cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.30246793
title: Scan Statistics for the Detection of Anomalies in <i>M</i>-Dependent Random Fields with Applications to Image Data

description: Anomaly detection in random fields is an important problem in many applications including the detection of cancerous cells in medicine, obstacles in autonomous driving and cracks in the construction material of buildings. Such anomalies are often visible as areas with different expected values compared to the background noise. Scan statistics based on local means have the potential to detect such local anomalies by enhancing relevant features. We derive limit theorems for a general class of such statistics over <i>M</i>-dependent random fields of arbitrary but fixed dimension. By allowing for a variety of combinations and contrasts of sample means over differently shaped local windows, this yields a flexible class of scan statistics that can be tailored to the particular application of interest. The latter is demonstrated for crack detection in 2D-images of different types of concrete. Together with a simulation study this indicates the potential of the proposed methodology for the detection of anomalies in a variety of situations. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.30246793.v1
title: Scan statistics for the detection of anomalies in <i>M</i>-dependent random fields with applications to image data*

description: Anomaly detection in random fields is an important problem in many applications including the detection of cancerous cells in medicine, obstacles in autonomous driving and cracks in the construction material of buildings. Such anomalies are often visible as areas with different expected values compared to the background noise. Scan statistics based on local means have the potential to detect such local anomalies by enhancing relevant features. We derive limit theorems for a general class of such statistics over <i>M</i>-dependent random fields of arbitrary but fixed dimension. By allowing for a variety of combinations and contrasts of sample means over differently-shaped local windows, this yields a flexible class of scan statistics that can be tailored to the particular application of interest. The latter is demonstrated for crack detection in 2D-images of different types of concrete. Together with a simulation study this indicates the potential of the proposed methodology for the detection of anomalies in a variety of situations.
DOI: 10.6084/m9.figshare.30246793.v2
title: Scan Statistics for the Detection of Anomalies in <i>M</i>-Dependent Random Fields with Applications to Image Data

description: Anomaly detection in random fields is an important problem in many applications including the detection of cancerous cells in medicine, obstacles in autonomous driving and cracks in the construction material of buildings. Such anomalies are often visible as areas with different expected values compared to the background noise. Scan statistics based on local means have the potential to detect such local anomalies by enhancing relevant features. We derive limit theorems for a general class of such statistics over <i>M</i>-dependent random fields of arbitrary but fixed dimension. By allowing for a variety of combinations and contrasts of sample means over differently shaped local windows, this yields a flexible class of scan statistics that can be tailored to the particular application of interest. The latter is demonstrated for crack detection in 2D-images of different types of concrete. Together with a simulation study this indicates the potential of the proposed methodology for the detection of anomalies in a variety of situations. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.15151/esrf-es-2285429354
title: Heidelberg BAG (HeidelBAG)

description: The HeidelBAG unifies structural efforts of EMBL Heidelberg inclusive former PIs and the Heidelberg University Biochemistry Center (BZH). Central to all projects are large assemblies involved in chromosome organization and transcription control, in nuclear and cytoplasmic RNA regulation, in translational control by ribosome associated factors, in protein targeting and membrane protein insertion, or in post-translational and RNA modification and processing. Nearly all projects make use of integrated structural biology techniques (MX, SAXS/SANS, NMR, cryo-EM) combined with complementary biophysical approaches. MX remains the essential and invariant tool for high resolution information of challenging functional modules.
DOI: 10.6084/m9.figshare.30107105
title: Macrophyte encounter rates on watercraft using boat launches in the Finger Lakes region of New York State

description: Brown WP, Beck-Andersen S, Cleckner LB. 2025. Macrophyte encounter rates on watercraft using boat launches in the Finger Lakes region of New York State. Lake Reserv Manage. 41:270–280. Watercraft inspection stewards in the Finger Lakes region of New York State educated boat operators about the identification and effects of aquatic invasive species and inspected watercraft and trailers entering and exiting waterbodies. In addition to recording and removing all macrophytes encountered, stewards noted intended activity on the water, type of watercraft, watercraft direction (launching or retrieving), the name of the waterbody, steward, and launch, with the date and year. Launches were categorized into one of 8 compass directions relative to the waterbody’s center, as prevailing winds may influence the presence of macrophyte fragments. From 2021 to 2023, 58 stewards conducted 79,326 inspections at 23 launches. Using a mixed effects generalized additive model, we analyzed 9 variables to assess their mutual influence on macrophyte encounter rates on watercraft and trailers. Date was smoothed with a cubic spline, and year was included as a random effect. The final model, selected based on the Akaike information criterion (AIC), included direction, activity, type of watercraft, launch, steward, date, and year, explaining ∼30% of deviance overall. Direction alone explained ∼18% of the deviance: Macrophyte encounter rates on watercraft and trailers exiting waterbodies were 5.5 times higher than on those entering (41% vs. 7.5%). Future research should examine relationships between encounter rates and seasonal biomass phenology, quantify separate encounter rates for watercraft and trailers, and evaluate macrophyte management strategies at launches.
DOI: 10.6084/m9.figshare.30107105.v1
title: Macrophyte encounter rates on watercraft using boat launches in the Finger Lakes region of New York State

