cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.2672978
title: Comunicado 003/2012 - Atualização do WEBQUALIS da área (PLURD)

description: <strong>Comunicado 003/2012 - Atualização do WEBQUALIS da área</strong>
DOI: 10.5281/zenodo.2672979
title: Comunicado 003/2012 - Atualização do WEBQUALIS da área (PLURD)

description: <strong>Comunicado 003/2012 - Atualização do WEBQUALIS da área</strong>
DOI: 10.6073/pasta/d65f0ca34bea1457119d815b8d5d9aa7
title: Groundwater level data from 2013 to 2015 in Central Sands area, Wisconsin

description: 38 groundwater wells' water level data in Central Sands area,
  Wisconsin.

      
  The Central Sands is an area of the state with a high density of
  high-capacity wells for irrigation. The Wisconsin Department of
  Natural Resources (WDNR), UW Extension, and the counties began
  monitoring shallow groundwater wells near lakes in 2013. The five
  counties include: Portage, Waupaca, Adams, Waushara, and Marquette.
  Volunteers were enlisted to monitor lake levels on 38 lakes in the
  region. Instead of using staff gauges, the team opted to install
  shallow groundwater wells near the lake shore. This eliminated the
  need to reinstall and survey staff gauges each season. It was assumed
  that shallow groundwater wells would be a good approximation of lake
  levels because the soils are made up of porous, sand material. On a
  subset of lakes, WDNR collected water level data from the shallow
  groundwater wells and from staff gauges at the same time. One season
  of monitoring showed that changes in water levels over time are
  similar, but the actual elevation of water in the well might be
  different than the surface of the lake. This data set can be
  downloaded from WDNR's SWIMS database:
  https://dnr.wi.gov/topic/surfacewater/swims/
DOI: 10.5281/zenodo.2672881
title: Picker-routing problem instances

description: The following instances are originally presented in the working paper "High-density storage with mobile racks: Picker routing and product location" written by Foroughi, Amir,  Boysen, Nils, Emde, Simon, and Schneider, Michael.

The data corresponds to 360 instances each in its own file. Each file is structured as follows:


	The first line contains: the number of racks, the number of locations in each rack. (Please note that the number of aisles in each instance is the number of racks minus one)
	Then there follows a line with the name of each SKU.
	Then a line with waiting time in seconds (the time it takes for an aisle to be opened)
	Then a line with the distances from the aisle cross to the depot for each aisle.
	Then a line with the driving time between two consecutive aisles. It means how long it takes for the SRV to travel from one cross aisle to the next cross aisle.
	Then the lines for the location of SKUs in each rack, where each line is associated with one rack. In each pair of numbers, the first number is the name of SKU and the second number is the position of that SKU in the rack. For example, in the first rack, 3 1 5 3 2 4 means SKU 3 is in location 1, SKU 5 is in location 3, and SKU 2 is in location 4.


All numbers in the same line are separated by spaces.
DOI: 10.5281/zenodo.2672882
title: Picker-routing problem instances

description: The following instances are originally presented in the working paper "High-density storage with mobile racks: Picker routing and product location" written by Foroughi, Amir,  Boysen, Nils, Emde, Simon, and Schneider, Michael.

The data corresponds to 360 instances each in its own file. Each file is structured as follows:


	The first line contains: the number of racks, the number of locations in each rack. (Please note that the number of aisles in each instance is the number of racks minus one)
	Then there follows a line with the name of each SKU.
	Then a line with waiting time in seconds (the time it takes for an aisle to be opened)
	Then a line with the distances from the aisle cross to the depot for each aisle.
	Then a line with the driving time between two consecutive aisles. It means how long it takes for the SRV to travel from one cross aisle to the next cross aisle.
	Then the lines for the location of SKUs in each rack, where each line is associated with one rack. In each pair of numbers, the first number is the name of SKU and the second number is the position of that SKU in the rack. For example, in the first rack, 3 1 5 3 2 4 means SKU 3 is in location 1, SKU 5 is in location 3, and SKU 2 is in location 4.


