cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15679654
title: 7-Tage-Inzidenz der COVID-19-Fälle in Deutschland

description: Im Datensatz '7-Tage-Inzidenz von COVID-19 in Deutschland' werden die aktuellen 7-Tage-Inzidenzen der nach den Vorgaben des Infektionsschutzgesetzes - IfSG - an das RKI übermittelten COVID-19-Fälle veröffentlicht. Datengrundlage zur Berechnung der 7-Tage-Inzidenzen sind die an das RKI übermittelten COVID-19-Fälle. Eine detaillierte Dokumentation zur Erhebung der Daten zum Infektionsgeschehen ist im Datensatz 'SARS-CoV-2-Infektionen in Deutschland' enthalten. Die für die Berechnung der 7-Tage-Inzidenz notwendigen Bevölkerungsdaten bezieht das RKI durch das Statistisches Bundesamt (Destatis), Referat F24 | Bevölkerungsfortschreibung, Ausländer- und Integrationsstatistiken.
DOI: 10.15468/dl.86xzqk
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eriocaulon africanum Hochst.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((20.7576 -24.3688,20.529 -24.0715,20.509 -24.0715,20.509 -28.5786,25.5395 -28.5786,25.5395 -24.0715,25.1306 -24.0715,24.9333 -24.4571,23.8962 -24.2467,22.8065 -24.295,22.3719 -24.7207,22.2323 -25.547,22.0387 -25.7188,21.7833 -25.7784,21.5388 -25.7113,21.3553 -25.5415,20.7576 -24.3688))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050289-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.5234968
title: RELLISUR: A Real Low-Light Image Super-Resolution Dataset

description: The RELLISUR dataset contains real low-light low-resolution images paired with normal-light high-resolution reference image counterparts. This dataset aims to fill the gap between low-light image enhancement and low-resolution image enhancement (Super-Resolution (SR)) which is currently only being addressed separately in the literature, even though the visibility of real-world images is often limited by both low-light and low-resolution.  The dataset contains 12750 paired images of different resolutions and degrees of low-light illumination, to facilitate learning of deep-learning based models that can perform a direct mapping from degraded images with low visibility to high-quality detail rich images of high resolution. The associated paper can be found here: https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/7ef605fc8dba5425d6965fbd4c8fbe1f-Paper-round2.pdf
DOI: 10.5281/zenodo.5234969
title: RELLISUR: A Real Low-Light Image Super-Resolution Dataset

description: The RELLISUR dataset contains real low-light low-resolution images paired with normal-light high-resolution reference image counterparts. This dataset aims to fill the gap between low-light image enhancement and low-resolution image enhancement (Super-Resolution (SR)) which is currently only being addressed separately in the literature, even though the visibility of real-world images is often limited by both low-light and low-resolution.  The dataset contains 12750 paired images of different resolutions and degrees of low-light illumination, to facilitate learning of deep-learning based models that can perform a direct mapping from degraded images with low visibility to high-quality detail rich images of high resolution. The associated paper can be found here: https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/7ef605fc8dba5425d6965fbd4c8fbe1f-Paper-round2.pdf
DOI: 10.25345/c51c1tt8j
title: MassIVE MSV000098226 - HumanGlycoproteomicsInitiative_Study2
title: oncluding at the end of 2021, the first Human Glycoproteomics Initiative community-driven study (Kawahara et al., Nature Methods, 2021) generated many insightful conclusions and, importantly, many interesting questions about data analysis throughout the glycoproteomics community. These open questions serve as the foundation for the second HGI-led community-driven study. This second study will focus on teams of software developers only, with the goal to identify strengths and weaknesses of the very latest glycoproteomics software for glycopeptide identification and quantitation.
DOI: 10.5281/zenodo.15679341
title: The Threat of Artificial Intelligence to the Programmer Job Market in Indonesia Dataset