description: Brown WP, Beck-Andersen S, Cleckner LB. 2025. Macrophyte encounter rates on watercraft using boat launches in the Finger Lakes region of New York State. Lake Reserv Manage. 41:270–280. Watercraft inspection stewards in the Finger Lakes region of New York State educated boat operators about the identification and effects of aquatic invasive species and inspected watercraft and trailers entering and exiting waterbodies. In addition to recording and removing all macrophytes encountered, stewards noted intended activity on the water, type of watercraft, watercraft direction (launching or retrieving), the name of the waterbody, steward, and launch, with the date and year. Launches were categorized into one of 8 compass directions relative to the waterbody’s center, as prevailing winds may influence the presence of macrophyte fragments. From 2021 to 2023, 58 stewards conducted 79,326 inspections at 23 launches. Using a mixed effects generalized additive model, we analyzed 9 variables to assess their mutual influence on macrophyte encounter rates on watercraft and trailers. Date was smoothed with a cubic spline, and year was included as a random effect. The final model, selected based on the Akaike information criterion (AIC), included direction, activity, type of watercraft, launch, steward, date, and year, explaining ∼30% of deviance overall. Direction alone explained ∼18% of the deviance: Macrophyte encounter rates on watercraft and trailers exiting waterbodies were 5.5 times higher than on those entering (41% vs. 7.5%). Future research should examine relationships between encounter rates and seasonal biomass phenology, quantify separate encounter rates for watercraft and trailers, and evaluate macrophyte management strategies at launches.
DOI: 10.17026/ar/3ohxfg
title: PAN-00175447 - pinch lead

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00175447&lt;/p&gt;
description: Modified: 2025-10-22T09:50:28+02:00
DOI: 10.15468/dl.rw5c6k
title: Occurrence Download

description: A dataset containing 87 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Peru",
 "TaxonKey is Prunus pleiantha"
 ]
}

 The dataset includes 87 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038072-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17514312
title: ML and MW solutions for the BELSHAKE database

description: This dataset contains local magnitude (ML) and moment magnitude (MW) solutions for earthquakes in the BELSHAKE database recorded by stations of the Belgian seismic network.

It consists of 5 tables in CSV-format and an accompanying TXT file explaining the different fields in each table.

For both ML and MW, there are separate tables for the event solutions and the individual station solutions, from which the former are derived. For ML, there is an additional table with the average station residuals.

For both magnitude types, different estimates are available, which can be extracted based on the corresponding table columns.



ML: using



different ways to calculate ML (ml_method column):



cam85: original Belgian ML scale

hrf2020: ML scale of the Bensberg network (Germany)

powerlaw: recalibrated Belgian ML scale



2 different ways to restitute the waveforms to displacement (restitution_method column):



rob: instrument response removal, followed by integration and 2x high-pass filtering (1 Hz, 4 poles)

wood_anderson: instrument response removal, followed by simulation of Wood-Anderson instrument (with gain=1)



2 different peak estimation methods (peak_method column)



strict_p2p=0: between successive peaks (peak method 1)

strict_p2p=1: taking into account intervening gradient changes using a Savitzky-Golay filter with a window length of 0.1 s (peak method 2)



different components (comp_code column)



Z: vertical component

E: east-west component

N: north-south component

GM: geometric mean of horizontal components






The recalibrated Belgian ML scale is based on fitting the combination (cam85, wood_anderson, strict_p2p=1, Z). The final recalibration corresponds to the combination (powerlaw, wood_anderson, strict_p2p=1, Z).



