cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.wjmes3
title: Occurrence Download

description: A dataset containing 20312 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Reynoutria japonica",
 "HasCoordinate is true",
 "Country is Germany"
 ]
}

 The dataset includes 20312 records from 93 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054923-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25397/eur.27868341
title: Disinformation for Hire

description: The replication material includes the do files and datasets to replicate the results in tables, figures and text of the main manuscript and the appendix. The code constructs the results from the field data and additional experiments we ran on Prolific and MTurk. The material contains 4 code files, all ending with “.do”. The code was last run using Stata (version 18.0) on MacOS. The replicator should expect the code to run under 5 minutes on a standard (2024) desktop machine.<b>Background</b> The spread of misinformation has been linked to increased social divisions and adverse health outcomes, but less is known about the production of disinformation, which is misinformation intended to mislead.<b>Method</b> The main data used in this paper has been collected by the authors using the Mturk interface (Field Experiment) or Qualtrics (Manipulation Check, Downstream Consequences, and Platform Interventions). It is available in the replication package. Our survey design and selection eligibility are included in the Supplementary Document in this depository.<b>Results</b> In a field experiment on MTurk (N=1,197), we found that while 70% of workers accepted a control job, 61% accepted a disinformation job requiring them to manipulate COVID-19 data. To quantify the trade-off between ethical and financial considerations in job acceptance, we introduced a lower-pay condition offering half the wage of the control job; 51% of workers accepted this job, suggesting that the ethical compromise in the disinformation task reduced the acceptance rate by about the same amount as a 25% wage reduction.A survey experiment with a nationally representative sample shows that viewing a disinformation graph from the field experiment negatively affected people’s beliefs and behavioral intentions related to the COVID-19 pandemic, including increased vaccine hesitancy.<b>Conclusion</b> Using a “wisdom-of-crowds” approach, we highlight how online labor markets can introduce features, such as increased worker accountability, to reduce the likelihood of workers engaging in the production of disinformation. Our findings emphasize the importance of addressing the supply side of disinformation in online labor markets to mitigate its harmful societal effects.
DOI: 10.25397/eur.27868341.v1
title: Disinformation for Hire

description: The replication material includes the do files and datasets to replicate the results in tables, figures and text of the main manuscript and the appendix. The code constructs the results from the field data and additional experiments we ran on Prolific and MTurk. The material contains 4 code files, all ending with “.do”. The code was last run using Stata (version 18.0) on MacOS. The replicator should expect the code to run under 5 minutes on a standard (2024) desktop machine.<b>Background</b> The spread of misinformation has been linked to increased social divisions and adverse health outcomes, but less is known about the production of disinformation, which is misinformation intended to mislead.<b>Method</b> The main data used in this paper has been collected by the authors using the Mturk interface (Field Experiment) or Qualtrics (Manipulation Check, Downstream Consequences, and Platform Interventions). It is available in the replication package. Our survey design and selection eligibility are included in the Supplementary Document in this depository.<b>Results</b> In a field experiment on MTurk (N=1,197), we found that while 70% of workers accepted a control job, 61% accepted a disinformation job requiring them to manipulate COVID-19 data. To quantify the trade-off between ethical and financial considerations in job acceptance, we introduced a lower-pay condition offering half the wage of the control job; 51% of workers accepted this job, suggesting that the ethical compromise in the disinformation task reduced the acceptance rate by about the same amount as a 25% wage reduction.A survey experiment with a nationally representative sample shows that viewing a disinformation graph from the field experiment negatively affected people’s beliefs and behavioral intentions related to the COVID-19 pandemic, including increased vaccine hesitancy.<b>Conclusion</b> Using a “wisdom-of-crowds” approach, we highlight how online labor markets can introduce features, such as increased worker accountability, to reduce the likelihood of workers engaging in the production of disinformation. Our findings emphasize the importance of addressing the supply side of disinformation in online labor markets to mitigate its harmful societal effects.
DOI: 10.15468/dl.72du7v
title: Occurrence Download

description: A dataset containing 1915 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-97.13757 37.56245,-71.15738 37.56245,-71.15738 49.8163,-97.13757 49.8163,-97.13757 37.56245))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Dreissena bugensis",
 "Year 2015-2025"
 ]
}

