Science and Africa - what colloboration exists and how do we know?

As I am adding large amount of African scientists to Wikidata, I find that I have moved into a green field. A green field as far as Wikipedia and Wikidata are concerned.

To learn about how the information about African science evolves in Wikidata, I created Listeria lists that inform about universities by country, fellows/member of academies of science and members of African young science organisations.

What I produce is a scaffolding; basic information that enables. The information that I use from the Royal Society of South Africa for its fellows includes dates, other awards, employers and even dates of death. Slowly but surely more information is being added for these people and consequently you will also find for, for instance Rhodes University, more employees and additional papers (currently only 1385 papers for its 84 scholars are known).

A scholar like Tebello Nyokong, a Rhodes scholar, has 637 papers to her name. She is a world class scientist and has four Wikipedia articles to her name. All kinds of questions may be queried for her co-authors; the gender distribution, the organisations they represent, the nationality of the co-authors.

Obviously, African science is not well represented at this time. This is a reflection of how people perceive and value African science... In essence it reflects a bias of regular Wikimedia editors. The regular Wikimedia editors are in the west, they have no reason to consider African science but this is a bias. It is highly likely that it will be hard to get Wikipedia articles accepted for African scientists because of a lack of sources and probably a lack of this perceived Western relevance.

Adding one scientist at a time does not make much of a difference. When scientists are added as part of a SourceMD process, any and all scientists who have a public ORCiD profile are likely to get included in Wikidata. This is why so many African scientist are already known. When a notable scientist is then recognised as a recipient of an award, we may already know about the papers they authored.

The SourceMD process is no longer available. It coincides with a lack of resources at Wikidata so any and all resources used for science papers are now available to something else. Understandable, but the result is that I am no longer motivated to seek ORCiD identifiers and consequently, the process is increasingly broken.
Thanks,
      GerardM

Scaffolding in Wikidata - the Christiaan Hendrik Persoon Medal

Professor Brenda D. Wingfield is another scientist who won an award Wikidata knew nothing about. This time the Christiaan Hendrik Persoon Medal. The Wikidata basics for an award are its name, the conferring organisation and a website with details. A bonus is when there is a link to a person, an organisation the award is named after..

The Persoon Medal is a South African award, its conferring org is new to Wikidata as well. In an article about Prof B.D. Wingfield, all the other recipients are named as well; there are only six in a time span of 53 years so it was not hard to add them all.

The objective is to make connections. For both the award and Prof Wingfield, connections shows best in a Scholia. One of the more frequent co-authors is a Michael J. Wingfield.. He is a co-recipient of the Persoon Medal, a co-member of the Member of the Academy of Science of South Africa and, a duplicate of Mike Wingfield and yes, he is the spouse of Prof B.D. Wingfield as well. He is at this time Q73879566 in the Scholia waiting for papers to be attributed to the earlier item.

Another frequent co-author, Bernard Slippers, is known from a different context. He is both a member of the South African Young Academy of Science and the Global Young Academy. Given some personal connections it was easy to ask if by chance Prof Wingfield is his doctoral advisor (he is the primary, they both are).

The point of scaffolding is that it provides the structures that enable finding the data, preferably in a context. Given that most data is static, the static representation that is Listeria is really powerful. When you group them like I did for African science or Young Academies, you get the satisfaction of understanding what work is done/has been done on a subject. The icing on the cake is when you enable collaboration. I am grateful for Robert Lepenies to pick up the lead and inform other young academies for what Wikidata may mean for them. I am grateful for Daniel Mietchen for improving on the queries I use; they now show the number of publications known for each scientists and a link to the tool that enables attribution to that scientists.

My role is a simple one. I add data. Data that connects, gives relevance but most importantly data that may be picked up in queries, lists by others. The scaffolds are made by others, relevance only happens when others pick it up. My point: there has to be something to pick up.
Thanks and happy new year,
          GerardM

The value of incomplete data - Fellows of the Ecological Society of America

This is about understanding data in Wikidata. The article is about understanding what you can and cannot do with incomplete data, it is not so much about the Ecological Society of America.

