cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7038175
title: Results from national testing programs on the occurrence of chemical contaminants in food and feed - Luxembourg

description: In the framework of Articles 23 and 33 of Regulation (EC) No 178/2002 EFSA has received from the European Commission a mandate (M-2010-0374) to collect all available data on the occurrence of chemical contaminants in food and feed. These data are used in EFSA’s scientific opinions and reports on contaminants in food and feed. The presence of unauthorised substances or chemical contaminants in food may pose a risk factor for public health and can cause a negative impact on the quality of food. Commission Recommendations and Regulations on occurrence monitoring are in place for several contaminants of interest, some of which can be found here below: Commission Regulation (EU) 625/2017, on the application of food and feed law Commission Regulation (EC) 1881/2006, on maximum levels for certain contaminants in foodstuffs Commission Regulation (EC) 2017/2158 establishing mitigation measures and benchmark levels for the reduction of the presence of acrylamide in food These datasets contain the results of sampling that was designed according to national testing programs for a variety of contaminants in food and feed, as reported under the Chemical Monitoring Data Collection 2021, split by sampling year (data element ‘sampY’). More details are available in the ‘Call for continuous collection of chemical contaminants occurrence data in food and feed | EFSA (europa.eu)’. REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: OCC-CHEMMON2020 – Ministry of Health OCC-CHEMMON2021 – Ministry of Health
description: LU; CSV; data.collection@efsa.europa.eu
DOI: 10.5281/zenodo.7808858
title: Results from national testing programs on the occurrence of chemical contaminants in food and feed - Luxembourg

description: In the framework of Articles 23 and 33 of Regulation (EC) No 178/2002 EFSA has received from the European Commission a mandate (M-2010-0374) to collect all available data on the occurrence of chemical contaminants in food and feed. These data are used in EFSA’s scientific opinions and reports on contaminants in food and feed. The presence of unauthorised substances or chemical contaminants in food may pose a risk factor for public health and can cause a negative impact on the quality of food. Commission Recommendations and Regulations on occurrence monitoring are in place for several contaminants of interest, some of which can be found here below: Commission Regulation (EU) 625/2017, on the application of food and feed law Commission Regulation (EC) 1881/2006, on maximum levels for certain contaminants in foodstuffs Commission Regulation (EC) 2017/2158 establishing mitigation measures and benchmark levels for the reduction of the presence of acrylamide in food These datasets contain the results of sampling that was designed according to national testing programs for a variety of contaminants in food and feed, as reported under the Chemical Monitoring Data Collection 2022, 2021, and 2020, split by sampling year (data element ‘sampY’). More details are available in the ‘Call for continuous collection of chemical contaminants occurrence data in food and feed | EFSA (europa.eu)’. REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: OCC-CHEMMON2020 – Ministry of Health OCC-CHEMMON2021 – Ministry of Health
description: LU; CSV; data.collection@efsa.europa.eu
DOI: 10.3535/1vv-nwa-pe1
title: Heteroschismoides Ludbrook, 1960

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/z32-cv4-h85
title: Foraminifera d'Orbigny, 1826

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/6zr-r77-xze
title: Nummulitidae Blainville, 1827

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/k5b-kcn-08l
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/sv0-5jx-5ya
title: †Lepidocyclina Gümbel, 1870

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/e1l-d81-vyj
title: Heteroschismoides Ludbrook, 1960

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/fsh-1x7-nn1
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/9kg-yvq-brg
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/ssx-tgv-zg2
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/1a9-rm4-24a
title: †Lepidocyclina martini (Schlumberger, 1900)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/btx-tnj-2ng
title: Achatinella Swainson, 1828

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/td7-937-qcy
title: Eponididae Hofker, 1951

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/c3b-1dd-hkh
title: Nummulitidae Blainville, 1827

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/lcs-q05-evg
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/led-a99-zp0
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/hlw-8sp-edh
title: Ammodiscus Reuss, 1862

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/vj9-pt9-rka
title: Nummulitidae Blainville, 1827

