for the timestamps, fixed to 'seconds'. |
| 15 | + - name: resolution |
| 16 | + dtype: float |
| 17 | + doc: The smallest possible difference between two timestamps. Usually 1 divided |
| 18 | + by the sampling rate for timestamps of the data acquisition system. |
| 19 | + required: false |
| 20 | + |
| 21 | +- neurodata_type_def: DurationVectorData |
| 22 | + neurodata_type_inc: VectorData |
| 23 | + dtype: float |
| 24 | + dims: |
| 25 | + - num_events |
| 26 | + shape: |
| 27 | + - null |
| 28 | + doc: A 1-dimensional VectorData that stores durations in seconds. |
| 29 | + attributes: |
| 30 | + - name: unit |
| 31 | + dtype: text |
| 32 | + value: seconds |
| 33 | + doc: The unit of measurement for the durations, fixed to 'seconds'. |
| 34 | + - name: resolution |
| 35 | + dtype: float |
| 36 | + doc: The smallest possible difference between two timestamps. Usually 1 divided |
| 37 | + by the sampling rate for timestamps of the data acquisition system. |
| 38 | + required: false |
| 39 | + |
| 40 | +- neurodata_type_def: CategoricalVectorData |
| 41 | + neurodata_type_inc: VectorData |
| 42 | + dims: |
| 43 | + - num_events |
| 44 | + shape: |
| 45 | + - null |
| 46 | + doc: A 1-dimensional VectorData that stores categorical data of any type. This is |
| 47 | + an experimental type. |
| 48 | + attributes: |
| 49 | + - name: meanings |
| 50 | + dtype: |
| 51 | + target_type: MeaningsTable |
| 52 | + reftype: object |
| 53 | + doc: The MeaningsTable object that provides the meanings of the values in this |
| 54 | + CategoricalVectorData object. |
| 55 | + - name: filter_values |
| 56 | + dtype: text |
| 57 | + dims: |
| 58 | + - num_events |
| 59 | + shape: |
| 60 | + - null |
| 61 | + doc: Optional dataset containing possible values in the parent data that represent |
| 62 | + missing or invalid values that should be filtered out during analysis. Currently, |
| 63 | + only string values are allowed. For example, the filter values may contain the |
| 64 | + values "undefined" or "None" to signal that those values in the data are missing |
| 65 | + or invalid. |
| 66 | + required: false |
| 67 | + |
| 68 | +groups: |
| 69 | +- neurodata_type_def: MeaningsTable |
| 70 | + neurodata_type_inc: DynamicTable |
| 71 | + doc: A table to store information about the meanings of categorical data. Intended |
| 72 | + to be used as a lookup table for the meanings of values in a CategoricalVectorData |
| 73 | + object. All possible values of the parent CategoricalVectorData object should |
| 74 | + be present in the 'value' column of this table, even if the value is not observed |
| 75 | + in the data. Additional columns may be added to store additional metadata about |
| 76 | + each value. |
| 77 | + datasets: |
| 78 | + - name: value |
| 79 | + neurodata_type_inc: VectorData |
| 80 | + doc: The value of the parent CategoricalVectorData object. |
| 81 | + - name: meaning |
| 82 | + neurodata_type_inc: VectorData |
| 83 | + dtype: text |
| 84 | + doc: The meaning of the value in the parent CategoricalVectorData object. |
| 85 | + |
| 86 | +- neurodata_type_def: EventsTable |
| 87 | + neurodata_type_inc: DynamicTable |
| 88 | + doc: A column-based table to store information about events (event instances), one |
| 89 | + event per row. Additional columns may be added to store metadata about each event, |
| 90 | + such as the duration of the event. |
| 91 | + attributes: |
| 92 | + - name: description |
| 93 | + dtype: text |
| 94 | + doc: A description of the events stored in the table, including information about |
| 95 | + how the event times were computed, especially if the times are the result of |
| 96 | + processing or filtering raw data. For example, if the experimenter is encoding |
| 97 | + different types of events using a strobed or N-bit encoding, then the description |
| 98 | + should describe which channels were used and how the event time is computed, |
| 99 | + e.g., as the rise time of the first bit. |
| 100 | + datasets: |
| 101 | + - name: timestamp |
| 102 | + neurodata_type_inc: TimestampVectorData |
| 103 | + doc: Column containing the time that each event occurred, in seconds, from the |
| 104 | + session start time. |
| 105 | + - name: duration |
| 106 | + neurodata_type_inc: DurationVectorData |
| 107 | + doc: Optional column containing the duration of each event, in seconds. A value |
| 108 | + of NaN can be used for events without a duration or with a duration that is |
| 109 | + not yet specified. |
| 110 | + quantity: '?' |
| 111 | + groups: |
| 112 | + - neurodata_type_inc: MeaningsTable |
| 113 | + doc: Lookup tables for the meanings of the values in any CategoricalVectorData |
| 114 | + columns. The name of the table should be the name of the corresponding CategoricalVectorData |
| 115 | + column followed by "_meanings". |
| 116 | + quantity: '*' |
| 117 | + |
| 118 | +- neurodata_type_def: AnnotationsTable |
| 119 | + neurodata_type_inc: EventsTable |
| 120 | + doc: A column-based table to store user annotations about events, one event per row. |
| 121 | + If annotations come from a fixed set of values (aka controlled vocabulary or enumeration), |
| 122 | + make the "annotation" column a CategoricalVectorData and add a MeaningsTable named |
| 123 | + "annotation_meanings" to specify the meanings of the fixed set of values in the |
| 124 | + "annotation" column. |
| 125 | + datasets: |
| 126 | + - name: annotation |
| 127 | + neurodata_type_inc: VectorData |
| 128 | + doc: Column containing the freeform text annotation. |
| 129 | + dtype: text |
| 130 | + dims: |
| 131 | + - num_times |
| 132 | + shape: |
| 133 | + - null |
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