MW: using



2 different wave types (wave_type column):



P-wave (Z component)

SH-wave (T component)



2 different radiation patterns (radiation_pattern column):



average

based on focal mechanism



2 different geometrical spreading models (spreading_model column):



1/R

segmented (piecewise continuous powerlaw model, segmented in log space)






The final adopted value in the BELSHAKE database is the weighted average of the combinations (P, average, 1/R) and (SH, average, segmented).
DOI: 10.5281/zenodo.17514313
title: ML and MW solutions for the BELSHAKE database

description: This dataset contains local magnitude (ML) and moment magnitude (MW) solutions for earthquakes in the BELSHAKE database recorded by stations of the Belgian seismic network.

It consists of 5 tables in CSV-format and an accompanying TXT file explaining the different fields in each table.

For both ML and MW, there are separate tables for the event solutions and the individual station solutions, from which the former are derived. For ML, there is an additional table with the average station residuals.

For both magnitude types, different estimates are available, which can be extracted based on the corresponding table columns.



ML: using



different ways to calculate ML (ml_method column):



cam85: original Belgian ML scale

hrf2020: ML scale of the Bensberg network (Germany)

powerlaw: recalibrated Belgian ML scale



2 different ways to restitute the waveforms to displacement (restitution_method column):



rob: instrument response removal, followed by integration and 2x high-pass filtering (1 Hz, 4 poles)

wood_anderson: instrument response removal, followed by simulation of Wood-Anderson instrument (with gain=1)



2 different peak estimation methods (peak_method column)



strict_p2p=0: between successive peaks (peak method 1)

strict_p2p=1: taking into account intervening gradient changes using a Savitzky-Golay filter with a window length of 0.1 s (peak method 2)



different components (comp_code column)



Z: vertical component

E: east-west component

N: north-south component

GM: geometric mean of horizontal components






The recalibrated Belgian ML scale is based on fitting the combination (cam85, wood_anderson, strict_p2p=1, Z). The final recalibration corresponds to the combination (powerlaw, wood_anderson, strict_p2p=1, Z).



MW: using



2 different wave types (wave_type column):



P-wave (Z component)

SH-wave (T component)



2 different radiation patterns (radiation_pattern column):



average

based on focal mechanism



2 different geometrical spreading models (spreading_model column):



1/R

segmented (piecewise continuous powerlaw model, segmented in log space)






The final adopted value in the BELSHAKE database is the weighted average of the combinations (P, average, 1/R) and (SH, average, segmented).
DOI: 10.15468/dl.xjtpcj
title: Occurrence Download

description: A dataset containing 2178 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tracheophyta",
 "DecimalLatitude is greater than or equal to 69.085278",
 "DecimalLatitude is less than or equal to 74.085278",
 "DecimalLongitude is greater than or equal to 110.0",
 "DecimalLongitude is less than or equal to 115.0",
 "HasCoordinate is TRUE",
 "HasGeospatialIssue is FALSE"
 ]
}

 The dataset includes 2178 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038071-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nqctt2
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((112.129 0.094185, 112.058737 0.088618, 111.990204 0.07205400000000001, 111.925088 0.044901, 111.864994 0.007828, 111.811399 -0.038253, 111.765625 -0.092207, 111.728797 -0.152705, 111.701823 -0.218259, 111.685367 -0.287253, 111.679834 -0.357989, 111.68536 -0.428726, 111.70181 -0.497721, 111.728779 -0.563277, 111.765604 -0.6237780000000001, 111.811377 -0.677735, 111.864973 -0.72382, 111.925071 -0.760896, 111.990191 -0.7880509999999999, 112.05873 -0.804617, 112.129 -0.810184, 112.19927 -0.804617, 112.267809 -0.7880509999999999, 112.332929 -0.760896, 112.393027 -0.72382, 112.446623 -0.677735, 112.492396 -0.6237780000000001, 112.529221 -0.563277, 112.55619 -0.497721, 112.57264 -0.428726, 112.578166 -0.357989, 112.572633 -0.287253, 112.556177 -0.218259, 112.529203 -0.152705, 112.492375 -0.092207, 112.446601 -0.038253, 112.393006 0.007828, 112.332912 0.044901, 112.267796 0.07205400000000001, 112.199263 0.088618, 112.129 0.094185))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038074-251120083545085/datasets/export for details.
DOI: 10.15468/dl.pf7ccw
title: Occurrence Download

description: A dataset containing 42 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Trachypithecus leucocephalus",
 "HasCoordinate is TRUE"
 ]
}