All numbers in the same line are separated by spaces.
DOI: 10.6073/pasta/3af6d433db427810280f52e7b8f801da
title: PIE LTER marsh water table height, logging data from the railroad Spartina marsh site on the Parker River, Newbury, MA, for April-November 2016.

description: Measurements of water table height in the Parker River marsh located downstream of the railroad bridge. Measurements were taken every 5 minutes at each logger along a transect of water level loggers running perpendicular to the Parker River bank at the railroad site, MAR-PR-Wtable-RR for April-November 2016.
DOI: 10.6073/pasta/123fe02be4698ab9640e68fdf681c204
title: Lake Water Level observations for 1036 lakes in Wisconsin, 1900 - 2015

description: This dataset contains the daily lake level observations and other lake attributes in Wisconsin. It covers 1036 lakes including 461 seepage lakes and 575 drainage lakes. It has 342,319 observations. The time span of this dataset is between January 1st, 1900 and December 31st, 2015. The data sources include USGS, Wisconsin Department of Natural Resources, North Temperate Lakes-Long Term Ecological Research (NTL-LTER), North Lakeland Discovery Center, Waushara County, and City of Shell Lake. Wisconsin Department of Natural Resources has two data sources: historical lake levels recorded in paper files and a recently-initiated citizen monitoring program. The latter are stored in Wisconsin DNR’s Surface Water Integrated Monitoring System (SWIMS).
The data compilation consists of four major steps. First, data were retrieved from different data sources. Then data from different sources but for the same lakes were tied together using the datum information if possible. The WISCID is used to denote unique data sets by lake and data source. If two data sources could be tied to the same datum, they share a WISCID. Third, three rounds of quality assurance and quality control (QAQC) were conducted. Finally, more attributes such as lake area, lake depth, and lake type were added to the lakes. This data compilation was funded by the Wisconsin Groundwater Joint Solicitation.
DOI: 10.17632/4cxyxs83pj.1
title: Data for: Sparse Support Vector Machines with L0 Approximation for Ultra-high Dimensional Omics Data

description: MATLAB toolbox for L0SVM
DOI: 10.17632/8zkd43s3xs.1
title: Data for: Sparse Support Vector Machines with L0 Approximation for Ultra-high Dimensional Omics Data

description: Software for L0SVM
DOI: 10.5519/qd.89wqdr7r
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (3655 records)
DOI: 10.5519/qd.94oo7bq6
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (47 records)
DOI: 10.6073/pasta/3cd3ebfa202816f5b224d172025aa5a0
title: Lake Water Level observations for 1036 lakes in Wisconsin, 1900 - 2015

description: This dataset contains the daily lake level observations and other lake attributes in Wisconsin. It covers 1036 lakes including 461 seepage lakes and 575 drainage lakes. It has 342,319 observations. The time span of this dataset is between January 1st, 1900 and December 31st, 2015. The data sources include USGS, Wisconsin Department of Natural Resources, North Temperate Lakes-Long Term Ecological Research (NTL-LTER), North Lakeland Discovery Center, Waushara County, and City of Shell Lake. Wisconsin Department of Natural Resources has two data sources: historical lake levels recorded in paper files and a recently-initiated citizen monitoring program. The latter are stored in Wisconsin DNR’s Surface Water Integrated Monitoring System (SWIMS).
The data compilation consists of four major steps. First, data were retrieved from different data sources. Then data from different sources but for the same lakes were tied together using the datum information if possible. The WISCID is used to denote unique data sets by lake and data source. If two data sources could be tied to the same datum, they share a WISCID. Third, three rounds of quality assurance and quality control (QAQC) were conducted. Finally, more attributes such as lake area, lake depth, and lake type were added to the lakes. This data compilation was funded by the Wisconsin Groundwater Joint Solicitation.
DOI: 10.6073/pasta/df84b82d57a387233ef1b9b0f6590af9
title: Groundwater level data from 2013 to 2015 in Central Sands area, Wisconsin

description: 38 groundwater wells' water level data in Central Sands area,
  Wisconsin.