 The dataset includes 1915 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054926-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25397/eur.27868341.v2
title: Disinformation for Hire

description: The replication material includes the do files and datasets to replicate the results in tables, figures and text of the main manuscript and the appendix. The code constructs the results from the field data and additional experiments we ran on Prolific and MTurk. The material contains 4 code files, all ending with “.do”. The code was last run using Stata (version 18.0) on MacOS. The replicator should expect the code to run under 5 minutes on a standard (2024) desktop machine.<b>Background</b> The spread of misinformation has been linked to increased social divisions and adverse health outcomes, but less is known about the production of disinformation, which is misinformation intended to mislead.<b>Method</b> The main data used in this paper has been collected by the authors using the Mturk interface (Field Experiment) or Qualtrics (Manipulation Check, Downstream Consequences, and Platform Interventions). It is available in the replication package. Our survey design and selection eligibility are included in the Supplementary Document in this depository.<b>Results</b> In a field experiment on MTurk (N=1,197), we found that while 70% of workers accepted a control job, 61% accepted a disinformation job requiring them to manipulate COVID-19 data. To quantify the trade-off between ethical and financial considerations in job acceptance, we introduced a lower-pay condition offering half the wage of the control job; 51% of workers accepted this job, suggesting that the ethical compromise in the disinformation task reduced the acceptance rate by about the same amount as a 25% wage reduction.A survey experiment with a nationally representative sample shows that viewing a disinformation graph from the field experiment negatively affected people’s beliefs and behavioral intentions related to the COVID-19 pandemic, including increased vaccine hesitancy.<b>Conclusion</b> Using a “wisdom-of-crowds” approach, we highlight how online labor markets can introduce features, such as increased worker accountability, to reduce the likelihood of workers engaging in the production of disinformation. Our findings emphasize the importance of addressing the supply side of disinformation in online labor markets to mitigate its harmful societal effects.
DOI: 10.25397/eur.27868341.v3
title: Disinformation for Hire

description: The replication material includes the do files and datasets to replicate the results in tables, figures and text of the main manuscript and the appendix. The code constructs the results from the field data and additional experiments we ran on Prolific and MTurk. The material contains 4 code files, all ending with “.do”. The code was last run using Stata (version 18.0) on MacOS. The replicator should expect the code to run under 5 minutes on a standard (2024) desktop machine.<b>Background</b> The spread of misinformation has been linked to increased social divisions and adverse health outcomes, but less is known about the production of disinformation, which is misinformation intended to mislead.<b>Method</b> The main data used in this paper has been collected by the authors using the Mturk interface (Field Experiment) or Qualtrics (Manipulation Check, Downstream Consequences, and Platform Interventions). It is available in the replication package. Our survey design and selection eligibility are included in the Supplementary Document in this depository.<b>Results</b> In a field experiment on MTurk (N=1,197), we found that while 70% of workers accepted a control job, 61% accepted a disinformation job requiring them to manipulate COVID-19 data. To quantify the trade-off between ethical and financial considerations in job acceptance, we introduced a lower-pay condition offering half the wage of the control job; 51% of workers accepted this job, suggesting that the ethical compromise in the disinformation task reduced the acceptance rate by about the same amount as a 25% wage reduction.A survey experiment with a nationally representative sample shows that viewing a disinformation graph from the field experiment negatively affected people’s beliefs and behavioral intentions related to the COVID-19 pandemic, including increased vaccine hesitancy.<b>Conclusion</b> Using a “wisdom-of-crowds” approach, we highlight how online labor markets can introduce features, such as increased worker accountability, to reduce the likelihood of workers engaging in the production of disinformation. Our findings emphasize the importance of addressing the supply side of disinformation in online labor markets to mitigate its harmful societal effects.
DOI: 10.15468/dl.zvnps9
title: Occurrence Download

description: A dataset containing 6682 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Reynoutria japonica",
 "HasCoordinate is true",
 "Country is Ireland"
 ]
}

 The dataset includes 6682 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054925-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30437771
title: Replication materials (private): Understanding Energy-Saving Behaviors via a MIMIC Model

description: Private replication bundle for peer review (individual-level, de-identified).Includes <code>data_analysis_ready.csv</code>, codebook templates, SPSS/R/Python starter scripts, README, license.For confidential use by editors and peer reviewers only.<br>
DOI: 10.6084/m9.figshare.30437771.v1
title: Replication materials (private): Understanding Energy-Saving Behaviors via a MIMIC Model

description: Private replication bundle for peer review (individual-level, de-identified).Includes <code>data_analysis_ready.csv</code>, codebook templates, SPSS/R/Python starter scripts, README, license.For confidential use by editors and peer reviewers only.<br>
DOI: 10.48322/8t0t-we11
title: SOHO LASCO C2 Level 0.5 FITS Data

description: The LASCO C2 Raw FITS Data. 