The most recent work started with the news of a new Wikipedia article. Prof Cottingham is a 2015 fellow of the esa, there is a category for fellows, adding her and other missing fellows to Wikidata showed that for one fellow there was no Wikipedia article. At the time there were 90 known fellows and for only two it was known when they became a member.

I expected that new fellows would be known to Wikidata not just as an "author string" but that they would be an "item". So I added 14 of the 2019 cohort and found this not to be the case. I then looked up the known fellows from the esa webpage, added their date to Wikidata because I wondered if it were particularly the older fellows that are represented in Wikipedia.

While adding the dates, I added many alternate names to aid disambiguation, I removed one item and found two false friends; fathers mistaken for their son. When I was done, I had a good impression of the data on the website and even though I do not have the full numbers, I feel to be correct in my belief that it is the old ecology/ecologists that are represented in Wikipedia.

When you scrutinize the list of fellows, you will find included "Early Career Fellows", they are "elected for advancing the science of ecology and showing promise for continuing contributions" and they take part for a limited amount of time. Programs like these are known from all over the world and from many science orgs. This time I did not spend time on them but from previous experience I can safely say that promising is putting it mildly.

Wikidata is a wiki and as such, the work that I did is of value even though it is incomplete. I did not add all the missing fellows for instance. The esa is very much an organisation for America (check the employment of its fellows) and it takes pride in global attention and solicits membership fees from all over the world. It takes a lot of additional data when you want to compare if its subject matter is biased towards America and in what way.

For many of the fellows I added, there are papers with "author strings" waiting to be linked to an author. The same can be said for the fellows that are still missing. It can be compared to other ecological organisations but how to deal with the differences takes a completely different understanding. It takes more data to make this possible but the data does not need to be complete, that is the beauty of averages.
Thanks,
       GerardM

Why didn’t @Wikidata have an item on Margaret Nakakeeto, a champion for living babies?

Ed Erhard wrote famously in 2018 "Why didn’t Wikipedia have an article on Donna Strickland, winner of a Nobel Prize?" A year later we can say that it is extremely likely that a Donna Strickland, a Margaret Nakakeeto are known in Wikidata if only because they are a co-author of a paper (technically: an "author string").

When Ed wrote his article, it was to highlight the gender gap we have in Wikipedia. Arguably relevant and important and it needs the attention it gets. However, it does not follow that it is the only "gap" that needs addressing, it even does not follow that the gender gap is the gap with the biggest impact.

When you consider Africa and particularly science in Africa, the subjects that matter in Africa most are reflected in for instance the Scholia for the members of the South African Academy of Science. As far as I now know, its gender ratio is 27% and this is a list with a mix of Wikipedia articles and Wikidata items. It shows the attention African science gets in Wikipedia nicely.

In Africa there is a huge amount of attention for maternal and neonatal care (eg Uganda) and as programs impact the health and survival of women, it follows that more women will become notable, notable for Wikipedia.

By giving attention to female African scientists, the subjects they are known for gain relevance. Their Scholias are developed, including links to co-authors and papers. It will improve the likelihood that when African science awards are announced, we will at least know the recipients in Wikidata.
Thanks,
       GerardM

#Science and America first

Several US American science organisations are quite adamant that for them, it is America first. Stupidity has its place and these days the United States has a lot of it particularly as those same science organisations expect people from the rest of the world to accept "pre-eminence" of the USA.

There may be good reasons to be a member of these organisations but from my perspective, it is one thing to be with stupid, it is another to have these organisations argue their case on "your" behalf. So when you are a scientist, chances are that we already know you at Wikidata. We may even know about your science, your co-authors, your memberships.

Take for instance Prof Lise Korsten, she is probably South African, this is her Scholia. She has many co-authors and for some we do not know their gender and for most we do not know their nationality. We do not know if she is a member of any science organisation and we do not know that for her co-authors either. So you may add your professional memberships at Wikidata, your nationality and when you do know the nationality of your co-authors, you may add that as well.