      
  The Central Sands is an area of the state with a high density of
  high-capacity wells for irrigation. The Wisconsin Department of
  Natural Resources (WDNR), UW Extension, and the counties began
  monitoring shallow groundwater wells near lakes in 2013. The five
  counties include: Portage, Waupaca, Adams, Waushara, and Marquette.
  Volunteers were enlisted to monitor lake levels on 38 lakes in the
  region. Instead of using staff gauges, the team opted to install
  shallow groundwater wells near the lake shore. This eliminated the
  need to reinstall and survey staff gauges each season. It was assumed
  that shallow groundwater wells would be a good approximation of lake
  levels because the soils are made up of porous, sand material. On a
  subset of lakes, WDNR collected water level data from the shallow
  groundwater wells and from staff gauges at the same time. One season
  of monitoring showed that changes in water levels over time are
  similar, but the actual elevation of water in the well might be
  different than the surface of the lake. This data set can be
  downloaded from WDNR's SWIMS database:
  https://dnr.wi.gov/topic/surfacewater/swims/
DOI: 10.5281/zenodo.2654563
title: OpenIng - Open Access und Open Educational Resources in den Ingenieurwissenschaften : Fragebogen zur bundesweiten Umfrage

description: Im Rahmen eines vom Bundesministerium für Bildung und Forschung (BMBF) geförderten Verbundprojekts der TU Darmstadt, TU Braunschweig und der Universität Stuttgart startete im Oktober 2018 eine Befragung zum Publikationsverhalten in den Ingenieurwissenschaften im deutschsprachigen Raum. Ziel des Projekts OpenIng ist, nach den Ergebnissen einer Bedarfsermittlung zielgerichtete Services zu etablieren, die das Publizieren in Open Access erleichtern sollen. Forscherinnen und Forscher waren aufgerufen, sich an der Umfrage zu beteiligen und hiermit an ihr Fach angepasste Services mit zu entwickeln und so den Prozess Richtung freie Verfügbarkeit von Wissenschaft mit zu gestalten. Grundlage für eine Entwicklung neuer Angebote sind die Ergebniss aus den Online-Fragebögen.
DOI: 10.5281/zenodo.2654564
title: OpenIng - Open Access und Open Educational Resources in den Ingenieurwissenschaften : Fragebogen zur bundesweiten Umfrage

description: Im Rahmen eines vom Bundesministerium für Bildung und Forschung (BMBF) geförderten Verbundprojekts der TU Darmstadt, TU Braunschweig und der Universität Stuttgart startete im Oktober 2018 eine Befragung zum Publikationsverhalten in den Ingenieurwissenschaften im deutschsprachigen Raum. Ziel des Projekts OpenIng ist, nach den Ergebnissen einer Bedarfsermittlung zielgerichtete Services zu etablieren, die das Publizieren in Open Access erleichtern sollen. Forscherinnen und Forscher waren aufgerufen, sich an der Umfrage zu beteiligen und hiermit an ihr Fach angepasste Services mit zu entwickeln und so den Prozess Richtung freie Verfügbarkeit von Wissenschaft mit zu gestalten. Grundlage für eine Entwicklung neuer Angebote sind die Ergebniss aus den Online-Fragebögen.
DOI: 10.5519/qd.lfl39tgf
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (3 records)
DOI: 10.25919/5cd285137e0d7
title: Parkes pulsar observations with undefined project IDs for 2019_09

description: Parkes pulsar observations with undefined project IDs for 2019. This collections contains observations from project ID PX048.
DOI: 10.5519/qd.5wcyxzwn
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (12 records)
DOI: 10.17632/tjfr3ddc52.1
title: Morphological study of fleas in genus Ctenocephalides