description: This dataset originates from a comprehensive survey of over 300 respondents within the Indonesian IT sector. The dataset is a valuable resource for research focusing on various dimensions of programmer perspective . It supports studies related to AI, which examines the perception and branding of AI Identity which assesses programmer satisfaction levels with AI Influence, which evaluates the overall quality and performance of programmer. Additionally, the dataset is instrumental in exploring Threat to job security, a critical factor influencing programmer confidence in their skill and future. Finally, the dataset underpins studies on AI Identity threat, which investigates the factors contributing to programmer intensity and perspective in the job market. This dataset provides a robust foundation for academic inquiries into key aspects of AI and programmer behavior. Its meticulous collection and cleaning processes ensure that it is a reliable and valuable resource for researchers aiming to understand and enhance the AI threat in Indonesia and beyond.
DOI: 10.5281/zenodo.15679342
title: The Threat of Artificial Intelligence to the Programmer Job Market in Indonesia Dataset

description: This dataset originates from a comprehensive survey of over 300 respondents within the Indonesian IT sector. The dataset is a valuable resource for research focusing on various dimensions of programmer perspective . It supports studies related to AI, which examines the perception and branding of AI Identity which assesses programmer satisfaction levels with AI Influence, which evaluates the overall quality and performance of programmer. Additionally, the dataset is instrumental in exploring Threat to job security, a critical factor influencing programmer confidence in their skill and future. Finally, the dataset underpins studies on AI Identity threat, which investigates the factors contributing to programmer intensity and perspective in the job market. This dataset provides a robust foundation for academic inquiries into key aspects of AI and programmer behavior. Its meticulous collection and cleaning processes ensure that it is a reliable and valuable resource for researchers aiming to understand and enhance the AI threat in Indonesia and beyond.
DOI: 10.15468/dl.bg99ha
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eriocaulon africanum Hochst.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((20.529 -24.0715,20.509 -24.0455,20.509 -24.0715,20.529 -24.0715))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050290-250525065834625/datasets/export for details.
DOI: 10.15468/dl.33vjub
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eriocaulon africanum Hochst.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((25.5395 -23.2724,25.1306 -24.0715,25.5395 -24.0715,25.5395 -23.2724))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050291-250525065834625/datasets/export for details.
DOI: 10.15468/dl.58eryh
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eriocaulon africanum Hochst.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((20.2855 -34.7533,20.509 -34.5676,20.509 -32.9007,17.7723 -32.9007,17.8156 -33.0786,18.3471 -33.7176,18.3748 -33.8287,18.2181 -34.0492,18.3197 -34.3408,18.4452 -34.429,18.5435 -34.4213,18.5872 -34.3667,18.5363 -34.167,18.7345 -34.1508,18.7216 -34.3637,18.7808 -34.4521,19.0289 -34.4206,19.0831 -34.4835,19.2655 -34.5017,19.2032 -34.6157,19.2394 -34.6926,19.375 -34.6963,19.6091 -34.8524,19.8251 -34.8396,19.9798 -34.9064,20.1081 -34.8731,20.1517 -34.7847,20.2855 -34.7533))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050293-250525065834625/datasets/export for details.
DOI: 10.15468/dl.nmt7m7
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eriocaulon africanum Hochst.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((35.628 -23.023,35.6007 -22.9702,35.6007 -23.165,35.628 -23.023))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050294-250525065834625/datasets/export for details.
DOI: 10.15468/dl.v4kznq
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eriocaulon africanum Hochst.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((35.6306 -23.844,35.6007 -23.8125,35.6007 -24.0157,35.6306 -23.844))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050295-250525065834625/datasets/export for details.
DOI: 10.6084/m9.figshare.29290430
title: <b>Processed Temperature Data for China (1990–2022): NOAA-Derived Daily Records with Spatial Attributes​</b>