LASCO (Large Angle Spectroscopic Coronagraph) is comprised of three nested coronagraphs (C1, C2, and C3) that image the solar corona from 1.1--30 solar radii (C1: 1.1--3; C2: 1.5--6; and C3: 3--30). The inner coronagraph (C1) is a newly developed mirror version of the classic Lyot coronagraph without an external occulter, while the middle coronagraph (C2) and the outer one (C3) are externally occulted. C1 is equipped with a Fabry-Perot interferometer that can take monochromatic images over the whole field of view with a spectral resolution of 0.07 nm. By stepping the bandpass across a spectral line, line profiles and Doppler shifts can be derived from all 1024 x 1024 pixels simultaneously. With an equivalent pixel size of 5.6 arcsec, the spatial resolution of C1 is ~11 arcsec. The resolution is ~23 arcsec for C2, and 112 arcsec for C3. The calculated expected precision of the emission line measurements, for example, of the 637.4 nm Fe X line is 0.05 Km/s at 1.1 solar radii, 1.0 km/s at 2 solar radii, and 8.0 km/s at 3 solar radii, all for a 60 s exposure. All three devices use a 1024 x 1024 pixel CCD. The CCDs are passively cooled to 193 K, but can be heated above ambient temperatures to drive off condensed material. The readout rate is ~21 s for a full image, but any line on the CCD can be accessed within about 60 ms, the unwanted lines being dumped. The electronics box provides overall control, including observation programs and scheduling. An observation program can be scheduled to be run at an absolute time or just next in sequence. It can run for a certain number of iterations, an interval of time, or stop at an absolute time. The observation program tasks included, e.g., taking single images from one of the telescopes, a series of images through the three linear polarizers, and a sequence of images at a series of Fabry-Perot wavelength steps. The image processing tasks are primarily image compression, with seven different techniques being available. On average, the readout time should be ~6 minutes or about 200 images/day. This information is from the paper ``The Large Angle Spectroscopic Coronagraph (LASCO): Visible Light Coronal Imaging and Spectroscopy,'' by G. E. Brueckner et al. (Proceedings of the First SOHO Workshop, ESA SP-348, pp. 27-34, November 1992).
DOI: 10.15468/dl.sffhrq
title: Occurrence Download

description: A dataset containing 2412 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Suivi mégafaune en mer en période internuptiale-Suivi terrestre des oiseaux marins hivernants à la côte sur la façade Manche – Mer du Nord"
 ]
}

 The dataset includes 2412 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054927-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17398343
title: EPO matrix for moisture correction of field soil spectra

description: A dataset of 81 soil samples was collected from French, Italian, Polish, Swedish, and Swiss soil archives, where SOC measurements were available. The soil samples were air dried and sieved at 2 mm and soil spectra were collected in the country of origin using different spectroradiometers. Before collecting the soil spectra, each lab scanned the ISS LB sands. Each soil sample was split into five subsamples and placed in five petri dishes: one was left dry, while a quantity of water was added to the other four subsamples to reach 5, 10, 20 and 30 % of gravimetric soil moisture. Spectral measurements were performed in laboratory dark rooms using a contact probe and at constant temperature and humidity for all 5 subsamples and 3 replicate soil spectra were collected for each subsample and the average spectrum was computed.

Two files are availble:



remoistened_probefield_SSL.csv which includes the soil spectra of 81 soil samples collected at different soil moisture levels;

EPO_correction_matrix.csv which includes the EPO correction matrix obtained from the re-moistened dataset.


 More details can be found here: https://doi.org/10.1016/j.atech.2025.101353
DOI: 10.5281/zenodo.17398344
title: EPO matrix for moisture correction of field soil spectra

description: A dataset of 81 soil samples was collected from French, Italian, Polish, Swedish, and Swiss soil archives, where SOC measurements were available. The soil samples were air dried and sieved at 2 mm and soil spectra were collected in the country of origin using different spectroradiometers. Before collecting the soil spectra, each lab scanned the ISS LB sands. Each soil sample was split into five subsamples and placed in five petri dishes: one was left dry, while a quantity of water was added to the other four subsamples to reach 5, 10, 20 and 30 % of gravimetric soil moisture. Spectral measurements were performed in laboratory dark rooms using a contact probe and at constant temperature and humidity for all 5 subsamples and 3 replicate soil spectra were collected for each subsample and the average spectrum was computed.

Two files are availble:



remoistened_probefield_SSL.csv which includes the soil spectra of 81 soil samples collected at different soil moisture levels;

EPO_correction_matrix.csv which includes the EPO correction matrix obtained from the re-moistened dataset.