In this way we make obvious to US American stupid that science is global.
Thanks,
       GerardM

Disseminate science says @EstherNgumbi, @Wikimedia projects have the power to do just that

In this day and age science is of the utmost importance. When I am pointed to a conference where an African scientist gives the plenary lecture; the message is on display in the picture. I take an interest.

When you want to disseminate research, when you want the science to be known by society, you have to pick your platform. You can do worse than choosing for the Wikimedia projects.

Professor Esther Ngumbi is employed at the University of Illinois at Urbana–Champaign. Her ORCiD profile has only one paper but at Wikidata we knew of others. As she is now known at Wikidata with her papers, she has a Scholia. At first there was only one co-author, a bit sparse, so others were added. They were linked to the papers they have on Wikidata. The same was done for some authors who cited professor Ngumbi..

When you, your science is known in Wikidata, you are more likely to get a Wikipedia article and yes, working for an American university helps. An ORCiD profile that is open will be even more potent when you trust organisations like your university, CrossRef to update your ORCiD when it knows about your papers, your new papers.

In this day and age where our ecology is no longer stable, it is vital to know and respect the science. While we aim for the best we have to be prepared for the worst; we have to see it coming. It is why our Wikimedia projects should inform about all the science and not just what a Wikipedia article has as a reference.
Thanks,
       GerardM

Jack needs help, so do we and, so do our audiences

Jack penciled his aspirations for Twitter in a tweet. In it he states: "... Second, the value of social media is shifting away from content hosting and removal, and towards recommendation algorithms directing one’s attention. Unfortunately, these algorithms are typically proprietary, and one can’t choose or build alternatives. Yet."

It is good news that Jack seeks a way out, he intends to hire a "small independent team of up to five open source architects, engineers, and designers" and "Twitter is to become a client of this standard"..

In the Wikimedia projects we have similar challenges and opportunities. We cannot expect for all kinds of reasons that scientists who are very much in the news (aka relevant) there to be a Wikipedia article Dr Tewoldeberhan is a recent example but there is no reason why we cannot have her, her work and the work of any other scientist in Wikidata. With tools like Scholia we already have a significant impact by making more known that just what may be found in a Wikipedia. Jack, we do know many scientists by their Twitter handle, they already make the case for their science on Twitter. This makes it easy for you to link to and expand on Scholia. What we give our readers is more to read so that they can find conformation for what they read.

Jack, Wikidata is not proprietary, Scholia is not proprietary and the Wikimedia motto is "to share in the sum of all knowledge". Together we can shift focus from what we have read before in the Wikipedias to what there is to read on the Internet. Put stuff in context and bring the scientists who care to inform about their science in the limelight.

What we do not have is the pretense that we cover everything well. we do aim to cover everything notable well. What we provide is static, Twitter is much more dynamic and together we will change the landscape. Great technology combined with both the Twitter and Wikimedia communities has the potential of being awesome.
Thanks,
      GerardM

What is it about Jess Wade?

It is not only that Jess writes Wikipedia articles. Others do as well. It is not only that she engages girls with science; it is why she enthuses about female (STEM) scientists. Others do as well. It is not that only that her tweets engage us with for instance the #PhotoHour, that she wants us to read the (fabulous) books by Angela Saini, she also organises for schools to have Inferior in their library for girls to read and become a scientist as well.. What makes her special is that she engages people to be part of what she communicates so well.

Take me for instance, Jess is on Twitter and I read her daily new article. For the person she writes about I enrich the information on Wikidata and ensure that the "authority control" is set in the Wikipedia article. What I add is award information, authorities, employment and education info. I often add awards and depending on how interesting an award is to me I add other recipients as well.