description: This dataset contains daily temperature records (minimum, maximum, and average) for China from 1990 to 2022, processed from NOAA’s raw data. The data was spatially filtered to include only points within China’s administrative boundaries and converted from Fahrenheit to Celsius. The dataset is formatted for easy integration with geospatial analyses and climate studies.​<b>​Key Features​</b>​​<b>​Data Source​</b>​: Derived from NOAA’s global temperature records, filtered for China using spatial joins with administrative boundaries.​<b>​Variables Included​</b>​:<code>TEMP_C</code>: Daily average temperature (°C).<code>MAX_C</code>/<code>MIN_C</code>: Daily maximum/minimum temperatures (°C).<code>LATITUDE</code>/<code>LONGITUDE</code>: Geographic coordinates (WGS84).Additional metadata (e.g., station IDs, dates).​<b>​Processing Workflow​</b>​:​<b>​Spatial Filtering​</b>​: Data was clipped to China’s boundaries using <code>geopandas</code> spatial joins (<code>EPSG:4326</code> CRS).​<b>​Unit Conversion​</b>​: Temperatures converted from Fahrenheit to Celsius.​<b>​Format​</b>​: Saved as <code>.pkl</code> (Pickle) files for efficient storage and Python compatibility.​<b>​Code Availability​</b>​: The Python script used for processing is included (see "Code" section), with dependencies listed below.​<b>​Intended Use​</b>​Climate trend analysis, regional temperature modeling, or validation of satellite-derived products.Integration with GIS platforms (e.g., QGIS, ArcGIS) or Python-based workflows.​<b>​Technical Details​</b>​​<b>​Software​</b>​: Processed using Python 3.x with <code>pandas</code>, <code>geopandas</code>, and <code>pyarrow</code>.​<b>​Coordinate System​</b>​: WGS84 (EPSG:4326).​<b>​Temporal Coverage​</b>​: January 1, 1990 – December 31, 2022.
DOI: 10.6084/m9.figshare.29290430.v1
title: <b>Processed Temperature Data for China (1990–2022): NOAA-Derived Daily Records with Spatial Attributes​</b>

description: This dataset contains daily temperature records (minimum, maximum, and average) for China from 1990 to 2022, processed from NOAA’s raw data. The data was spatially filtered to include only points within China’s administrative boundaries and converted from Fahrenheit to Celsius. The dataset is formatted for easy integration with geospatial analyses and climate studies.​<b>​Key Features​</b>​​<b>​Data Source​</b>​: Derived from NOAA’s global temperature records, filtered for China using spatial joins with administrative boundaries.​<b>​Variables Included​</b>​:<code>TEMP_C</code>: Daily average temperature (°C).<code>MAX_C</code>/<code>MIN_C</code>: Daily maximum/minimum temperatures (°C).<code>LATITUDE</code>/<code>LONGITUDE</code>: Geographic coordinates (WGS84).Additional metadata (e.g., station IDs, dates).​<b>​Processing Workflow​</b>​:​<b>​Spatial Filtering​</b>​: Data was clipped to China’s boundaries using <code>geopandas</code> spatial joins (<code>EPSG:4326</code> CRS).​<b>​Unit Conversion​</b>​: Temperatures converted from Fahrenheit to Celsius.​<b>​Format​</b>​: Saved as <code>.pkl</code> (Pickle) files for efficient storage and Python compatibility.​<b>​Code Availability​</b>​: The Python script used for processing is included (see "Code" section), with dependencies listed below.​<b>​Intended Use​</b>​Climate trend analysis, regional temperature modeling, or validation of satellite-derived products.Integration with GIS platforms (e.g., QGIS, ArcGIS) or Python-based workflows.​<b>​Technical Details​</b>​​<b>​Software​</b>​: Processed using Python 3.x with <code>pandas</code>, <code>geopandas</code>, and <code>pyarrow</code>.​<b>​Coordinate System​</b>​: WGS84 (EPSG:4326).​<b>​Temporal Coverage​</b>​: January 1, 1990 – December 31, 2022.
DOI: 10.15468/dl.fk8pv6
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Adenostemma caffrum DC.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((30.0251 -31.3959,30.5701 -30.7329,30.5701 -28.5786,25.5395 -28.5786,25.5395 -32.9007,28.2048 -32.9007,28.5753 -32.6569,29.4902 -31.7517,30.0251 -31.3959))"
 ]
}

