Example Data Status Reports | Exploring data-status results in R

Purpose of the Ecological Report | Example Ecological Reports | Customising the Ecological Report | Viewing and changing metadata | Adjusting the focus group | Selecting years and adjusting filters | Selecting which sections are included

Introduction | What camtrapReport is designed to do | Input data | Key outputs | Data Status Check Report | Ecological Report | Modular workflow | Conclusion

How to add new modules? | 1. List existing modules | 2. Create a new module YAML file | 3. Add the new module | 4. Update the current camReport object | 5. Test the new module | 6. Generate the full report

Overview | Data | 1. Point the package at the bioacoustics flavor | 2. Deployments (from the deployment notebook) | 3. Media (derived from the observation notebook's file names) | 4. Observations (from the observation notebook) | 5. Metadata, relations, write, validate | 6. Inspecting the acoustic requirements

概要 | データ | 1. bioacoustics フレーバーを指定 | 2. deployments（設置野帳から） | 3. media（観察野帳のファイル名から生成） | 4. observations（観察野帳から） | 5. メタデータ・リレーション・書き出し・検証 | 6. 音響スキーマの要件確認

概要 | データ | 1. バージョンの選択とスキーマの確認（任意） | スキーマ内の外部（URL）参照 | 2. 3 つの中核テーブルの作成 | デプロイメントの作成 | メディアの作成 | observationの作成 | 3. データパッケージの組み立て | R6 オブジェクトの作成（バージョン指定） | テーブルの登録（スキーマ検証付き） | テーブル間リレーションの検査 | 4. メタデータ | どのメタデータが必須かを profile で確認する | 必須メタデータ | 貢献者（Contributors） | プロジェクト（Project） | 空間・時間（Spatial and temporal） | 分類（Taxonomic） | 作成日時（Created） | 任意メタデータ | ライセンス（Licenses） | 関連識別子（Related identifiers） | プロパティ・ソース・参考文献 | カスタムリソース | 5. データパッケージの出力 | 6. Frictionless による検証 | 適合の事前チェック（Python を呼ぶ前に） | Frictionless の実行 | 7. 任意のスプレッドシートを直接変換する | 8. 別のスキーマフレーバー（例: bioacoustics） | カメラトラップの列を音響フレーバーへ対応づける

Overview | Data | 1. Choose a version and inspect its schema (optional) | External (URL) references in a schema | 2. Build the three core tables | Create deployments | Create media | Create observations | 3. Assemble the data package | Create the R6 object (with a version) | Import the tables (now schema-validated) | Check relations between tables | 4. Metadata | Check which metadata the profile requires | Required metadata | Contributors | Project | Spatial and temporal | Taxonomic | Created | Optional metadata | Licenses | Related identifiers | Properties, sources and references | Custom resources | 5. Output the data package | 6. Validate the written package with Frictionless | Conformance pre-checks (before calling Python) | Run Frictionless | 7. Converting an arbitrary spreadsheet directly | 8. Other schema flavors (e.g. bioacoustics) | Mapping camera-trap columns to the acoustic flavor

Data | Create deployment | Create media | Create observation | Create datapackages

Data | Create deployment | Create media | Create observation | Create datapackages

Setup | Station names | Session and camera IDs

Setup | Species record table | Temporal independence | Exclude some species | Station names | Remove duplicates | Use filter predicates | Other arguments needed?

Setup | Get taxonomic information | Create maps | Basic usage | Number of species | Number of observations | Number of individuals | RAI | RAI (individuals) | Effort | Clustering and hovering | Visualize deployments without detected animals | Use a color palette | Use a specific icon and color for zero values | Modify circle size | Use absolute scale | Use filter predicates

Setup | One argument - one value predicates | pred(), pred_not() | pred_gt(), pred_gte(), pred_lt(), pred_lte() | One argument - no value predicates | One argument - multiple value predicates | multiple predicates: pred_and() and pred_or() | How to use filter predicates | One predicate | Multiple predicates

Introduction | Camera trap station information | Organising raw camera trap images

Methods for species and individual identification - General | Species identification | Individual identification

Tabulating species and individual records: the recordTable functions | Camera Operation | Input for subsequent analyses (single-season) | Input for subsequent analyses (multi-season)

Overview | Species presence maps | Visualising species activity data | Survey summary report

5. Multi-species occupancy models | Overview | Warning | Limitations | Parameter naming convention | Quick example | Data preparation | Fit model in JAGS | Models in Nimble | More complete example | Prepare data | JAGS Model | Nimble Model | Missing values in detection histories | Further possibilies | Fixed effects | Priors | Data augmentation | Categorical covariates | Random effects other than species | Nested random effects | Species and site random effect on detection | Quadratic covariate effects | WAIC | Species richness | Spatial predictions | Percentage of area occupied (by species) | bayesplot | Model diagnostics | Royle-Nichols model | Single-species occupancy models