cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48668/mu3mfc/1j8mut
title: China_1960_1960_3_1960_Issue-10_1960_Issue-10_47_page_0_extracted_2_medium_conf0.426_772.png

description: :unav
DOI: 10.48668/mu3mfc/avfqlm
title: China_1960_1960_1_1960_Issue-6_1960_Issue-6_11_page_0_extracted_1_premium_conf0.993_978.png

description: :unav
DOI: 10.48668/mu3mfc/9voyhp
title: China_1960_1960_2_1960_Issue-2_1960_Issue-2_3_page_0_extracted_3_medium_conf0.361_1103.png

description: :unav
DOI: 10.48668/mu3mfc/qftv5o
title: China_1960_1960_2_1960_Issue-4_1960_Issue-4_4_page_0_extracted_1_premium_conf0.980_721.png

description: :unav
DOI: 10.48668/mu3mfc/0dxizt
title: China_1960_1960_3_1960_Issue-11_1960_Issue-11_18_page_0_extracted_1_excellent_conf0.861_786.png

description: :unav
DOI: 10.48668/mu3mfc/pjlaxz
title: China_1960_1960_3_1960_Issue-10_1960_Issue-10_15_page_0_extracted_2_good_conf0.591_738.png

description: :unav
DOI: 10.48668/mu3mfc/eaoawe
title: China_1960_1960_1_1960_Issue-7_1960_Issue-7_4_page_0_extracted_1_excellent_conf0.892_424.png

description: :unav
DOI: 10.48668/mu3mfc/kd2eum
title: China_1960_1960_1_1960_Issue-8_1960_Issue-8_33_page_0_extracted_1_excellent_conf0.872_443.png

description: :unav
DOI: 10.48668/mu3mfc/guvgrf
title: China_1960_1960_3_1960_Issue-11_1960_Issue-11_12_page_0_extracted_2_medium_conf0.409_781.png

description: :unav
DOI: 10.48668/mu3mfc/mjbbou
title: China_1960_1960_1_1960_Issue-9_1960_Issue-9_38_page_0_extracted_1_excellent_conf0.857_498.png

description: :unav
DOI: 10.48668/mu3mfc/q5oh7g
title: China_1960_1960_3_1960_Issue-11_1960_Issue-11_8_page_0_extracted_1_premium_conf0.968_831.png

description: :unav
DOI: 10.48668/mu3mfc/cnw3zx
title: China_1960_1960_3_1960_Issue-10_1960_Issue-10_24_page_0_extracted_1_premium_conf1.000_750.png

description: :unav
DOI: 10.48668/mu3mfc/c15avi
title: China_1960_1960_3_1960_Issue-11_1960_Issue-11_41_page_0_extracted_2_high_conf0.719_812.png

description: :unav
DOI: 10.5281/zenodo.18212341
title: Cytometry data to reproduce the study Functional characterization of specialized immune cells in a cnidarian reveals an ancestral antiviral program

description: This repository contains flow cytometry (FCS) and imaging flow cytometry (ImageStream) datasets generated from Nematostella vectensis larval-derived cells to characterize RLRb-expressing immune cells and their functional responses to immune stimulation.

The dataset includes experiments designed to profile and functionally characterize RLRb-positive cells, including intracellular RLRb immunostaining, RLRb::mCherry reporter expression, poly(I:C)-induced immune activation, phagocytosis of pHrodo-labeled Staphylococcus aureus, pHrodo-labeled Escherichia coli, and DQ-ovalbumin, apoptosis (Apotracker green), proliferation (Edu incorporation), and detailed morphological analysis using imaging flow cytometry. Together, these data capture multiple aspects of RLRb-mediated antiviral and phagocytic immune responses at the single-cell level.

Data were acquired using an Amnis ImageStreamX Mk II imaging flow cytometer and BD FACSAria III. Fluorescent labels and probes include RLRb::mCherry, DRAQ5, pHrodo-conjugated Staphylococcus aureus and  Escherichia coli, DQ-ovalbumin, Calcein Violet-AM, Zombie NIR viability dye, Apotracker-green, and SYTOX Blue. The repository contains raw FCS files, raw ImageStream (.rif) files, and associated analysis outputs (e.g., compensation matrices, IDEAS exports, and reports), allowing full reanalysis and reuse of the data. Gating strategies and experimental design are described in the associated manuscript.