It is not only me, there are many more people inspired by Jess who get involved, they read the books she champions, donate so that more girls read Inferior, follow her on Twitter, write articles and also get involved, are involved. It all happens because of the enthusiasm that Jess brings to us all. This enthusiasm, the involvement is what I so cherish. When the inevitable naysayers come along it dampens the positivity, the sense that we are making a difference.

When you want to know how important the women she writes about can be, consider Joy Lawn she tweets really effectively as well... It shows how women scientists really effectively communicate the relevance of science. It is vitally important for us to know about the science, the subjects they champion. At that it may be our Jess but actually, it is Dr Jess Wade, she is a scientists first, she promotes science and Wikipedia is a vehicle to get the message out.
Thanks,
        GerardM

It is not a list when it is the result of a query

A list is a presentation of data. When a list is maintained manually, the list IS the data, when the data is the result of a query, it REPRESENTS the data.

The difference is quite important. Changing the information in a query is in the definition of the query, changing the data is a matter of re-running the query. Changing the information in a list is a lot of work and therefore there is no integrity in the data itself, it is always potluck what quality the data is.

In the Wikipedia world, Listeria is king of the queried lists. For some its use is controversial but things are changing for the better. Projects like Women in Red use Listeria a lot, their work is possible because people add notable women in Wikidata. The queries work on the basis of awards, professions, nationality enabling volunteers to write the articles they care to write. This works because once an article is written they are automagically removed from the lists.

On the English Wikipedia consensus has it that manual lists are to be preferred. However, emperically the quality of automated lists perform better {{REF}} and as data in Wikidata does not suffer from "false friends" even the support for "red links" is vastly superior.

There is no point in anecdotal evidence who is best. When the English Wikipedia has a black link for Stephen Fleming on its page for the Spearman medal first, it is an obvious start for a new item on Wikidata that is more than just a person who won the Spearman medal. It then becomes a target for lists of the special interest groups who aim to cover "their" subject matter well.

The next stage of the acceptance of lists relies on the realisation that "consensus" does not serve us well particularly when it trumps established facts. It will serve us well in politics and, in what Wikimedia projects could be.
Thanks,
      GerardM

Please let us support #Science at @Wikidata

When the BBC informs us about reforestation in Ethiopia.. It is Dr Tewolde-Berahan who informs BBC's Justin Rowlatt about the work that is done in preparation of planting trees.

It is a humorous piece of information that gets the message across; you can plant where trees were absent for generations and make the (local) climate change.

Consider; you now want to seriously know more about reforestation in Ethiopia. Where do you go to? Wikipedia, in all its magnificence, is rooted in its articles and thereby dated. Through its references however, there are links to its authors, to many more authors and their publications. Every article has in this way its concept cloud and it could be translated in a Scholia for an article.

The current Scholias are itself already a rabbit hole that leads in many directions and a Scholia for an article would be something different again. The article links to subjects, has its papers and by inference authors, they may link to newer papers, more papers, contradicting papers. They may lead to scientists who research similar notions for another locality.. Why not reforest Spain in France? When reforestation is possible in Ethiopia, what would be different to make this unfeasible in Europe?

And all this becomes possible when you consider Wikipedia as the jumping off point in any and all directions, not just within Wikipedia..
Thanks,
     GerardM

NB I know there are two fellows of the Ethiopian Academy of Science related to this subject. Who are they and how are they connected to Dr Tewolde-Berahan?

@wikidata - I don't scale, help me scale

At Wikidata there is always more to do and as a volunteer you make the biggest impact when you concentrate on specific subjects. I do not scale enough to do everything I would like to do.

There are a few area's where I aim to make a difference; of particular concern is where we do not represent a body of knowledge/information in Wikidata. At this time the favour scientists particularly women, young scientists and scientists from Africa.

To make my work scale, I twitter and blog. I latch on to the great work done by Dr Jess Wade. She writes articles on well deserving scientists and I aim to add value for those scientists on Wikidata. Typically I add professions, alma maters, employers and awards. In addition I add "authorities" like ORCiD, Google Scholar and VIAF. This is important because it enables the linking of scholarly papers already in Wikidata or known at ORCiD. I can more or less keep up with Jess and, I happily add information for any and all scientists I come across on Twitter.

