Updated README and add column aliases

- Updated README with details about the Canadian Invertebrate 1.5M dataset.
- Added column aliases to ensure compatibility with the previous metadata structure.

Author: vivianp17

README.rst

@@ -12,12 +12,13 @@
 BIOSCAN Datasets for PyTorch
 ============================
 
-In this package, we provide PyTorch/torchvision style dataset classes to load the `BIOSCAN-1M <BIOSCAN-1M paper_>`_ and `BIOSCAN-5M <BIOSCAN-5M paper_>`_ datasets.
+In this package, we provide PyTorch/torchvision style dataset classes to load the `BIOSCAN-1M <BIOSCAN-1M paper_>`_, `BIOSCAN-5M <BIOSCAN-5M paper_>`_, and `Canadian Invertebrates 1.5M <CanadianInvertebrates paper_>`_ datasets.
 
 BIOSCAN-1M and 5M are large multimodal datasets for insect biodiversity monitoring, containing over 1 million and 5 million specimens, respectively.
 The datasets are comprised of RGB microscopy images, `DNA barcodes <what-is-DNA-barcoding_>`_, and fine-grained, hierarchical taxonomic labels.
+The Canadian Invertebrates 1.5M dataset provides DNA barcodes for over 1.5 million Invertebrates that are collected across 23 ecozones in Canada. It is a major reference library for biodiversity research and consists of DNA Barcodes collected from platforms like BOLD, GenBank and GBIF.
 Every sample has both an image and a DNA barcode, but the taxonomic labels are incomplete and only extend all the way to the species level for around 9% of the specimens.
-For more details about the datasets, please see the `BIOSCAN-1M paper`_ and `BIOSCAN-5M paper`_, respectively.
+For more details about the datasets, please see the `BIOSCAN-1M paper`_ , `BIOSCAN-5M paper`_, and  `Canadian Invertebrates 1.5M <CanadianInvertebrates paper_>`_ respectively.
 
 Documentation about this package, including the full API details, is available online at readthedocs_.
 
@@ -69,6 +70,18 @@ To load the BIOSCAN-1M dataset:
         # Do something with the image, dna_barcode, and label
         pass
 
+To load the Canadian Invertebrates 1.5M dataset:
+
+.. code-block:: python
+
+    from bioscan_dataset import CanadianInvertebrates
+
+    dataset = CanadianInvertebrate(root="~/Datasets/bioscan/")
+
+    for dna_barcode, label in dataset:
+        # Do something with the dna_barcode, and label
+        pass
+
 Note that although BIOSCAN-5M is a superset of BIOSCAN-1M, the repeated data samples are not identical between the two due to data cleaning and processing differences.
 For details, please see Appendix Q of the `BIOSCAN-5M paper`_.
 Additionally, note that the splits are incompatible between the two datasets.
@@ -341,7 +354,7 @@ The transform indicates the name of a taxonomic rank and its value for every ran
 Other resources
 ---------------
 
-- Read the `BIOSCAN-1M paper`_ and `BIOSCAN-5M paper`_.
+- Read the `BIOSCAN-1M paper`_ , `BIOSCAN-5M paper`_ and `Canadian Invertebrates 1.5M <CanadianInvertebrates paper_>`_.
 - The dataset can be explored through a web interface using our `BIOSCAN Browser`_.
 - Read more about the `International Barcode of Life (iBOL) <https://ibol.org/>`__ and `BIOSCAN <https://ibol.org/bioscan/>`__ initiatives.
 - See the code for the `cropping tool <https://github.com/bioscan-ml/BIOSCAN-5M/tree/main/BIOSCAN_crop_resize>`__ that was applied to the images to create the cropped image package.
@@ -352,7 +365,7 @@ Other resources
 Citation
 --------
 
-If you make use of the BIOSCAN-1M or BIOSCAN-5M datasets in your research, please cite the following papers as appropriate.
+If you make use of the BIOSCAN-1M, BIOSCAN-5M or Canadian Invertebrates 1.5M datasets in your research, please cite the following papers as appropriate.
 
 `BIOSCAN-5M <BIOSCAN-5M paper_>`_:
 
@@ -394,6 +407,25 @@ If you make use of the BIOSCAN-1M or BIOSCAN-5M datasets in your research, pleas
         url={https://proceedings.neurips.cc/paper_files/paper/2023/file/87dbbdc3a685a97ad28489a1d57c45c1-Paper-Datasets_and_Benchmarks.pdf},
     }
 
+`CanadianInvertebrates <CanadianInvertebrates paper_>`_:
+
+.. code-block:: bibtex
+
+    @article{dewaard2019reference,
+        title={A reference library for Canadian invertebrates with 1.5 million barcodes, voucher specimens, and DNA samples},
+        author={DeWaard, J. R. and Ratnasingham, S. and Zakharov, E. V. and Borisenko, A. V.
+            and Steinke, D. and Telfer, A. C. and Perez, K. H. J. and Sones, J. E.
+            and Young, M. R. and Levesque-Beaudin, V. and others
+        },
+        journal={Scientific data},
+        volume={6},
+        number={1},
+        pages={308},
+        year={2019},
+        publisher={Nature Publishing Group UK London}
+        url={https://www.nature.com/articles/s41597-019-0320-2.pdf},
+    }
+
 If you use the CLIBD partitioning scheme for BIOSCAN-1M, please also consider citing the `CLIBD paper`_.
 
 .. code-block:: bibtex
@@ -411,6 +443,7 @@ If you use the CLIBD partitioning scheme for BIOSCAN-1M, please also consider ci
 .. _BIOSCAN Browser: https://bioscan-browser.netlify.app/
 .. _BIOSCAN-1M paper: https://papers.nips.cc/paper_files/paper/2023/hash/87dbbdc3a685a97ad28489a1d57c45c1-Abstract-Datasets_and_Benchmarks.html
 .. _BIOSCAN-5M paper: https://arxiv.org/abs/2406.12723
+.. _CanadianInvertebrates paper: https://www.nature.com/articles/s41597-019-0320-2
 .. _BS1M-class: https://bioscan-dataset.readthedocs.io/en/stable/api.html#bioscan_dataset.BIOSCAN1M
 .. _BS5M-class: https://bioscan-dataset.readthedocs.io/en/stable/api.html#bioscan_dataset.BIOSCAN5M
 .. _CLIBD paper: https://arxiv.org/abs/2405.17537

bioscan_dataset/CanadianInvertebrates.py

@@ -50,6 +50,7 @@
     "split",
 ]
 
+COLUMN_ALIASES = {"bin_uri": "dna_bin", "nucleotides": "dna_barcode"}
 VALID_SPLITS = ["pretrain", "train", "val", "test", "key_unseen", "val_unseen", "test_unseen", "other_heldout"]
 SPLIT_ALIASES = {"validation": "val"}
 VALID_METASPLITS = ["all", "seen", "unseen"]
@@ -385,7 +386,7 @@ def __init__(
             self.target_type = [target_type]
         else:
             self.target_type = list(target_type)
-        self.target_type = ["dna_bin" if t == "uri" else t for t in self.target_type]
+        self.target_type = [COLUMN_ALIASES.get(t, t) for t in self.target_type]
 
         if not self.target_type and self.target_transform is not None:
             raise RuntimeError("target_transform is specified but target_type is empty")