These datasets support the analyses presented in the preprint:

Kozlovski et al., “Functional characterization of specialized immune cells in a cnidarian reveals an ancestral antiviral program.”bioRxiv (2025). https://doi.org/10.1101/2025.01.24.634691

This Zenodo record provides the raw and processed single-cell cytometry data underlying the figures and conclusions of that study, enabling independent validation, re-analysis, and comparative immunology research in early-branching metazoans.
DOI: 10.5281/zenodo.18514252
title: Synchronized Multi-Device Environmental Audio Dataset for Spatial and Propagation Analysis

description: Description

Despite the availability of numerous environmental audio datasets, none fully satisfy the requirements for synchronized multi-device recording across varying distances. This dataset addresses that gap by providing audio captured simultaneously from four recording devices, all synchronized at the start of each recording session using a shared synchronization flag.

Recordings were conducted across six representative acoustic scenes: jungle, café, machine/factory, street, and multi-audio-source environments. These scenes capture a wide range of real-world acoustic characteristics, from highly dynamic and layered soundscapes to more stationary and monotonic environments. Hybrid scenarios, such as overlapping speech with machinery (MultiTalk) and urban noise combined with conversations (MultiStreet), represent complex environments where multiple sound sources compete.

The dataset is intended for research in environmental sound analysis, acoustic propagation, spatial audio modeling, and multi-device signal alignment.

Data Acquisition and Synchronization





Four audio recording devices were used simultaneously.




All devices begin recording at the same time.




A synchronization flag is embedded at the start of each recording to ensure precise temporal alignment across devices.




This flag enables accurate cross-device comparison and post-processing synchronization.



Recording Environments (Scenes)





Jungle – Dense, layered natural soundscape




Café – Human activity with background noise




Machine/Factory – Predominantly mechanical and monotonic sounds




Street – Traffic, voices, and urban ambience




MultiTalk – Overlapping speech with machinery




MultiStreet – Urban noise with concurrent conversations



These environments collectively represent common real-world acoustic conditions and varying frequency and tonal characteristics.

Audio Specifications





Sampling Rate: 44,100 Hz




Bit Depth: 16-bit




Channels: Mono 




Format: WAV




Recording Equipment: Standardized across all devices and scenes



Post-Processing





Amplitude standardization applied to normalize volume levels across recordings




Segmentation into fixed-length clips of:





1 second




3 seconds




5 seconds





Dataset Size





Total Samples: 3,600 audio clips




Number of Devices: 4




Number of Scenes: 6



Intended Use

This dataset is suitable for:





Environmental sound classification




Multi-device audio synchronization research




Acoustic propagation and spatial analysis




Source separation and overlapping sound studies




Robust audio modeling in real-world environments



Limitations





The dataset focuses on controlled synchronization and may not represent unsynchronized consumer-grade recordings.




Scene diversity is broad but limited to six environment types.



Keywords

Environmental Audio, Multi-Device Recording, Synchronized Audio, Acoustic Propagation, Spatial Audio, Soundscape Analysis, Audio Dataset
DOI: 10.48668/mu3mfc/dmp3nk
title: China_1960_1960_2_1960_Issue-12_1960_Issue-12_6_page_0_extracted_1_premium_conf0.987_558.png

description: :unav
DOI: 10.48668/mu3mfc/ytfdj5
title: China_1960_1960_2_1960_Issue-1_1960_Issue-1_43_page_0_extracted_1_high_conf0.728_1086.png

description: :unav
DOI: 10.48668/mu3mfc/rygica
title: China_1960_1960_2_1960_Issue-1_1960_Issue-1_7_page_0_extracted_2_good_conf0.667_599.png

description: :unav
DOI: 10.48668/mu3mfc/d7qnsx
title: China_1960_1960_1_1960_Issue-9_1960_Issue-9_45_page_0_extracted_3_high_conf0.799_1058.png

description: :unav
DOI: 10.48668/mu3mfc/75l5n0
title: China_1960_1960_1_1960_Issue-9_1960_Issue-9_8_page_0_extracted_1_high_conf0.771_1063.png

description: :unav
DOI: 10.17630/db41e230-72eb-4bfe-b48a-6408886b2096
title: Modular Enantioselective Photocatalysts from Privileged Pybox Scaffolds (dataset)

description: The open data folder includes two sub-folders: Computations, Photophysics.
The Computations folder includes the .log and .com (or .gjf) files for the investigated compounds.
The Photophysics folder includes the raw data (the txt file saved from the instrument) and the Origin files used for plotting spectra of TRPL decays and ΔEST measurements
DOI: 10.48668/mu3mfc/r19awf
title: China_1960_1960_2_1960_Issue-2_1960_Issue-2_19_page_0_extracted_1_excellent_conf0.933_612.png