While doing this I learned of the Global Young Academy and as a side project started adding scientists who are member of the GYA or one of affiliated organisations to Wikidata. I am so pleased  I got into contact with Robert Lepenies. Robert is happy with the opportunity that a Scholia provides for an organisation like the GYA, for him and for all the young scientists involved. We collaborated on completing the lists on many wikipedias, Robert added many scientists to Wikidata and is now battling to keep the pictures of these young scientists on Commons...

What is crucially important for me is that Robert advocates an open ORCiD profile to scientists worldwide so that they may have their Scholia. Both Robert and I do not scale and what would help us most is an easy and obvious way that enables any scientists to start a process that will include all his papers from ORCiD, will update the known co-authors and instruct in what they can do to enrich their Wikidata / ORCiD / Scholia profile even more.

I am now working on African scientists and yes, I would appreciate some help.
Thanks,
     GerardM

PS my wife would like this scale to be enough for me

Instant gratification at @Wikidata

As I write this, it is 11:46am at 09:26am I added papers to prof Hafida Merzouk. The edits are picked up by Reasonator but not by Scholia. In a similar way, edits done are not picked up by Listeria.

Instant gratification is now a thing of the past, the work done at Wikidata may eventually be picked up in a Scholia or Listeria but it is not funny. Can I tweet about the things I find or have done when Wikidata no longer reflects the relevant changes?

This may sound like trivial but it does mean that when I look back at my work that  there is no longer a timely way to do so.

Instant gratification motivates and it is a factor in maintaining quality. We are losing it.
Thanks,
      GerardM

Put (modern) #science of #Africa on the map

A young African scholar commented that the info on websites of African scholarly organisations was all about its past. There is a point to recognizing those who did good and consequently making obvious that the science of today is rooted in the past.

African scientists as well as any other scientist have a place in Wikidata with their affiliations, papers, co-authors and also with their scholarly advisors. My proposal is for all scholars to check if they are on Wikidata, check if their doctoral thesis is on Wikidata. Then add their doctoral advisor to their item and reciprocate themselves as a doctoral student.

Do not forget to include where you studied and for what university you work(ed). Check if your ORCiD profile includes trusted organisations like CrossRef that will update your profile when appropriate. When many of you do this at Wikidata you will be surprised what the impact will be.
Thanks,
      GerardM

Bias in @Wikidata and a SMART approach

When at the WikidataCon quality was presented, it was rated from 1 to 5. This approach has its own bias because it does not consider what may not be there. What is not there can be made visible using assumptions like: "a university has more than one employee" (employee includes professors) and, every country has at least one university..

The bias in Wikidata starts with the way it is mostly used and consequently how it is taught. People are shown what Wikidata looks like, immediately followed up with training in the use of query and the use of tools. At every level it takes considerable skills to make a use of Wikidata. The first hurdle to overcome is to understand the data in a single item. When your language is not English you are toast. This is Cape Town in Newari and this is a useful presentation using Reasonator. With Reasonator the information is easy to digest and adding missing labels is just one click away.

The second hurdle is knowing what bias it is you want to remedy. For a known bias like the gender gap, the Women in Red have lists of missing Wikipedia articles. A Wikidata gap is expressed by the absense of data. Listeria lists are great at that.. These are all the universities of Africa.. If you do not get the extend of what we miss, you have some thinking to do. When you apply this principle to the science of Africa, you find a lot of lists and the biggest issue remains; missing lists.

When you tackle a missing subject like I did for the "Affiliates of the African Academy of Sciences", you will find a source as a reference for the group and a reference on every affiliate. To ensure that the data is relevant and actionable, I added all of them, linked them to ORCiD and/or Google Scholar enabling SourceMD to link them to their papers. I added nationality because this may trigger inclusion on the Women in Red lists and when it was obvious, I added employers so that they may be included as a scholar on African University lists..

