Reviewed information on data card

README.md

@@ -136,14 +136,15 @@ English
   * Approximately 48K images of 419 species for species classification tasks.
   * There are about 35K training, 7.6K test, and 5K validation images.
 
-* **Species-level Trait Identification:** `identification_<split>.csv`
-  * Approximately 53K images of 682 species for trait identification based on _species-level trait expectation_ (i.e., presence/absence of traits based on expectation for the species from information provided by [Phenoscape]() and [FishBase](https://www.fishbase.se/), not by looking at the images).
+* **Trait Identification:** `identification_<split>.csv`
+  * Approximately 53K images of 682 species for trait identification based on _species-level trait labels_ (i.e., presence/absence of traits based on trait labels for the species from information provided by [Phenoscape]() and [FishBase](https://www.fishbase.se/)).
   * About 38K training, 8K `test_insp` (species in training set), 1.6K `test_lvsp` (species not in training), and 5.3K validation images.
   * Train, test, and validation splits are generated based on traits, so there are 628 species in train, 471 species in `test_insp`, 51 species in `test_lvsp`, and 452 in the validation set (4 species only in val). 
 
-* **Image-level Trait Identification:** `segmentation_data.csv`
+* **Trait Segmentation:** `segmentation_data.csv`
   * Pixel-level annotations of 9 different traits for 2,427 fish images.
-  * These are ground-truth _image-level trait IDs_ manually annotated.
+  * About x training, y test and z validation images for the segmentation task
+  * These are also used as manually annotated test set for Trait Identification.
 
 
 * **Image Information**
@@ -186,7 +187,7 @@ For each task (or subset), the split is indicated by the CSV name (e.g., `classi
 <!-- Motivation for the creation of this dataset. For instance, what you intended to study and why that required curation of a new dataset (or if it's newly collected data and why the data was collected (intended use)), etc. -->
 
 Fishes are integral to both ecological systems and economic sectors, and studying fish traits is crucial for understanding biodiversity patterns and macro-evolution trends.
-Currently available fish datasets tend to focus on species classification, and when annotations are available, they tend to be for the entire specimen, allowing for segmenation of background, but not trait discovery.
+Currently available fish datasets tend to focus on species classification. They lack finer-grained labels for traits. When segmentation annotations are available in existing datasets, they tend to be for the entire specimen, allowing for segmenation of background, but not trait segmentation. 
 The ultimate goal of Fish-Vista is to provide a clean, carefully curated, high-resolution dataset that can serve as a foundation for accelerating biological discoveries using advances in AI.
 
 
@@ -204,8 +205,9 @@ Images and taxonomic labels were aggregated by [Fish-AIR](https://fishair.org/)
 - [Field Museum of Natural History (Zoology, FMNH) Fish Collection](https://fmipt.fieldmuseum.org/ipt/resource?r=fmnh_fishes)
 - [The Ohio State University Fish Division, Museum of Biological Diversity (OSUM), Occurrence dataset](https://doi.org/10.15468/subsl8)
 
+[Phenoscape](https://kb.phenoscape.org/about/phenoscape/kb) and [FishBase](https://www.fishbase.se/search.php) were used to provide the information on traits at the species level.
 
-[Phenoscape](https://kb.phenoscape.org/about/phenoscape/kb) and [FishBase](https://www.fishbase.se/search.php) were used to standardize the species labels and provided the information on expected traits at the species level.
+[Open Tree Taxonomy](https://tree.opentreeoflife.org/) was used to standardize the species names provided by Fish-AIR.
 
 
 #### Data Collection and Processing
@@ -218,7 +220,7 @@ This is what _you_ did to it following collection from the original source; it w
 |**Figure 4.** An overview of the data processing and filtering pipeline used to obtain Fish-Vista. |
 
 We carefully curated a set of
-107K images sourced from various museum collections through [Fish-AIR](https://fishair.org/), including [Great Lakes Invasives Network (GLIN)](https://greatlakesinvasives.org/portal/index.php), [iDigBio](https://www.idigbio.org/), and [Morphbank](https://www.morphbank.net/).
+60K images sourced from various museum collections through [Fish-AIR](https://fishair.org/), including [Great Lakes Invasives Network (GLIN)](https://greatlakesinvasives.org/portal/index.php), [iDigBio](https://www.idigbio.org/), and [Morphbank](https://www.morphbank.net/).
 Our pipeline incorporates rigorous stages such as duplicate removal, metadata-driven filtering, cropping, background removal using the [Segment Anything Model (SAM)](https://github.com/facebookresearch/segment-anything), and a final
 manual filtering phase. Fish-Vista supports several biologically meaningful tasks such as species
 classification, trait identification, and trait segmentation. 
@@ -233,9 +235,11 @@ Ex: We standardized the taxonomic labels provided by the various data sources to
 
 #### Annotation process
 <!-- This section describes the annotation process such as annotation tools used, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
+[Phenoscape](https://kb.phenoscape.org/about/phenoscape/kb) and [FishBase](https://www.fishbase.se/search.php) were used to provide the information on species-level traits (the species-trait matrix).
 
-[Phenoscape](https://kb.phenoscape.org/about/phenoscape/kb) and [FishBase](https://www.fishbase.se/search.php) were used to standardize the species labels provided by Fish-AIR. They also provided the information on expected species-level traits.
-Image-level traits were manually annotated as described below.
+[Open Tree Taxonomy](https://tree.opentreeoflife.org/) was used to standardize the species names provided by Fish-AIR.
+
+Image-level trait segmentations were manually annotated as described below.
 
 The annotation process for the segmentation subset was led by Wasila Dahdul. She provided guidance and oversight to a team of three people from [NEON](https://www.neonscience.org/about), who used [CVAT](https://zenodo.org/records/7863887) to label nine external traits in the images. These traits correspond to the following terms for anatomical structures in the UBERON anatomy ontology:
 1. Eye, [UBERON_0000019](http://purl.obolibrary.org/obo/UBERON_0000019)