description: :unav
DOI: 10.48668/mu3mfc/govghv
title: China_1960_1960_3_1960_Issue-10_1960_Issue-10_36_page_0_extracted_1_premium_conf0.999_1150.png

description: :unav
DOI: 10.48668/mu3mfc/1kcyz1
title: China_1960_1960_2_1960_Issue-1_1960_Issue-1_8_page_0_extracted_1_excellent_conf0.934_600.png

description: :unav
DOI: 10.48668/mu3mfc/3eljg9
title: China_1960_1960_2_1960_Issue-1_1960_Issue-1_29_page_0_extracted_1_high_conf0.804_1081.png

description: :unav
DOI: 10.48668/mu3mfc/onwphf
title: China_1960_1960_2_1960_Issue-12_1960_Issue-12_37_page_0_extracted_1_high_conf0.785_533.png

description: :unav
DOI: 10.48668/mu3mfc/p4xnux
title: China_1960_1960_2_1960_Issue-12_1960_Issue-12_42_page_0_extracted_4_medium_conf0.353_1067.png

description: :unav
DOI: 10.48668/mu3mfc/nbwazi
title: China_1960_1960_2_1960_Issue-2_1960_Issue-2_39_page_0_extracted_1_high_conf0.730_1102.png

description: :unav
DOI: 10.48668/mu3mfc/yeiuwf
title: China_1960_1960_1_1960_Issue-9_1960_Issue-9_29_page_0_extracted_1_premium_conf0.951_485.png

description: :unav
DOI: 10.48668/mu3mfc/onggqh
title: China_1960_1960_2_1960_Issue-1_1960_Issue-1_19_page_0_extracted_1_high_conf0.794_573.png

description: :unav
DOI: 10.48668/mu3mfc/tgoxza
title: China_1960_1960_2_1960_Issue-12_1960_Issue-12_8_page_0_extracted_2_high_conf0.707_565.png

description: :unav
DOI: 10.5281/zenodo.18517684
title: Task2Model

description: We release EarlyBird and Task2Model, two datasets derived from scientific publications that document how NLP models are mentioned and linked to scientific tasks in the research literature.

EarlyBird (EarlyBird Model Mention Dataset) is an intermediate dataset that captures all detected mentions of NLP models within individual scientific papers. Each entry is accompanied by rich metadata, including paper identifiers, section indicators, and model mention counts, enabling fine-grained analyses of model usage, reporting practices, and mention patterns across the scientific literature.

Building on this resource, we further release Task2Model, a large-scale dataset of scientific problem descriptions or tasks linked to NLP models.Task2Model emphasizes hierarchical and family-level evaluation, systematic quality audits, and explicit leakage-prevention measures, providing a reliable framework for benchmarking task-to-model mapping approaches. The dataset comprises more than 99,000 scientific problem descriptions associated with over 500 unique NLP models.

Each row in the Task2Model dataset contains a cleaned and filtered text snippet with the model name masked, capturing a scientific task or problem context, along with associated metadata.

Columns:



Scientific_problem_descriptions: Cleaned and filtered text snippets in which the model name is masked, capturing a scientific task or problem context.

Full_ID: Canonical full identifier of the NLP model associated with the task (e.g., openai-community/gpt2).

Short_ID: Normalized short identifier of the model, used as a collapsed label for analysis (e.g., gpt2).

paper_id: Identifier linking the task description to the original scientific publication.

family_label: Higher-level model family corresponding to the model (e.g., BERT, GPT, LLaMA), enabling hierarchical and family-level evaluation.


Purpose and Use Cases:

These datasets are designed to support research on how NLP models are mentioned, reported, and applied in scientific literature. They can be used for:





Analyzing trends and common usage patterns of NLP models in research.




Studying reporting practices and model adoption across scientific domains.




Grouping models by families for aggregated and hierarchical analyses.




Training and evaluating systems for model mention recognition and task-to-model mapping.




Linking model mentions to their scientific task contexts for downstream empirical studies.



Overall, these resources provide a foundation for systematic analysis of NLP model usage in scientific publications and for the development and evaluation of task-to-model recommendation and benchmarking methods.The full codebase for dataset construction, preprocessing, and split generation is publicly available athttps://github.com/khushbakhtahmed/Task2Model.
DOI: 10.5281/zenodo.18517685
title: Task2Model

description: We release EarlyBird and Task2Model, two datasets derived from scientific publications that document how NLP models are mentioned and linked to scientific tasks in the research literature.