 The dataset includes 42 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038075-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vg6jw8
title: Occurrence Download

description: A dataset containing 2348 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is France",
 "DatasetKey is one of (Rapportage 2007-2012 au titre de la directive Habitats-Faune-Flore - Données de synthèse Espèces issues du rapportage au titre de la directive Habitats - période 2007-2012, Rapportage 2013-2018 au titre de la directive Habitats-Faune-Flore - Données de synthèse Espèces issues du rapportage au titre de la directive Habitats - période 2013-2018)",
 "OccurrenceStatus is Present",
 "TaxonKey is Triturus cristatus"
 ]
}

 The dataset includes 2348 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038076-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/ar/yycpkl
title: PAN-00127683 - coin/coin-related, Gallische tegenkeizer (?), naar, antoninianus

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00127683&lt;/p&gt;
description: Modified: 2025-10-21T11:19:19+02:00
DOI: 10.15468/dl.ubzh5s
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((101.398 0.103185, 101.327737 0.097618, 101.259204 0.081054, 101.194088 0.053901, 101.133994 0.016828, 101.080399 -0.029253, 101.034625 -0.083207, 100.997797 -0.143706, 100.970823 -0.209259, 100.954367 -0.278253, 100.948834 -0.348989, 100.954361 -0.419726, 100.970811 -0.488722, 100.99778 -0.554277, 101.034604 -0.614778, 101.080378 -0.668736, 101.133973 -0.71482, 101.194071 -0.751896, 101.259191 -0.779051, 101.32773 -0.795617, 101.398 -0.801184, 101.46827 -0.795617, 101.536809 -0.779051, 101.601929 -0.751896, 101.662027 -0.71482, 101.715622 -0.668736, 101.761396 -0.614778, 101.79822 -0.554277, 101.825189 -0.488722, 101.841639 -0.419726, 101.847166 -0.348989, 101.841633 -0.278253, 101.825177 -0.209259, 101.798203 -0.143706, 101.761375 -0.083207, 101.715601 -0.029253, 101.662006 0.016828, 101.601912 0.053901, 101.536796 0.081054, 101.468263 0.097618, 101.398 0.103185))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038077-251120083545085/datasets/export for details.
DOI: 10.15468/dl.63x6gx
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Quercus robur",
 "HasCoordinate is true",
 "Geometry POLYGON((55.08 5.87, 55.08 15.04, 47.27 15.04, 47.27 5.87, 55.08 5.87))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038078-251120083545085/datasets/export for details.
DOI: 10.15468/dl.5abzxh
title: Occurrence Download

description: A dataset containing 1025 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tracheophyta",
 "DecimalLatitude is greater than or equal to 74.085278",
 "DecimalLatitude is less than or equal to 79.085278",
 "DecimalLongitude is greater than or equal to 110.0",
 "DecimalLongitude is less than or equal to 115.0",
 "HasCoordinate is TRUE",
 "HasGeospatialIssue is FALSE"
 ]
}

 The dataset includes 1025 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038080-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pcyrhh
title: Occurrence Download

description: A dataset containing 10392 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (iNaturalist Research-grade Observations)",
 "Geometry POLYGON(( -123.517678884873 39.4373040777211 , -122.543147590988 39.4373040777211 , -122.543147590988 40.4429899961914 , -123.517678884873 40.4429899961914 , -123.517678884873 39.4373040777211 ))",
 "Year is greater than or equal to 2016"
 ]
}

 The dataset includes 10392 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038079-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vcw3ab
title: Occurrence Download

description: A dataset containing 18 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Trachypithecus margarita",
 "HasCoordinate is TRUE"
 ]
}

 The dataset includes 18 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038081-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/ar/qt85ws
title: PAN-00175637 - pinch lead

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00175637&lt;/p&gt;
description: Modified: 2025-10-28T14:48:03+01:00
DOI: 10.15468/3kt7sc
title: norfish_irish_herring_fishery_1520-1790

description: The Irish herring fisheries were highly productive at the beginning of the sixteenth century and were one of the largest industries in Ireland at that time (Hayes, 2021). The fishery suffered a decline towards the end of the sixteenth century due to a mixture of persistent warfare, environmental change and shifts in market conditions. The fishery recovered in the seventeenth century but was severely disrupted during the 1641 Rebellion and Confederate period in Ireland. Large scale herring fishing again resumed in the later seventeenth century and continued to fluctuate over the next century, before briefly becoming one of the most productive herring fisheries in Europe in the later eighteenth century.