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/2lj-cbm-9vw
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/3mt-2k1-4zf
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/hnp-rs2-txr
title: Pteroplatus gracilis Buquet, 1840

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/0sy-k99-4wk
title: Nummulitidae Blainville, 1827

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/mxv-ev8-eyg
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/507-6x4-xfv
title: Ostracoda Latreille, 1802

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/6nb-tqk-gr6
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/6gp-8sq-hvn
title: Foraminifera d'Orbigny, 1826

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/bnk-y19-ere
title: †Siderolites Lamarck, 1801

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/py9-l2x-xyb
title: Alopecurus pratensis L.

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/36w-rvx-ea4
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/te8-cj0-vvz
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/bc1-wfk-gh6
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/yjz-w03-d65
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/vt2-f6k-naq
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/y1s-5e2-qbb
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/td4-2zv-flx
title: Nummulitidae Blainville, 1827

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/9ga-16x-4mj
title: Plantae

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/g2n-jaq-3y6
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/rqw-8kp-sep
title: Nodosaria Lamarck, 1816

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/zmv-cga-y66
title: Nummulitidae Blainville, 1827

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/b7f-qll-fsr
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/fg2-38v-gtq
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.5281/zenodo.17353815
title: PIECING TOGETHER THE PAST THROUGH TIME: A CERAMIC ANALYSIS OF LOS SOLDADOS, MEXICO

description: Abstract:

The Olmec Civilization of the Gulf Coast lowlands is central to understanding the Formative period (2000-400 BCE) in Mesoamerican prehistory, yet the development of a universal chronology for the Olmec Heartland remains inconsistent. This difficulty is severely compounded by the Hallstatt Plateau (ca. 800-400 BCE), a segment of the radiocarbon record where date precision is significantly reduced, hindering the ability to establish clear chronological divisions. This research addresses this critical gap by analyzing ceramic attributes collected from Early to Late Formative contexts at the site of Los Soldados, a secondary Olmec settlement located near La Venta, Mexico. Utilizing data from the Proyecto Arqeológico Arroyo Pesquero (PAAP), the central objective is to determine if identifiable pottery attributes can be reliably utilized as relative chronological indicators to better differentiate phases within the plateau. Furthermore, this study tests whether the ceramic findings align with established regional chronologies. By identifying diagnostic ceramic traits and comparing them to existing seriation studies from proximal sites. The research proposes a refined relative dating method to supplement absolute dating techniques, thereby contributing essential reliable data toward the development of a unified chronology for the Olmec Heartland.
DOI: 10.5281/zenodo.17620582
title: PIECING TOGETHER THE PAST THROUGH TIME: A CERAMIC ANALYSIS OF LOS SOLDADOS, MEXICO

description: Abstract:

The Olmec Civilization of the Gulf Coast lowlands is central to understanding the Formative period (2000-400 BCE) in Mesoamerican prehistory, yet the development of a universal chronology for the Olmec Heartland remains inconsistent. This difficulty is severely compounded by the Hallstatt Plateau (ca. 800-400 BCE), a segment of the radiocarbon record where date precision is significantly reduced, hindering the ability to establish clear chronological divisions. This research addresses this critical gap by analyzing ceramic attributes collected from Early to Late Formative contexts at the site of Los Soldados, a secondary Olmec settlement located near La Venta, Mexico. Utilizing data from the Proyecto Arqeológico Arroyo Pesquero (PAAP), the central objective is to determine if identifiable pottery attributes can be reliably utilized as relative chronological indicators to better differentiate phases within the plateau. Furthermore, this study tests whether the ceramic findings align with established regional chronologies. By identifying diagnostic ceramic traits and comparing them to existing seriation studies from proximal sites. The research proposes a refined relative dating method to supplement absolute dating techniques, thereby contributing essential reliable data toward the development of a unified chronology for the Olmec Heartland.
DOI: 10.15468/dl.2pvdn4
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-113.6688 29.99913, -113.5188 29.99913, -113.5188 30.14913, -113.6688 30.14913, -113.6688 29.99913))"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037058-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12549243
title: Data from: Mapping canopy cover for municipal forestry monitoring: Using free Landsat imagery and machine learning

description: Paper Abstract:

Trees across the urban-rural continuum are recognized for their ecological importance and ecosystem services. Municipalities often utilize spatial canopy cover data for monitoring this resource. Monitoring frameworks typically rely on fine-scale maps derived from very high spatial resolution sensors, which are high quality but expensive and unwieldy for consistent wide-area monitoring. In this paper, we explore how free Landsat imagery, supported by very high-resolution imagery interpretation and/or digital hemispherical photographs, can be used to effectively map canopy cover at a scale appropriate for municipal monitoring. We compare linear models and random forest machine learning for predicting canopy cover across a landscape (general) and within specific land covers (specialized). We create 2018 canopy cover maps and track progress towards forestry objectives in a region of southern Ontario, Canada. Random forest models using all reference data perform best for general use (R2: 0.90, RMSE: 10.1%), separating non-canopy vegetation (e.g., agricultural fields) from tree canopy. Specialized models are useful in forest land cover patches, where hemispherical photographs relate with Landsat at a moderate strength (R2: 0.67, RMSE: 2.73%), and in residential areas, capturing the totality of canopy cover variation (R2: 0.85, RMSE: 5.66%). Accuracy was assessed with standard cross-validation, which is useful given limited resources. However, following best practice, an independent reference sample was also leveraged to assess the best general model (R2: 0.86, RMSE: 11.4%), indicating that cross-validation was slightly overoptimistic. Results show that Caledon, a rural-dominant municipality within the study area, is the greenest (34% canopy cover). The two cities (Brampton and Mississauga) have 15.9% and 17.5% canopy cover. Residential canopy criteria indicate “Good” performance in Caledon, “Moderate” in Mississauga, and “Low” in Brampton based on our 2018 assessment. The methods described here can provide municipalities with a low-cost approach for tree canopy monitoring across complex landscapes.

 

Data details:

See paper: Mapping canopy cover for municipal forestry monitoring: Using free Landsat imagery and machine learning - ScienceDirect

See code on GitHub: ZZMitch/PredictTreeCC_Landsat_1972to2020: Code from the portion of my PhD about using Landsat time-series to predict tree canopy cover from 1972 - 2020. Code will be released as papers are published. (github.com)

 

If you use these data, please reference:

Bonney, M.T., He, Y., Vogeler, J., Conway, T., Kaye, E., 2024. Mapping canopy cover for municipal forestry monitoring: Using free Landsat imagery and machine learning. Urban For. Urban Greening 128490. https://doi.org/10.1016/j.ufug.2024.128490.
DOI: 10.5281/zenodo.12549244
title: Data from: Mapping canopy cover for municipal forestry monitoring: Using free Landsat imagery and machine learning

description: Paper Abstract:

Trees across the urban-rural continuum are recognized for their ecological importance and ecosystem services. Municipalities often utilize spatial canopy cover data for monitoring this resource. Monitoring frameworks typically rely on fine-scale maps derived from very high spatial resolution sensors, which are high quality but expensive and unwieldy for consistent wide-area monitoring. In this paper, we explore how free Landsat imagery, supported by very high-resolution imagery interpretation and/or digital hemispherical photographs, can be used to effectively map canopy cover at a scale appropriate for municipal monitoring. We compare linear models and random forest machine learning for predicting canopy cover across a landscape (general) and within specific land covers (specialized). We create 2018 canopy cover maps and track progress towards forestry objectives in a region of southern Ontario, Canada. Random forest models using all reference data perform best for general use (R2: 0.90, RMSE: 10.1%), separating non-canopy vegetation (e.g., agricultural fields) from tree canopy. Specialized models are useful in forest land cover patches, where hemispherical photographs relate with Landsat at a moderate strength (R2: 0.67, RMSE: 2.73%), and in residential areas, capturing the totality of canopy cover variation (R2: 0.85, RMSE: 5.66%). Accuracy was assessed with standard cross-validation, which is useful given limited resources. However, following best practice, an independent reference sample was also leveraged to assess the best general model (R2: 0.86, RMSE: 11.4%), indicating that cross-validation was slightly overoptimistic. Results show that Caledon, a rural-dominant municipality within the study area, is the greenest (34% canopy cover). The two cities (Brampton and Mississauga) have 15.9% and 17.5% canopy cover. Residential canopy criteria indicate “Good” performance in Caledon, “Moderate” in Mississauga, and “Low” in Brampton based on our 2018 assessment. The methods described here can provide municipalities with a low-cost approach for tree canopy monitoring across complex landscapes.