description: Images depicting morphological features of fleas in the genus Ctenocephalides. Included taxa are: Ctenocephalides felis, C. canis, C. orientis, C. connatus and an unknown species denoted as Ctenocephalides sp. 'AL909' (female only). Unknown species is morphologically indistinguishable from C. f. felis, males are needed to confirm species identity. Subspecies included are: Ctenocephalides felis felis, C. f. strongylus, C. f. damarensis (male only) and an intermediate form denoted as C. f. "transitional". Images depict male and female specimens for each species. Image names are in the format (SUB)SPECIES_SEX_SAMPLE IDENTIFIER(ALXXX-X)_FEATURE. Key to terminology: LMA = Lateral metanotal area (note: number of bristles) SPB tarsi = spiniform plantar bristles present on fore tarsus V
DOI: 10.17632/2f3hchym9v.1
title: Global flea collection specimen details: Supplementary Table 1

description: A total of 5128 fleas (3585 female, 1520 male and 23 unknown) were collected from 57 countries across all continents (n = 6) except Antarctica, from October 2012 to March 2017. The most common flea was the cat flea Ctenocephalides felis (n = 4772; 93.0%) inclusive of three subspecies: Ctenocephalides felis felis (n = 3856), Ctenocephalides felis strongylus (n = 32) and Ctenocephalides felis damarensis (n = 4) as well as an additional number (n = 436) that were either unable to be assigned due to ambiguous morphologies (Ctenocephalides felis “transitional”; n = 29) or were not processed for slide-mounting and thus unable to be classified to subspecies level. The second most common flea including all non-Ctenocephalides species was the dog flea Ctenocephalides canis (n = 208; 4.1%) followed by the sticktight flea Echidnophaga gallinacea (n = 195; 3.8%), Ctenocephalides orientis (n = 194; 3.8%) and the human flea Pulex irritans (n = 119; 2.3%). The remaining 1% was represented by 8 species: Echidnophaga larina (n = 24), Ctenocephalides connatus (n = 12), Ceratophyllus gallinae (n = 7), Echidnophaga myrmecobii (n = 3), Nosopsyllus fasciatus (n = 1), Leptopsylla segnis (n = 1), Xenopsylla cheopis (n = 1) and Ceratophyllus sp. (n = 1). There was a total of 49 cases of mixed flea infestations on a single host with the most common sympatry occurring between C. felis and E. gallinacea (16 cases) followed by C. felis and C. orientis (11 cases), C. felis and C. canis (9 cases) and C. felis and P. irritans (7 cases). Fleas were predominantly collected from domestic dogs and cats. This included owned and stray dogs (Canis lupus familiaris; n = 541) and owned and feral cats (Felis catus; n = 228). An additional 33 samples were collected from either cats or dogs. Other canid hosts included red foxes (Vulpes vulpes; n = 99), jackals (Canis aureus; n = 4) and a wolf (Canis lupus signatus; n = 1). A small number of samples (n = 12) were collected from other hosts, both wild and domestic. These include four domestic goats (n = 7 fleas), five Cape ground squirrels (Xerus inauris) (n = 8 fleas), one slender mongoose (Galerella sanguinea) (n = 1 flea), one Rueppell`s Starling (Lamprotornis purpuropteraone) (n = 1 flea), two humans (n = 2 fleas) and one ferret (n= 1 flea). An average of 90 fleas were collected from each country ranging between 1 (Canada and Costa Rica) and 1093 (Australia) (median = 23) from a total of 937 hosts. The largest number of fleas including all species was obtained from Oceania (n = 1560), followed by Europe (n = 1283), Asia (including eastern Russia; n = 803), Africa (n = 742), South America (n = 412) and then North America (n = 323). Only two flea species were found across all continents: C. felis and C. canis. A further two species were present across multiple continents: Pulex irritans in Europe, Oceania, North America and Africa and Echidnophaga gallinaceae from Oceania and Africa. All remaining species were bound to single continents.
DOI: 10.5519/qd.ztcb2ukp
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (46 records)
DOI: 10.15156/bio/sh3253635.08fu
title: SH3253635.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh3253636.08fu
title: SH3253636.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh2097897.08fu
title: SH2097897.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh2097898.08fu
title: SH2097898.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh3253637.08fu
title: SH3253637.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh3253638.08fu
title: SH3253638.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.