 The dataset includes 7 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050296-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15672142
title: Global vegetation phenology data

description: We used the Normalized Difference Vegetation Index (NDVI) from the Terra MODIS MOD13C1 Version 6 product , which provides 16-day composite vegetation data at a 0.05° spatial resolution. The temporal coverage spans from 2001 to 2022. A Savitzky–Golay (S-G) filter was used to smooth noise and capture the overall seasonal curve. Then, an asymmetric Gaussian (A-G) function was used to fit the smoothed NDVI to get  more accurately model phenological transitions. Spring and autumn phenological parameters (SOS and EOS)—were extracted using a dynamic threshold method (threshold = 0.5).
DOI: 10.5281/zenodo.15672141
title: Global vegetation phenology data

description: We used the Normalized Difference Vegetation Index (NDVI) from the Terra MODIS MOD13C1 Version 6 product , which provides 16-day composite vegetation data at a 0.05° spatial resolution. The temporal coverage spans from 2001 to 2022. A Savitzky–Golay (S-G) filter was used to smooth noise and capture the overall seasonal curve. Then, an asymmetric Gaussian (A-G) function was used to fit the smoothed NDVI to get  more accurately model phenological transitions. Spring and autumn phenological parameters (SOS and EOS)—were extracted using a dynamic threshold method (threshold = 0.5).
DOI: 10.15468/dl.j8ma7e
title: Occurrence Download

description: A dataset containing 33 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Adenostemma caffrum DC.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((30.5701 -24.0715,25.5395 -24.0715,25.5395 -28.5786,30.5701 -28.5786,30.5701 -24.0715))"
 ]
}

 The dataset includes 33 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050297-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xpbgad
title: Occurrence Download

description: A dataset containing 3334769 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Leucanthemum vulgare Lam., Dactylis glomerata L., Cirsium arvense (L.) Scop., Gnaphalium sylvaticum Sm., Trifolium repens L.)",
 "Geometry POLYGON ((-180 45.53015, 180 45.53015, 180 83.62313, -180 83.62313, -180 45.53015))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "CoordinateUncertaintyInMeters is less than or equal to 50000"
 ]
}

 The dataset includes 3334769 records from 2566 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050292-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/p0d-72m-w57
title: Carex dioica L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0d-x66-rhp
title: Salix pentandra L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0d-y87-12s
title: Iberis amara L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0e-0g6-gn2
title: Salix myrsinites L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0e-2ks-k76
title: Carex elongata L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0e-eg0-k0s
title: Arabidopsis × suecica (Fr.) Norrl.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0e-frp-qzh
title: Lathyrus vernus (L.) Bernh.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0e-wns-fjc
title: Potentilla L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0e-wrv-60g
title: Thlaspi arvense L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0f-2qj-69s
title: Carex aquatilis Wahlenb.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0f-58r-57d
title: Trifolium medium L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0f-6bg-m7d
title: Populus tremula f. gigas

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0f-7xq-pjz
title: Betula nana L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0d-vhr-mrg
title: Dianthus superbus L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0f-2ne-dhx
title: Luzula multiflora (Ehrh.) Lej.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0f-ceh-9gq
title: Stellaria palustris (Murray ex Ehrh.) Hoffm.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0f-rng-n74
title: Cryptogramma crispa (L.) R. Br. ex Hook.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0f-rnz-4nh
title: Thalictrum simplex L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0f-rrf-gdz
title: Eriophorum latifolium Hoppe

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0f-w6b-lb0
title: Isoetes lacustris L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0g-0xr-jaa
title: Parnassia palustris L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0g-2tw-dhv
title: Plantago lanceolata L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0g-3ec-p2p
title: Descurainia sophia (L.) Webb ex Prantl

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0g-ak4-q3q
title: Carex rhynchophysa Fisch., C.A.Mey. & Avé-Lall.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0g-lfq-kap
title: Corylus avellana L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0f-mv5-y19
title: Carex rotundata Wahlenb.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0h-8ll-p95
title: Rorippa × armoracioides (Tausch) Fuss

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0g-3jd-hjf
title: Erysimum cheiranthoides L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0h-rv1-6q3
title: Alnus incana (L.) Moench

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0h-t0t-nb3
title: Saxifraga aizoides L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0h-vwz-xsj
title: Lathyrus palustris L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0j-477-l47
title: Carex dioica L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0j-5wk-3vx
title: Stellaria nemorum L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0j-74h-h6w
title: Anthoxanthum nitens (Weber) Y.Schouten & Veldkamp

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0j-eax-z5r
title: Carex flava L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0j-las-0qa
title: Calypso bulbosa (L.) Oakes

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/p0j-n3a-4ff
title: Cardamine amara L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.