 More details can be found here: https://doi.org/10.1016/j.atech.2025.101353
DOI: 10.15468/dl.w8ehfu
title: Occurrence Download

description: A dataset containing 132 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Saimiri collinsi"
 ]
}

 The dataset includes 132 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054928-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57967/hf/6798
title: harm-conversations
DOI: 10.25397/eur.28010144
title: Finding Love in the Black Box: Algorithm Awareness on Dating Apps - Data Files

description: The dataset included here belongs to the project ‘Finding Love in the Black Box: Algorithm Awareness on Dating Apps’ by Bukman, Sharabi and Timmermans (2024). The aim was to assess the level of algorithm awareness among Dutch and Belgian users of the dating apps Breeze (Dutch) and Tinder, and test whether it is associated with high algorithm trust and satisfaction with matches.<b>BACKGROUND </b>Most dating apps incorporate artificial intelligence (AI) to match users to one another but are not fully transparent about how their recommender systems work. In turn, users have varying degrees of awareness of matching algorithms. Unfortunately, little is known about how insight into algorithmic functioning affects the online dating experience. This research examined the relationship between algorithm awareness, trust in the recommender system, and satisfaction with the quality of matches.<b>METHOD </b>A survey was conducted among 216 users of U.S.-based Tinder and 500 users of the Dutch dating app Breeze. Relationships were estimated with structural equation modeling, and effect sizes were compared between these two groups of users to assess variability based on dating app affordances. Higher algorithm awareness was associated with more trust in the recommender system, which in turn positively related to the ease by which users could find matches. The dataset is a composite of two separate collections. The data among Breeze users was collected in collaboration with Breeze itself. Users received a popup in the app with the link to the survey. 688 people participated, which resulted in 500 complete responses in two days. The data for Tinder users was collected in a period of several weeks on various unrelated social media pages. After 500 respondents started the survey, 216 complete responses remained. All participants had to be over 18 years old, and had to have used the app in the previous 30 days.<b>RESULT </b>The presence of this fully mediated effect between awareness and satisfaction with matches suggested that knowledge of matching algorithms can affect both the perception of the recommender system as well as the results of using dating apps. In other words, users with higher algorithm awareness are more content with the quality of the profiles they match with, mediated by a higher trust in the abilities of matching algorithms. No significant differences were found between groups.<b>CONCLUSION </b>Overall, the results indicate that increasing the awareness that users have of algorithm functioning could improve the online dating experience. This research contributes to the limited literature on human-AI interaction in the context of dating apps.
DOI: 10.25397/eur.28010144.v1
title: Finding Love in the Black Box: Algorithm Awareness on Dating Apps - Data Files

description: The dataset included here belongs to the project ‘Finding Love in the Black Box: Algorithm Awareness on Dating Apps’ by Bukman, Sharabi and Timmermans (2024). The aim was to assess the level of algorithm awareness among Dutch and Belgian users of the dating apps Breeze (Dutch) and Tinder, and test whether it is associated with high algorithm trust and satisfaction with matches.<b>BACKGROUND </b>Most dating apps incorporate artificial intelligence (AI) to match users to one another but are not fully transparent about how their recommender systems work. In turn, users have varying degrees of awareness of matching algorithms. Unfortunately, little is known about how insight into algorithmic functioning affects the online dating experience. This research examined the relationship between algorithm awareness, trust in the recommender system, and satisfaction with the quality of matches.<b>METHOD </b>A survey was conducted among 216 users of U.S.-based Tinder and 500 users of the Dutch dating app Breeze. Relationships were estimated with structural equation modeling, and effect sizes were compared between these two groups of users to assess variability based on dating app affordances. Higher algorithm awareness was associated with more trust in the recommender system, which in turn positively related to the ease by which users could find matches. The dataset is a composite of two separate collections. The data among Breeze users was collected in collaboration with Breeze itself. Users received a popup in the app with the link to the survey. 688 people participated, which resulted in 500 complete responses in two days. The data for Tinder users was collected in a period of several weeks on various unrelated social media pages. After 500 respondents started the survey, 216 complete responses remained. All participants had to be over 18 years old, and had to have used the app in the previous 30 days.<b>RESULT </b>The presence of this fully mediated effect between awareness and satisfaction with matches suggested that knowledge of matching algorithms can affect both the perception of the recommender system as well as the results of using dating apps. In other words, users with higher algorithm awareness are more content with the quality of the profiles they match with, mediated by a higher trust in the abilities of matching algorithms. No significant differences were found between groups.<b>CONCLUSION </b>Overall, the results indicate that increasing the awareness that users have of algorithm functioning could improve the online dating experience. This research contributes to the limited literature on human-AI interaction in the context of dating apps.