EarlyBird (EarlyBird Model Mention Dataset) is an intermediate dataset that captures all detected mentions of NLP models within individual scientific papers. Each entry is accompanied by rich metadata, including paper identifiers, section indicators, and model mention counts, enabling fine-grained analyses of model usage, reporting practices, and mention patterns across the scientific literature.

Building on this resource, we further release Task2Model, a large-scale dataset of scientific problem descriptions or tasks linked to NLP models.Task2Model emphasizes hierarchical and family-level evaluation, systematic quality audits, and explicit leakage-prevention measures, providing a reliable framework for benchmarking task-to-model mapping approaches. The dataset comprises more than 99,000 scientific problem descriptions associated with over 500 unique NLP models.

Each row in the Task2Model dataset contains a cleaned and filtered text snippet with the model name masked, capturing a scientific task or problem context, along with associated metadata.

Columns:



Scientific_problem_descriptions: Cleaned and filtered text snippets in which the model name is masked, capturing a scientific task or problem context.

Full_ID: Canonical full identifier of the NLP model associated with the task (e.g., openai-community/gpt2).

Short_ID: Normalized short identifier of the model, used as a collapsed label for analysis (e.g., gpt2).

paper_id: Identifier linking the task description to the original scientific publication.

family_label: Higher-level model family corresponding to the model (e.g., BERT, GPT, LLaMA), enabling hierarchical and family-level evaluation.


Purpose and Use Cases:

These datasets are designed to support research on how NLP models are mentioned, reported, and applied in scientific literature. They can be used for:





Analyzing trends and common usage patterns of NLP models in research.




Studying reporting practices and model adoption across scientific domains.




Grouping models by families for aggregated and hierarchical analyses.




Training and evaluating systems for model mention recognition and task-to-model mapping.




Linking model mentions to their scientific task contexts for downstream empirical studies.



Overall, these resources provide a foundation for systematic analysis of NLP model usage in scientific publications and for the development and evaluation of task-to-model recommendation and benchmarking methods.The full codebase for dataset construction, preprocessing, and split generation is publicly available athttps://github.com/khushbakhtahmed/Task2Model.
DOI: 10.48668/mu3mfc/wluclr
title: China_1960_1960_3_1960_Issue-10_1960_Issue-10_18_page_0_extracted_1_high_conf0.747_742.png

description: :unav
DOI: 10.48668/mu3mfc/9zxqy6
title: China_1960_1960_2_1960_Issue-12_1960_Issue-12_8_page_0_extracted_3_medium_conf0.359_1077.png

description: :unav
DOI: 10.48668/mu3mfc/qxp4ms
title: China_1960_1960_1_1960_Issue-9_1960_Issue-9_47_page_0_extracted_2_high_conf0.756_520.png

description: :unav
DOI: 10.48668/mu3mfc/sfiev2
title: China_1960_1960_3_1960_Issue-11_1960_Issue-11_9_page_0_extracted_3_low_conf0.259_835.png

description: :unav
DOI: 10.48668/mu3mfc/lduvmn
title: China_1960_1960_3_1960_Issue-11_1960_Issue-11_31_page_0_extracted_1_premium_conf0.955_799.png

description: :unav
DOI: 10.48668/mu3mfc/7y5zlo
title: China_1960_1960_3_1960_Issue-11_1960_Issue-11_1_page_0_extracted_1_medium_conf0.441_1162.png

description: :unav
DOI: 10.48668/mu3mfc/cxbro4
title: China_1960_1960_1_1960_Issue-6_1960_Issue-6_34_page_0_extracted_1_premium_conf0.984_375.png

description: :unav
DOI: 10.48668/mu3mfc/d6ba9z
title: China_1960_1960_1_1960_Issue-8_1960_Issue-8_8_page_0_extracted_1_excellent_conf0.896_458.png

description: :unav
DOI: 10.48668/mu3mfc/mtxjyu
title: China_1960_1960_2_1960_Issue-2_1960_Issue-2_26_page_0_extracted_2_good_conf0.601_620.png

description: :unav
DOI: 10.15468/dl.65943t
title: Occurrence Download

description: A dataset containing 258 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cryptochloa strictiflora",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 258 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018437-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48668/mu3mfc/iut4ey
title: China_1960_1960_2_1960_Issue-4_1960_Issue-4_47_page_0_extracted_1_good_conf0.529_714.png