When we as a movement want to fight bias, we have to consider the use of lists and particularly Listeria list to show the developments of a subject. With lists available on many Wikipedias, it becomes possible to gain traction on what we miss. This approach is distinctly different as it acknowledges the need for more support for item based editing and it makes the point that missing data is a quality issue that needs to be addressed as a fundamental issue.
Thanks,
      GerardM

@Wikipedia talks about @Wikidata

"WD is unreliable. WP:V and WP:RS are completely ignored (from any editors). International NPOV is a problem too." It is so SMART, that the best I can do is ignore it. Then again it is an open invitation to talk about Wikipedia..  There is no Wikipedia there are over 300 Wikipedia language editions.. so even the acronyms are lost on me as there is no one Wikipedia to rule them all.. 

So forget about acronyms and lets talk Wikidata and by inference raise issues particularly for the English Wikipedia where appropriate. First, Wikidata includes more items than there are subjects raised in any and all Wikipedias. Its quality can be considered in many ways and verifiability is largely ensured because of the association with other "authorities" about a subject. Thanks to the increased use of open data, it is possible to verify that specific statements are shared, increasing the likelihood that they are correct. For some information like for scientists who are a member of the AAS Affiliates Programme, we have/may have references to the authoritative source. Such references may be on a project or on an item level, it makes verifiability easy and obvious. 

Wikidata has an issue with all kinds of gaps in its coverage. For many African countries no universities are known, there are hardly any scholars associated with them. Thanks to Listeria functionality we can monitor if and when data is added. Many a Wikipedia do not have such tools because of the aversion of Wikidata by some. At the same time projects like Women in Red rely on Listeria lists and by inference Wikidata to know what to work on.

In tools like Reasonator and Listeria lists are generated and, when you compare them with Wikipedia lists, the quality is measurably better. I published frequently in the past about the Polk award.. In its lists Wikipedia has a likely error rate of six percent. When they fudge the record by not linking at all, the quality of a Wikidata lists is even better because it is much better at linking items than Wikipedia is at linking red links.  There is a solution, it just requires a willingness by Wikipedians to cooperate. 

I understand what is meant by "international NPOV" and it is where Wikidata is by definition better than an individual Wikipedia. By definition because Wikidata represents data from ALL Wikipedias. Thanks to the people of DBpedia, there is a potential to highlight where Wikipedias differ and it is more likely that the fruit of their labour will enrich Wikidata than Wikipedias.

So a Wikidatan walks into a bar..
Thanks,
       GerardM

Adegoke O. S. - Fellow of the African Academy of Sciences

It is easy enough to add "O.S. Adegoke" to Wikidata and mark him as a fellow of the AAS.  With only initials there is no way to know the gender and to me that is quite unsatisfactory. This is when Google becomes your friend when you find Mr Adegoke is addressed as "Silvester".

There are some 384 fellows and slowly but surely they find their way into Wikidata. If there is a point to it, it is the same point why there are fellows of the African Academy of Sciences; "they provide Advisory and Think Tank functions and help to develop strategies that promote science in Africa and that are relevant to the continent".

The objective of Wikipedia and, by inference Wikidata, is to share in the sum of all knowledge. As we do not really consider what is relevant for our public in Africa and for those interested in Africa the AAS in its choices of its fellows at least points in the right direction. Adding the AAS fellows to Wikidata is a puzzle because the format of names differ. Some 240+ fellows are known at Wikidata as data but for it to become informative there is a need for suplemental data and even better Wikipedia articles.
Thanks,
     GerardM

Rebecca R. Richards-Kortum

A text on the Internet read: "She’s Rice’s first-ever MacArthur grant winner. But her real claim to fame? Her clever medical inventions might just save your life." It is not as if I know her even though I added to her Wikidata item in the past .

I looked her up because she approves of the NEST360° organisation on Twitter. It is an organisation committed to reducing neonatal mortality in sub-Saharan hospitals by 50 percent.