 

Data details:

See paper: Mapping canopy cover for municipal forestry monitoring: Using free Landsat imagery and machine learning - ScienceDirect

See code on GitHub: ZZMitch/PredictTreeCC_Landsat_1972to2020: Code from the portion of my PhD about using Landsat time-series to predict tree canopy cover from 1972 - 2020. Code will be released as papers are published. (github.com)

 

If you use these data, please reference:

Bonney, M.T., He, Y., Vogeler, J., Conway, T., Kaye, E., 2024. Mapping canopy cover for municipal forestry monitoring: Using free Landsat imagery and machine learning. Urban For. Urban Greening 128490. https://doi.org/10.1016/j.ufug.2024.128490.
DOI: 10.15468/dl.p2c6ab
title: Occurrence Download

description: A dataset containing 65 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-113.5188 29.99913, -113.3688 29.99913, -113.3688 30.14913, -113.5188 30.14913, -113.5188 29.99913))"
 ]
}

 The dataset includes 65 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037060-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2mvbeg
title: Occurrence Download

description: A dataset containing 101 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Neogymnocrinus richeri"
 ]
}

 The dataset includes 101 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037062-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12789027
title: Data from: Attributing drivers to spatio-temporal changes in tree density across a suburbanizing landscape since 1944

description: Paper Abstract:

Urban expansion, especially suburbanization, represents a major social, economic and environmental shift that has escalated since the mid-1900s in North America. Suburban development leads to corresponding changes in the treed environment of urban-rural fringes. It is important to understand where, when and why trees change in response to development over many decades, but this is difficult since long-term data are scarce. We used 70+ years (1944–2017) of leaf-off aerial photographs, often representing the only long-term landscape record, to quantify and map spatio-temporal changes in tree density through the entirety of the agricultural-suburban transitional period. Photo-interpretation of individual tree locations, along with recording observable drivers of change, was completed across six different modern landscapes in Mississauga, Ontario, Canada. Results indicate that tree density often recovers, or even increases, post-development. There are differences between landscapes, but most tree density gains are associated with forest expansion and tree planting, while most losses are associated with building and road construction. The influence of these drivers, along with the temporal trajectory of tree density changes, are shaped by a landscape’s socioecological legacy and the length, scope and intensity of development (as decided by decision makers). Processes include initial tree losses followed by recovery from tree planting, and forest succession in abandoned fields after land purchase and nearby development. We assert that the spatio-temporal changes in tree density and related drivers quantified here can be generalized to gain knowledge on how tree density and distribution across agricultural landscapes will change under different development scenarios.

 

Data details:

See paper: Attributing drivers to spatio-temporal changes in tree density across a suburbanizing landscape since 1944 - ScienceDirect

See code on GitHub: ZZMitch/SuburbanizingTreeDensity_1944to2017: Code from "Attributing drivers to spatio-temporal changes in tree density across a suburbanizing landscape since 1944" (L&UP, 2019) (github.com)

- Note: High resolution imagery is not included in this repository. 

 

If you use these data, please reference: 

Bonney, M.T., He, Y., 2019. Attributing drivers to spatio-temporal changes in tree density across a suburbanizing landscape since 1944. Landscape and Urban Planning 192, https://doi.org/10.1016/j.landurbplan.2019.103652.