description: :unav
DOI: 10.48668/mu3mfc/1bnrjw
title: China_1960_1960_1_1960_Issue-7_1960_Issue-7_30_page_0_extracted_2_high_conf0.726_1012.png

description: :unav
DOI: 10.48668/mu3mfc/cfagoi
title: China_1960_1960_2_1960_Issue-1_1960_Issue-1_42_page_0_extracted_1_premium_conf0.999_582.png

description: :unav
DOI: 10.48668/mu3mfc/9rgda8
title: China_1960_1960_3_1960_Issue-11_1960_Issue-11_4_page_0_extracted_1_good_conf0.505_1179.png

description: :unav
DOI: 10.48668/mu3mfc/gavsox
title: China_1960_1960_2_1960_Issue-2_1960_Issue-2_23_page_0_extracted_1_low_conf0.273_616.png

description: :unav
DOI: 10.48668/mu3mfc/4d7fq4
title: China_1960_1960_1_1960_Issue-5_1960_Issue-5_44_page_0_extracted_1_good_conf0.560_975.png

description: :unav
DOI: 10.48668/mu3mfc/0lwbdc
title: China_1960_1960_2_1960_Issue-12_1960_Issue-12_38_page_0_extracted_1_medium_conf0.444_1064.png

description: :unav
DOI: 10.48668/mu3mfc/dgf54k
title: China_1960_1960_2_1960_Issue-2_1960_Issue-2_14_page_0_extracted_2_medium_conf0.325_606.png

description: :unav
DOI: 10.48668/mu3mfc/abmvn9
title: China_1960_1960_1_1960_Issue-9_1960_Issue-9_13_page_0_extracted_2_low_conf0.292_1040.png

description: :unav
DOI: 10.48668/mu3mfc/rjcubn
title: China_1960_1960_3_1960_Issue-10_1960_Issue-10_42_page_0_extracted_1_excellent_conf0.875_766.png

description: :unav
DOI: 10.48668/mu3mfc/zhqlfb
title: China_1960_1960_1_1960_Issue-9_1960_Issue-9_31_page_0_extracted_1_good_conf0.623_1049.png

description: :unav
DOI: 10.48668/mu3mfc/bt7a0c
title: China_1960_1960_1_1960_Issue-6_1960_Issue-6_13_page_0_extracted_1_premium_conf0.975_348.png

description: :unav
DOI: 10.48668/mu3mfc/sgacdm
title: China_1960_1960_3_1960_Issue-11_1960_Issue-11_31_page_0_extracted_1_high_conf0.756_801.png

description: :unav
DOI: 10.48668/mu3mfc/acuhi2
title: China_1960_1960_2_1960_Issue-2_1960_Issue-2_47_page_0_extracted_1_premium_conf0.998_1106.png

description: :unav
DOI: 10.48668/mu3mfc/hjhnnb
title: China_1960_1960_3_1960_Issue-12_1960_Issue-12_31_page_0_extracted_2_high_conf0.719_859.png

description: :unav
DOI: 10.48668/mu3mfc/bj85b8
title: China_1960_1960_1_1960_Issue-7_1960_Issue-7_30_page_0_extracted_4_medium_conf0.495_410.png

description: :unav
DOI: 10.48668/mu3mfc/jcom93
title: China_1960_1960_2_1960_Issue-1_1960_Issue-1_20_page_0_extracted_1_premium_conf0.999_574.png

description: :unav
DOI: 10.48668/mu3mfc/haoahb
title: China_1960_1960_2_1960_Issue-2_1960_Issue-2_3_page_0_extracted_2_good_conf0.629_630.png

description: :unav
DOI: 10.48668/mu3mfc/gu9viz
title: China_1960_1960_1_1960_Issue-9_1960_Issue-9_46_page_0_extracted_1_high_conf0.734_1059.png

description: :unav
DOI: 10.48668/mu3mfc/ek36zw
title: China_1960_1960_2_1960_Issue-3_1960_Issue-3_32_page_0_extracted_1_excellent_conf0.916_667.png

description: :unav
DOI: 10.48668/mu3mfc/a3t2x9
title: China_1960_1960_2_1960_Issue-4_1960_Issue-4_43_page_0_extracted_2_high_conf0.822_707.png

description: :unav
DOI: 10.48668/mu3mfc/4tulbp
title: China_1960_1960_1_1960_Issue-6_1960_Issue-6_30_page_0_extracted_2_high_conf0.704_367.png