Such organisations deserve a place in Wikidata, it has members I am adding. I consider it part of my "Africa project" even though it does not have a place there yet.

Yesterday I added an item for "neonatal care" and all the papers that are already included in Wikidata  about neonatal care need to be associated with the subject. Scientists like Prof Joy Lawn are to be marked for their specialty.

How is it possible that it takes a 60 year old white male from the Netherlands to add something this basic to Wikidata. We are talking about more yearly deaths than Ebola..
Thanks,
       GerardM

What data is wrangled is obvious when its presentation is considered

When you watch a game, you want to know the score. When you have a favourite author, you want to know all his/her publications and when you hear about a place you want to know where it is. Easy.

Such data may be included in a repository like Wikidata and, in essence the data is still simple. You still want to know the score, the publications or the location, the question is how do you get the data in a format that makes sense.

People are really good at understanding data when it is in an agreeable format.. These are three format for the same data; a scientist in Wikidata. This is how Wikidata presents its data and imho the data is really hard to understand. This is the same data in Reasonator, it is a general purpose tool that shows data and its relations. It can be used for all kinds of data, it is my goto tool to get to grips with data related to one item. Finally Scholia presents data formatted in a way that makes sense for this scientist.

Given how awful the default presentation of Wikidata is, it is obvious why everyone teaching the use of Wikidata focuses on querying the data and therefore people seek/work on the results provided in what is their default tool. I typically focus on particular subjects, today it was Dr Shima Taheri, I added a reference, some publications and genders for her co-authors. To do this I am triggered by the presentation of the data in the tools I use.

The holy grail for Wikidata is the use of its data in Wikipedia info boxes. However, people are taught to query data and that approach does not align well with the data items you find in info boxes. So when the purpose of Wikidata is in Wikipedia info boxes, presentation needs to become a priority.
Thanks,
      GerardM

The lowest hanging fruit in #DBpedia

What I hate with a vengeange is make work. DBpedia as a project retrieves information from all the Wikipedias, wrangles it into shape and publishes it. In one scenario they have unanimous support from one or more Wikipedias agreeing on the same fact and, they all may have their own references.

We should import such agreeable data without further ado. An additional manual step to import to Wikidata is not smart because manual operations introduce new errors. Arguably when there is no unanimous support manual intervention may improve the quality but given the quantity of the data involved, it means that a lot of data will not become available. THAT in and of itself has a negative impact on the quality of available data as well.

So what to do.. Harvest all the data that is of an acceptable quality, that is the data DBpedia accepts for its own purposes. Enable an interface where people verify the data where their project is challenged.

When we truly aim to engage people, we enable them to target the data they want to work on. I will happily work on scientists but do not expect me to work on "sucker stars". More than likely there will be people who care about soccer stars but not about "crazy professors".
Thanks,
      GerardM

With #DBpedia to the (data) cleaners

The people at DBpedia are data wranglers. What they do is make the most of the data provided to them by the Wikipedias, Wikidata and a generous sprinkling of other sources. They are data wranglers because they take what is given to them and make the data shine.

Obviously, it takes skill and resources to get the best result and obviously, some of the data gathered does not pass the smell test. The process the data wranglers use includes a verification stage as described in this paper. They have two choices for when data that should be the same is not; they either have a preference or they go with the consensus ie the result that shows most often.

For data wranglers this is a proper choice.. There is an other option for another day, these discrepancies are left for the cleaners.

With the process well described, the data openly advertised as available, the cleaners will come. First people akin to the wranglers, they have the skills to build the queries, the tools to slice and dice the data. When these tools are discovered, particularly by those who care about specific subsets, they will dive in and change things where applicable. They will seek the references, make the judgments necessary to improve what is there.

The DBpedia data wranglers are part of the Wikimedia movement and do more than build something on top of what the Wikis produced; DBpedia and the Wikimedia projects work together improving our movement's qualities. With the processing data generally available this will become even more effective.
Thanks,
        GerardM