description: :unav
DOI: 10.48668/mu3mfc/b5slpe
title: China_1960_1960_1_1960_Issue-5_1960_Issue-5_8_page_0_extracted_1_high_conf0.794_344.png

description: :unav
DOI: 10.48668/mu3mfc/z3jjey
title: China_1960_1960_2_1960_Issue-2_1960_Issue-2_12_page_0_extracted_1_premium_conf0.960_604.png

description: :unav
DOI: 10.48668/mu3mfc/w7m1u2
title: China_1960_1960_2_1960_Issue-12_1960_Issue-12_44_page_0_extracted_1_excellent_conf0.922_551.png

description: :unav
DOI: 10.48668/mu3mfc/augiun
title: China_1960_1960_1_1960_Issue-9_1960_Issue-9_31_page_0_extracted_1_excellent_conf0.885_488.png

description: :unav
DOI: 10.48668/mu3mfc/p2bkh9
title: China_1960_1960_3_1960_Issue-12_1960_Issue-12_14_page_0_extracted_2_good_conf0.656_846.png

description: :unav
DOI: 10.48668/mu3mfc/8galez
title: China_1960_1960_2_1960_Issue-12_1960_Issue-12_8_page_0_extracted_4_medium_conf0.306_566.png

description: :unav
DOI: 10.48668/mu3mfc/qdoynp
title: China_1960_1960_3_1960_Issue-11_1960_Issue-11_23_page_0_extracted_1_medium_conf0.371_795.png

description: :unav
DOI: 10.48668/mu3mfc/hgpbyt
title: China_1960_1960_3_1960_Issue-11_1960_Issue-11_10_page_0_extracted_2_good_conf0.665_1156.png

description: :unav
DOI: 10.48668/mu3mfc/o73df7
title: China_1960_1960_1_1960_Issue-7_1960_Issue-7_1_page_0_extracted_2_low_conf0.292_1007.png

description: :unav
DOI: 10.48668/mu3mfc/u1ksbn
title: China_1960_1960_3_1960_Issue-11_1960_Issue-11_8_page_0_extracted_1_premium_conf0.956_830.png

description: :unav
DOI: 10.48668/mu3mfc/vshahn
title: China_1960_1960_2_1960_Issue-4_1960_Issue-4_25_page_0_extracted_1_premium_conf0.998_698.png

description: :unav
DOI: 10.48668/mu3mfc/sqzhwb
title: China_1960_1960_1_1960_Issue-7_1960_Issue-7_45_page_0_extracted_3_medium_conf0.480_415.png

description: :unav
DOI: 10.48668/mu3mfc/fmdye5
title: China_1960_1960_2_1960_Issue-1_1960_Issue-1_43_page_0_extracted_1_premium_conf0.953_584.png

description: :unav
DOI: 10.48668/mu3mfc/rpzd2i
title: China_1960_1960_1_1960_Issue-8_1960_Issue-8_4_page_0_extracted_1_premium_conf0.975_454.png

description: :unav
DOI: 10.48668/mu3mfc/uhi4a2
title: China_1960_1960_1_1960_Issue-6_1960_Issue-6_25_page_0_extracted_1_excellent_conf0.948_984.png

description: :unav
DOI: 10.48668/mu3mfc/ts0v2r
title: China_1960_1960_2_1960_Issue-4_1960_Issue-4_42_page_0_extracted_1_high_conf0.831_705.png

description: :unav
DOI: 10.48668/mu3mfc/zeeiqi
title: China_1960_1960_1_1960_Issue-9_1960_Issue-9_32_page_0_extracted_3_good_conf0.526_492.png

description: :unav
DOI: 10.15468/dl.ykuc2y
title: Occurrence Download

description: A dataset containing 324 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ctenium concinnum",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 324 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018438-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48668/mu3mfc/itryon
title: China_1960_1960_2_1960_Issue-4_1960_Issue-4_6_page_0_extracted_2_excellent_conf0.913_724.png

description: :unav
DOI: 10.48668/mu3mfc/g1c1zx
title: China_1960_1960_1_1960_Issue-5_1960_Issue-5_34_page_0_extracted_1_premium_conf0.970_972.png

description: :unav
DOI: 10.48668/mu3mfc/5idkr1
title: China_1960_1960_2_1960_Issue-1_1960_Issue-1_26_page_0_extracted_1_premium_conf0.961_1080.png

description: :unav
DOI: 10.48668/mu3mfc/x5nab2
title: China_1960_1960_1_1960_Issue-9_1960_Issue-9_24_page_0_extracted_1_premium_conf0.989_1045.png

description: :unav